dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing We used three metrics to evaluate gait abnormalities. For knee hyperextension, mean knee extension (MKE) angle from the initial contact to the end of the terminal stance phase of the gait cycle was calculated. For SKG, the peak of knee flexion (PKF) angle during the swing phase was calculated. The drop foot was represented by the mean ankle dorsiflexion (MAD) angle during the swing phase. Knee flexion velocity at toe-off, ankle push-off work, leg circumduction, and early and late braking impulses  38  were also computed. Early and late braking impulses were calculated as the areas of the posterior component of GRF curve (i. e. , negative anteriorposterior GRF) preceding and following the propulsion, respectively. Circumduction was calculated as the maximum lateral displacement of the calcaneus body origin (tracking and prediction results) and of the ankle marker (ID results).
Since the ID and tracking simulation results were based on kinematic and GRF data collected during gait analysis, each condition had four trials; the mean and standard deviation were calculated after normalizing the gait cycle. Each predictive simulation condition yielded a single result that was also normalized. The ID and tracking results were filtered using a lowpass second-order Butterworth filter in MATLAB. All simulations were performed on the same workstation (3. 60 GHz Intel Core i3 processor). The computational time, number of iterations, and stride time of the simulations are presented in Supplementary Table  S4.
The different gait speeds showed a linear relationship between the increases in PKF, ankle push-off work, and knee flexion velocity, which has also been described by others  2. A limitation of the present work was the use of a generic musculoskeletal model that did not include subject-specific characteristics. As neither magnetic resonance imaging (MRI) nor electromyography (EMG) data were collected, it was not possible to create a personalized musculoskeletal model. MRI and EMG data could improve the personalization of muscle-tendon parameters, whereas standardized values may not accurately represent the physiology and anatomy of the patient. Another limitation is that we analyzed only one patient. Although each stroke patient may present a unique gait pattern, future studies on pathologic gait in more patients based on these methods could lead to more reliable conclusions in a clinical context.
Background and objectives Effective pain control improves postoperative rehabilitation and enhances recovery. The aim of this review was to evaluate the available evidence and to develop recommendations for optimal pain management after open liver resection using Procedure-Specific Postoperative Pain Management (PROSPECT) methodology. Strategy and selection criteria Randomized controlled trials (RCTs) published in the English language from January 2010 to October 2019 assessing pain after liver resection using analgesic, anesthetic or surgical interventions were identified from MEDLINE, Embase and Cochrane databases. Results Of 121 eligible studies identified, 31 RCTs and 3 systematic reviews met the inclusion criteria. Preoperative and intraoperative interventions that improved postoperative pain relief were non-steroidal anti-inflammatory drugs, continuous thoracic epidural analgesia, and subcostal transversus abdominis plane (TAP) blocks.
7 In addition, the approach reports true clinical effectiveness by balancing the invasiveness of the analgesic interventions and the degree of pain after surgery, as well as balancing their efficacy and adverse effects. 7 he aim of this review was to evaluate the available literature on the management of pain after open liver resection in adults regardless of the indication. Postoperative pain outcomes defined as pain scores and opioid requirements were the primary focus, but other recovery outcomes, including adverse effects, were also assessed, when reported, and the limitations of the data were reviewed. The ultimate aim was to develop recommendations for pain management after open liver resection. The search terms relating to pain interventions for open liver resections included ("hepatectomy" OR "hepatectomies" OR "liver resection" OR "liver resections" OR "hepatic resection" OR "hepatic resections" OR "hepatic lobectomy" OR "hepatic lobectomies" OR "living donor hepatect*") AND ("pain" OR "pains" OR "pain management" OR "postoperative pain" OR "VAS" OR "visual analog*" OR "VRS" OR "verbal rating scale*" OR "NRS" OR "numerical rating scale*" OR "pain rating" OR "epidural" OR "neuraxial" OR "intrathecal" OR "paravertebral" OR "spinal" OR "infiltration" OR "nerve block*" OR "neural block*" OR "paravertebral block*" OR "field block*" OR "transversus abdominis plane bloc*" OR "TAP block*" OR "NSAID" OR "nonsteroidal anti-inflammator*" OR "nonsteroidal anti-inflammator" OR "COX-2" OR "paracetamol" OR "acetaminophen" OR "clonidine" OR "opioid*" OR "ketamine" OR "corticosteroid*" OR "gabapentin" OR "pregabalin").
The RCTs that reported data pooled from patients undergoing simultaneous surgical procedures were excluded as were the RCTs evaluating combinations of different perioperative interventions such as studies comparing ERAS programs to conventional care, because the variability of definitions and protocols can make practical recommendations about a particular intervention impossible. Meta-analyses that reported data on mixed surgical procedures were only included when a subanalysis on liver resection was available. Data extraction and data analysis adhered to the PROSPECT methodology (Online supplemental file 1). 7 Pain intensity scores were used as the primary outcome measure. We regarded a change of more than 10 mm out of 100 mm on the Visual Analog Scale (VAS) or more than one out of 10 on a Numerical Rating Scale (NRS) as clinically relevant.
