dataesr/scientific-paragraphs-categorization huggingface dataset:
For solve the phase problem, five of the 6 keV datasets were clustered and merged using blend  61  and fed into the CRANK2 pipeline for SAD phasing  61  which resulted in a low resolution (4. 42 Å) structure of METTL6. This lowresolution structure was used to obtain phases for the other data sets by molecular replacement using Phenix-phaser  62. For all models, several rounds of model building using COOT  63  and data refinement using PHENIX-REFINE  64  were performed. Ramachandran statistics for the METTL6 FL are 97. 42% favoured, 2. 58% allowed and 0% disallowed and for the METTL6 40-269) are 95. 18% favoured, 4. 82% allowed and 0% disallowed. Data collection and refinement statistics are shown in Extended Table  1.
5 min. The total time is 11 min and the flow rate is 0. 35 mL/min at a column temperature of 35 °C. An ESI source was used for ionisation of the nucleosides (ESI-MS, Agilent Jetstream). The gas temperature (N2) was 230 °C with a flow rate of 6 L/min. Sheath gas temperature was 400 °C with a flow rate of 12 L/min. Capillary voltage was 2500 V, skimmer voltage was 15 V, nozzle voltage was 0 V, and nebuliser pressure was 40 Psi. The cell accelerator voltage was 5 V. For nucleoside modification screening, MS2Scan (m/z 250-500) was used. For quantification, a DMRM and positive ion mode was used (Extended data Table  4 ).
Acknowledgements We acknowledge  Martin Pelosse  for support in using the Eukaryotic Expression Facility at  EMBL Grenoble , and  Sarah Schneider  for support in using the  EM Facility at EMBL Grenoble. This work benefited from access to the cryo-EM platform of the  Structural and Computational Biology Unit at EMBL Heidelberg  and we thank  Félix Weis  for his assistance with the data collection. We acknowledge the  HTX Team  for crystallization setups, automated crystal harvesting and thermostability assays. This work used the platforms of the  Grenoble Instruct-ERIC Centre  (ISBG; UAR 3518  CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB ), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and  GRAL , financed within the  University Grenoble Alpes graduate school (Écoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Data availability The cryo-EM maps and their derived models for the deposited in the PDB: accession code 8P7B for the 1:2:2 METTL6:SerRS:tRNA stochiometry, 8P7C for the 2:2:2 complex and 8P7D for the 1:2:1 complex. EMDB entries in the same order are EMD-17528, EMD-17529 and EMD-17530. Maps of SerRS-tRNA and tRNA 3D-classes were side products of the data processing and have been deposited under EMD-17531 and EMD-17532. All micrographs of the dataset are deposited at EMPIAR-XXXXX. The crystal structures of METTL6 are deposited in the PDB (8OWX and 8OWY). Source data are provided in addition to this paper.
2/300). Fractions were analysed for protein and RNA contents using SDS-PAGE and urea PAGE. f Cartoon representation of crystal structures from this study: apo-METTL6 full-length (pdb:8OWX) and apo-METTL6 40-269 (pdb 8OWY) in complex with SAH. Extended Data Figure 2: Cryo-EM data processing scheme. a Cryo-EM sample preparation with a schematic of the SerRS-METTL6 fusion polypeptide. Size-exclusion chromatogram and SDS-PAGE and urea PAGEs of a typical purification are depicted. The fraction marked with a red arrow was subjected to cryo-EM grid vitrification. b Cryo-EM data processing scheme. The final resolution estimates were calculated using the gold standard FSC method implemented in CryoSPARC. Colouring of the structure by components as in main Figure 1. Extended Data Figure 3: Binding of SerRS to tRNA a Left panel: superposition of the structure of human SerRS binding to tRNA Ser (this study) with the structure of Thermus thermophilus SerRS bound to tRNA Ser (PDB-ID: 1SER, rmsd = 1.
White letters on red background signify 100% of conservation, blue boxes indicate >70% residue similarity with the conserved residues in red. A secondary structure representation of human METTL6 from our cryo-EM model is shown at the top. Residues involved in the coordination of C 32 are annotated with a green circle. Phenylalanine F32 in the METTL6-SerRS interface is annotated with a yellow circle. Residues involved in the coordination of i 6 A 37 are annotated with a red circle. Single-point mutants analysed in this study are additionally annotated with a "+" sign. Comparison with METTL11. a Secondary structure diagrams of METTL6 in comparison with METTL11A and METTL11B. Dotted lines represent unmodelled parts of the experimental structures. b Superposition of the model of tRNA bound METTL6 with METTL11A (PDB:5UBB, rmsd = 1.
137 Å) and METTL11B (PDB:6DUB) (rmsd = 1. 102 Å) c Detailed view of the -extension of METTL6 with the cryo-EM map represented as a transparent surface. Low resolution in this region indicates the flexibility of the hairpin. Extended Data Figure 6: Modifications in the co-purified tRNA Ser from T. ni insect cell expression a Sequence alignment of the ten tRNA Ser present in expression host T. ni, the three tRNA Ser from D. melanogaster with mapped modifications, H. sapiens tRNA Ser UGA and the consensus sequence we used for our model. The background colours represent the tRNA secondary structure features. b Relative abundance of modified nucleotides per tRNA molecule as determined by mass spectrometry. See also Extended Table 4. c Example of the cryo-EM map around modified nucleotides at positions specified in panel d.
Recruitment We conducted a randomised, controlled, open-label, two-arm, single-centre, prospective therapeutic trial with a medicoeconomic objective, comparing two referenced medical devices for surgical use. Inclusions were made prospectively from patients hospitalised and surgically managed in the orthopaedic department of Clermont-Ferrand University Hospital from 13 July 2021 to 27 July 2022 with a three-month follow-up for each patient. This was a Type 2 Human Subjects Research trial (minimal risks and constraints) approved by our Ethics Committee (CPP No. 21-CHCF-02). The project was submitted to clinical trials (NCT04903860). The inclusion criteria were as follows: over 18 years of age, eligible for a primary THR for isolated coxarthrosis, health insurance coverage, and informed consent after explanation of the protocol and perusal of a written summary of the surgical procedure and the successive stages of follow-up.
Course of the protocol After giving consent, each patient was included and randomised using RedCap TM (University Vanderbilt; Nashville, USA) software into either of the two trial arms. Stratification on BMI > or < 24 was   [17] , Harris score  [18] , Charnley  [19] , and Devane score  [20]  Western Ontario and McMaster University Osteoarthritis (WOMAC) index  [21]  and Hip Disability and Osteoarthritis Outcome Score (HOOS)  [22] ) (Table  1 ). Detailed information was collected during surgery: time spent setting up the operating table, duration of the procedure, total duration of room occupation, mass (by weighing bins) of infectious healthcare risk waste (HCRW) and non-HCRW, number of SU reamers used, operator satisfaction (scale 0-10), and the duration of all stages of the sterilisation process in our facility.
Description of surgical technique Preoperative planning with OrthoView TM (Materialise, Hampshire, United Kingdom) software was performed on frontal pelvic radiographs with a standardised scale (radio-opaque ball). This planning enabled us to evaluate the size of the acetabulum after determining the theoretical centre of rotation of the head and the rear of the acetabular cavity. This measurement was refined during the operation with a calliper to determine the size of the final implant to be chosen. When using the reusable ancillaries, the preparation of the acetabulum was started with a small-calibre reamer to hollow out the rear of the acetabulum and then with reamers of increasing diameter to enlarge the cavity. When using the SU ancillaries, the acetabulum was prepared with an SU reamer according to the value determined in the preoperative planning, combined with femoral head diameter data measured intra-operatively  [23].
Statistics The number of subjects needed was calculated based on the comparison of costs between the groups. Assuming a power of 90% and an alpha risk of 5%, and assuming a cost of 140 D for the SU group versus 220 D for the reusable group, and standard deviations of about 50 D for the SU group and 80 D for the reusable group, at least 20 subjects per group were needed. We thus planned to include a total of 40 patients in our study. Statistical analyses were performed with Stata v15 software (StataCorp, College Station, TX, USA). All tests were two-tailed, with a fixed type-I error of 5%. Continuous data are presented as mean ± standard deviation or median [interquartile range]. Categorical data are presented as frequency and percentage. Given the near-zero risk of attrition, the main analysis was conducted on an intention-to-treat (ITT) basis to compare the randomised groups with regard to the direct costs of care.