Several studies confirmed the analgesic efficacy of TEA typically administered in the postoperative period, particularly during coughing and deep breathing. The limitations with TEA in patients undergoing liver resection include concerns of epidural hematoma and subsequent catastrophic neurological injuries or unplanned delays in postoperative removal of the epidural catheter due to coagulopathy and the avoidance of intraoperative use due to the potential hemodynamic effects of vascular clamping, as well as the concerns of delaying patient mobilization in the context of ERAS pathways. In fact, a large (n=68 028) retrospective analysis of an administrative database found that the use of TEA for major liver resection is very low (approximately 6%). 46 he propensity-matching technique showed that TEA did not seem to influence the incidence of postoperative complications, although it was associated with an increased use of blood transfusions and a longer hospital LOS in this particular population who had undergone major liver resection.
Analyzing Gene Content of Transposable (Pro)Phage To better understand how Leptospiraceae prophages relate to known transposable phages in terms of gene content, a set of 11,745 proteins was built by combining (i) 8875 proteins predicted from the 156 prophages, (ii) 2625 proteins from 48 reference transposable phages infecting a wide range of bacterial species, and (iii) 245 proteins from the three known Leptospira biflexa phages LE1, LE3, and LE4. These 11,745 proteins were clustered into groups of orthologous proteins (OG) using a two-step procedure involving remote homology detection (Table  S4 ). Ogs were then functionally annotated using HMM comparisons to the PHROG database  [29]. Considering only the 204 transposable (pro)phages, their 11,500 proteins are organized into 480 Ogs with at least two proteins and 392 singletons.
Genomic organization of the predicted prophages. One reference genome was chosen for each of the six prophage clades and reference phage Mu, B3, and D3112 are also shown. Protein coding genes are indicated with arrows showing their transcription orientation and are colored according to their functional category defined in the PHROG database. In addition, proteins whose best match in RefSeqVirus is a L. biflexa phage (LE1, LE3, or LE4) are highlighted in red dashed arrows. Similarities between genes from adjacent virus are displayed and were here defined by OG membership. On the left, two schematic representations of the head morphogenesis and final phage Mu structure. TnpA: transposase. TnpB: DNA transposition protein. MCP: major capsid protein; Pilot: Minor head and ejection (pilot) protein; Portal: portal protein; TerL: terminase large subunit; TerS: terminase small subunit; Sc: scaffolding; Prot: head maturation protease; BH, BS, BW: baseplate hub, spike, and wedge proteins; Sh: tail sheath; Tube: tail tube; MTP: major tail protein.
Prophage Search in the RefSeq Database Four protein sequences, namely, GemA, Mor, portal, and DDE transposase from phage Mu and B3 were downloaded from the NCBI (respectively NP_050620. 1, NP_050621. 1, NP_050633. 1, NP_050607. 1 for Mu and YP_164038. 1, YP_164037. 1, YP_164068. 1, YP_164048. 1 for B3) as well as Tn552 transposase (Accession: X52734. 1, CAA36949. 1 DDE transposase). These nine proteins were compared to RefSeq proteins encoded by L. weilii (BLASTp, bit-score > 50, accessed on 1 January 2021  [43] ). This allowed for the identification of the transposase protein of one possibly complete prophage named WETA1. This transposase sequence was then compared to genomes in the Leptospiraceae family (taxid:170) contained in RefSeq Genome (tBLASTn, accessed on 1 March 2021). Genomic contigs carrying a transposase were manually inspected for the presence of additional annotated phage proteins in order to identify potential prophages.
Using significant similarities, singletons and protein clusters were then again clustered with MCL (inflation 2. 0). This second step involving HMM comparisons was then performed a second time to group clusters that were similar, yet not already gathered due to the entangled nature of similarity networks and to the fact that larger and more diverse clusters allow the detection of more distant homologies. Finally, using the same tools, multiple alignments and HMM profiles were computed for all OGs. The OGs HMM profiles were compared to the HMM profiles of the PHROG database  [29]  (annotation release v2), which contains well annotated protein clusters of prokaryotic viruses. This provided a probable functional annotation for OGs similar with at least one PHROG profile. These putative functions were refined using the Virfam web tool with default homology thresholds  [49].
Phylogenies and Gene Content Matrix From these OGs, the transposase TnpA, the transposition target binding ATPase TnpB and the baseplate wedge subunit BW2  [31]  were identified (OG3, OG2, and OG7, respectively) and their multiple alignments were used to compute phylogenies with RAxML  [50]  (v8. 2. 12, 100 bootstrap replicates). Based on the congruency of TnpA and TnpB phylogenies, their respective multiple alignments were concatenated and used to build the phylogeny displayed in Figure  2  (RAxML, 100 bootstrap replicates), and visualized and refined using iTOL  [51]. A presence/absence matrix of OGs was generated using the ggplot R package  [52]  using one representative genome for each genus defined by VIRIDIC (25 genera for the 156 prophages, 22 for known transposable phages and 2 for L.