Conclusion Improving operational efficiency inside and outside the operating room and reducing cost dissipation are critical initiatives for many healthcare providers. SU ancillaries significantly reduce the table set up time and have the potential to facilitate time and cost savings but further research is needed in this direction. Our study shows that the daily workload, operating times, and the number of boxes of instruments to be sterilised are decreased. The associated environmental gain is significant. Nevertheless, the economic promise of these SU ancillaries is only partially supported in this trial owing to the small number of patients. Further work will be needed to obtain a more powerful medico-economic assessment of this promising ancillary product. Clin Trials: NCT04903860.
There are several challenges in adapting Convolutional Neural Networks (CNNs) to learn discriminative features for survival. Firstly, survival data often suffers from censorship related to patients presenting no event during the study, and which requires a careful formulation of the loss function used for training the network. Secondly, prospective datasets that control patient treatment, and follow-up allow to draw more meaningful medical conclusions but are often limited in the number of patients. Finally, PET images have low resolution relative to the heterogeneity of the MM tumour, while small lesions make transfer learning from existing CNNs pre-trained on natural images difficult. Both issues require specific architectural adaptations. In this paper, we propose a deep learning method for survival analysis, called M2P2 (Multiple Myeloma Prognosis from PET images), addressing the challenges above.
The proposed losses can be adapted to handle censorship through the choice of triplets. To deal with the next challenge, i. e. the relatively small dataset size of prospective studies, we propose to pretrain the network with pretext tasks as it has been shown in other medical applications that pre-training with auxiliary losses (instead of from scratch) improves the results in small dataset regimes  [13]. The two proposed pretext-tasks are: the binary (lesion / not lesion) classification of patches or the contrastive learning of an embedding space reflecting survival with a triplet loss  [11]. Finally, to handle the lesions' small and variable size, we designed a dedicated architecture considering spatial and channel attention  [14]. Indeed, attention has been successfully used in a large variety of medical applications including reconstruction, segmentation, detection or classification, but hardly ever with survival tasks  [15] ,  [16].
The survival problem has also been simplified to the classification of risk groups (e. g. , low, middle, high risk)  [24] , or to the regression of the time-to-event  [23]. However, such formulations do not natively handle censored data and therefore require removing censored patients from the dataset. Both RSFs and deep learning are machine learning methods allowing for personalised treatment recommendations. Survival losses. The design of the deep learning for survival analysis involves adapting the loss and the metric to censorship. The reference risk-predicting method by Faraggi and Simon  [25]  replaced Cox's linear β ⊤ x i term with a more flexible feedforward network. The loss used for training the network parameters is the negative log likelihood of the updated Cox model over the training population.
III. METHOD The input to our method is a dataset X = {x i , t i , δ i } N i=1 , of N patients, each consisting of a data vector x i associated to a target time-to-event t i and a binary value indicating censorship δ i. The data vectors x i , contain the 3D image of the lesion with the highest SUVmax 1 for each patient, identified manually on a full-body PET exam by a nuclear physician. The time t i is given in days. Considering this data, we design a CNN model that predicts the PFS, i. e. the time to (or risk of) the next disease progression, from a single input image. The parameters of the CNN are optimized minimizing a survival loss function capable of handling censored data. In the following, we describe the CNN model architecture (sec. III-A). Then, in (sec. III-B), we present a unified description of existing survival losses, including the widely used Cox function and its discrete version; a continuous regression loss 1 Standard Uptake Value of the maximum intensity voxel within the lesion.
B. Survival losses 1) Cox loss: A common approach to train learning models for survival analysis tasks has been to derive a loss function from the Cox regression model. Cox's partial likelihood models the joint probability of the events in the dataset, assuming they are statistically independent. The probability of each event is computed as the ratio between the risk of the patient i and the cumulative risk of all individuals still at risk at time t i : L cox-lin (X , β) = {i|δi=1} e β ⊤ xi {j|tj ≥ti} e β ⊤ xj , (1) where the product is done over the defined (i. e. uncensored) time events. Following  [25] , the linear model β ⊤ x is replaced by the output of a neural network h θ (x) parameterised by weights θ while the rest of the partial likelihood is kept. Computing the negative log likelihood from Eq.
1 leads to the following loss to optimise the parameters θ: l cox-nn (X , θ) = - {i|δi=1} [h θ (x i ) -log {j|tj ≥ti} e h θ (xj ) ] (2) with l the loss, h θ the risk function, and x i is the data vector describing patient i. This loss function pushes the network to predict risks that explain the order of events in the dataset. Note that the output of the current patient depends on the output of all patients at risk at the event time. This loss, originally proposed for an MLP, was later used to train deep  [7]  and convolutional  [8]  networks. 2) Discrete time survival loss: Gensheimer et al. [10]  chose a more interpretable output, with a survival model, called nnetsurvival, that predicts the time-to-event instead of the risk. The event time is defined over D discrete time intervals T = [∆ 1 , ∆ 2 ,.
Moreover, this choice makes the likelihood of each individual independent of other patients, favouring training with small batch sizes and as opposed to the full dataset for the Cox loss  [10]. Finally, since the method provides times rather than risks and given the time intervals are fixed beforehand, the output value range will not be dependent of the training range values. For the above reasons, the predicted values for a new individual are easier to interpret. 3) Continuous regression and pairwise ranking: A straightforward way to formulate survival analysis is as the Mean Squared Error (MSE) regression of the time-to-event t i for every uncensored patient i. To allow for censored data and further constrain the problem, Jing et al. [9]  proposed a network that predicts survival time ti trained with the RankDeepSurv loss consisting of an extended MSE loss l reg and a pairwise ranking regularization term l rank , balanced with parameters α and λ: l RankDeepSurv = αl reg + λl rank The extended MSE is: l reg = 1 N i∈Creg (t i -ti ) 2 , (6) where C reg defines the set of admissible individuals, including uncensored patients and individuals with predicted times ti greater than or equal to the censored times t i.
C. Model pre-training A widely used approach to counter the lack of annotated data, avoid overfitting, and speed up convergence is transfer learning, based on pre-training a network on large datasets such as ImageNet  [41]. However, to be effective, data for pre-training should bear at least some similarities with the actual task, e. g. , in input size and type of texture. Since there are currently no large publicly available PET imaging dataset, we opt for pre-training on our own data, exploiting the already provided annotations: lesion segmentation and survival data. A first pre-training task is thus a binary lesion/no lesion classification. The second task builds a meaningful embedding space from the survival data thanks to the tripletSurv loss. 1) Binary lesion classification: Despite a relative small number of patients, our work analyses annotated ROI images around lesions.
Therefore, by decomposing each full-body PET image into 3D patches, we can pretrain our network on the simpler binary lesion/no lesion classification task, determining a patch label according to the portion of the patch covered by the lesion. To build the training dataset, for each patient we take N i patch patches, where the N i patch /2 positive patches are all patches from patient i whose ratio between the lesion pixels and the background is greater than a ratio r +. The remaining N i patch /2 negative patches are sampled randomly from the image. The training and validation sets collect positive and negative patches from different patients. We also ensure a consistent patient-wise split between the pretraining and training stages. During pre-training, the output layer is replaced by two neurons with a softmax activation, and training is done with a cross-entropy loss.
A. Dataset description and data preprocessing We evaluate our survival model on a dataset composed of two prospective multi-centric MM studies (IMAJEM and EMN02/HO95) with respectively 87 and 65 patients considered among the whole dataset  [42] ,  [43]. The 3D baseline PET-images and survival time (until 7 years) are available for each patient (censorship rate of 45%). The most intense lesion for each patient was selected and a global polygon around the lesion was drawn by a nuclear physician with a propagation method. No pixel-wise segmentation step was involved in order to avoid the effects of the segmentation being operator or algorithm dependant. As preprocessing, we draw a rectangular bounding box tightly around each lesion's polygonal annotation. Then, as lesion size has been reported to be a non-informative feature (Jamet et al.
, 2020  [2] ), we resize each cropped bounding box to a fixed size 36 × 36 × 36 cube before feeding them to the network. This favours focusing on the textural analysis and improves the performance. This size was chosen according to the distribution of the MM lesions' scale in our database. Finally, we use data augmentation (rotation, translation, zoom, flip) to obtain 15 or 30 different images per patient. This leads to respectively a total of 2280 or 4560 3D images, split patient-wise into 4 sets for crossvalidation. Regarding the conventional methods used for comparison (c. f. Section V-A), we compute radiomics features extracted from a tight rectangular bounding box around each polygon surrounding a lesion (no further resizing), follow the IBSI standardisation initiative  [44].
VI. CONCLUSION This paper addresses for the first time the prediction of MM patient's progression from lesions of PET images with deep learning approaches. Relying on a prospective clinical dataset, we show that CNN can learn meaningful feature representations, removing the need to rely on conventional two-stage approaches based on generic texture descriptors and prediction methods such as Lasso Cox or RSFs. Our results suggest that analysing the image of the most intense lesion with our model and training strategies can help in determining high-risk patients at baseline. From a technical point of view, we propose a simple 3D CNN architecture as a backbone and study the influence of attention and pre-training steps as two effective strategies to adapt deep learning methods to clinical studies datasets.
Submission:  13/09/2021. This work has been partially funded by the  European Regional Development Fund , the  Pays de la Loire  region on the  Connect Talent MILCOM programme   (Multi-modal Imaging and Learning for Computational-based Medicine) ,  Nantes M étropole  (Convention  2017-10470 ), the  French National Research Agency  called " Investissements d'Avenir "  Labex IRON  no. ANR-11-LABX-0018-01  and  INCa-DGOS-Inserm-12558  ( SIRIC ILIAD ) L.
Systematic Literature Review Search Strategies Used to Identify Studies We systematically searched PubMed, Google Scholar, and ClinicalTrials. gov up to October 17, 2022, using the following search terms: "[(MCT8) OR (SLC16A2) OR (Allan Herndon Dudley syndrome)] AND [(therapy) OR (treatment)]" without restrictions on publication type or language. We also searched for eligible studies by screening the reference lists of included articles and reviews. This yielded n=2,430 publications.
Data Collection and Analysis A first reviewer screened the results of the above-mentioned search strategies for eligibility (see Criteria for inclusion of studies in this review) by reading titles and abstracts. In cases of uncertainty, the reviewer read the full text of the article and consulted a second reviewer. The study selection process was documented in a flow chart according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. 30  First, we extracted data on the favorite twelve preferred patient-oriented outcomes from the survey. We then extracted data from full-text articles and supplementary data on patient-oriented outcome measures and outcomes. We fully considered the data provided in the supplements. For missing data, we tried to contact the corresponding author of the respective study or digitized data from published figures using ImageJ.
Systematic Literature Review We then performed a systematic literature review to search for available treatment trials for MCT8 deficiency. After searching the PubMed and Google Scholar databases, we identified a total 2,430 publications (Figure  3 ). We removed 150 duplicates and excluded an additional 2,204 studies after screening their titles and abstracts that did not address therapeutic strategies for MCT8 deficiency. We read 76 full-text articles from peer-reviewed journals and assessed their eligibility for inclusion in the systematic review. We excluded commentaries (n=3), basic research articles (n=6), natural history studies (n=15), in vitro therapeutic trials (n=8), and animal model studies (n=12). After exclusion of 23 reviews, n=9 original articles  15, [19] [20] [21] [22] [23] [24] [25] [26]  remained.
1  Another compassionate use study of n=1 patient was published five years later with the addition of MMI (methimazole) for approximately 2 years, which is a thyrostatic medication that blocks TH synthesis only. 24  (ii) LT4 and LT3 (liothyronine) substitution: Long-term treatment with LT4 for 6. 5 years and short-term treatment with LT3 for 6 months was tested to saturate TH transport in n=1 patient. 21  (iii) Prenatal use of LT4: The only prenatal approach was performed in an index family, in which a mother was pregnant with a second affected child. 26  The male fetus was treated with intra-amniotic LT4 from the 18th week of Figure  3  PRISMA flowchart for systematic literature review. We searched PubMed, Google Scholar and ClinicalTrials. gov for "[(MCT8) OR (SLC16A2) OR (Allan Herndon Dudley syndrome)] AND [(therapy) OR (treatment)]" until October 17, 2022.