Genomic Maps Genomic maps of one representative genome for each of the six defined prophage clades and three reference transposable phages (Mu, B3, and D3112) were constructed using the genoPlotR package  [53]. Plotted genes were colored using their PHROG functional categories and their synteny was identified by OG membership. Annotations were manually refined considering additional information on PHROGs similar to the OGs (e. g. , their similarity to PFAM profiles) and on the presence of annotated Mu proteins. For example, the Mu baseplate module was annotated based on its extensive study by  Büttner et al. in 2016 [31]. Lysis genes were predicted within the Apollo annotation frame  [54]. Briefly, genes were defined taking into account the fact that o-spanins are entirely embedded in i-spanin genes, and translated proteins were compared to dedicated databases, and searched for lipo-boxes and transmembrane domains.
Number of Sequenced Leptospiraceae Species To estimate the number of genomes available for all Leptospiraceae species, the RpoB protein that encodes the β subunit of bacterial RNA polymerase was used. First, RpoB from L. interrogans (serovar Lai 56601: LA_3420) was compared using tBLASTn to all complete Leptospiraceae genomes of the KEGG database to verify that this protein was only found as a single copy in each complete genome. Then, this protein was compared to the genomes in the Leptospiraceae family (taxid:170) contained in RefSeq Genome (tBLASTn, bit-score > 50, accessed on 1 March 2021). The number of significant hits was then computed for all species of this family, and each of the Leptospira species was affiliated to one of the five clades (P1-virulent, P1-low-virulent, P2, S1, S2) (Table  S3 ).
Acknowledgments: A. T. is very grateful to  Mathieu Picardeau ,  Pascale Bourhy , and the  Institut Pasteur in Paris  for accepting to archive Adolf Weil's manuscript describing the first cases of a new, suspected infectious disease, which later received his name. This induced the present search for transposable prophages in Leptospira, a tribute to Adolf Weil but also to AT's late Mu colleagues,  Ahmad Bukhari ,  Ditmar Kamp ,  Neville Symonds , and  Piet van de Putte. We thank  Jolene Ramsey  and  Ryland Young  for helping to predict lysis functions,  Phoebe Rice  for the structural prediction and discussion of the WETA1 and INNN139 transposases, and  Simon Roux  for helpful discussions.
In this issue of Clinical Infectious Diseases, the same consortium aimed to explore the link between oral health and brain abscess, and to evaluate the potential impact of invasive dental procedure on the risk of brain abscess (Bodilsen J, Helweg-Larsen J, Otto Jarløv J et al. Dentist's visits and risk of brain abscess: A nationwide, population-based case control study. Clin Infect Dis 2022). This time, they restricted the definition of cases to patients with culture-proven brain-abscess caused by oral cavity bacteria during years 1989-2016 (n=362), and selected 10 population controls per case, matched on age, sex, and residential area (n=3,257). They extracted data from four nationwide registries: i) the National Patients Registry, for information on all hospital admissions; ii) the Civil Registration System, for information on vital and migration status for all Danish residents; iii) the Danish National Health Service Register, for information on primary healthcare services, including dentist's visits; and iv) the healthcare records of all departments of clinical microbiology providing services to neurosurgical departments in Denmark, for information on the microbiology of brain abscess.
g. physical distancing, use of face masks). In a sensitivity analysis, we also considered scenarios in which individuals older than 70 years further reduced their contacts by 20% or 40% instead of 0% assumed in our baseline scenario. Contact matrices were generated from the COMES-F survey  [7]  which describes age-and location-specific contact patterns in the French population, using the socialmixr package  [8]. For each scenario, we defined the origin of time as the first day when the daily number of ICU admissions exceeds 10 at the national level (0. 15 daily admissions/ million inhabitants).
Acknowledgements Funding: We acknowledge financial support from the  Investissement d'Avenir program , the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (grant  ANR-10-LABX-62-IBEID ),  Santé Publique France , the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ), and the  European Union 's  Horizon 2020 research and innovation program  under grants  101003589  (RECOVER) and 874735 (VEO),  AXA.
In this paper we propose a self-supervised imagebased method, exploiting, at training time only, the imprecise kinematic information provided by the robot. In order to avoid introducing time-consuming manual annotations, the problem is formulated as an auto-encoder, smartly bottlenecked by the presence of a physical model of the robotic instruments and surgical camera, forcing a separation between image background and kinematic content. Validation of the method was performed on semi-synthetic, phantom and in-vivo datasets, obtained using a flexible robotized endoscope, showing promising results for realtime image-based 3-D pose estimation of surgical instruments.
The idea behind this loss is to ease the optimization process by providing the network with a range in which the solution is likely to be found, avoiding any hard-coded constraint. The decoder φ processes the predicted projection mask m and the backgroundized image x b to reconstruct the image x. The network is implemented as a UU-net, an extension of the well-established U-net architecture  [34] , having two separate contracting paths for m and x b , and an expanding path where corresponding features from the two contracting branches are concatenated. In order to learn the auto-encoder formulation defined in eq. 3, enforcing the reconstructed image x to match the input image x, we implemented a perceptual loss L r , defined as: L r = Γ(x) -Γ(x) 2 , ( 6 ) where Γ is a feature extractor implemented as a VGG-16 network  [35]  pre-trained on the publicly available Imagenet dataset  [36].
The two VTK models were used exclusively in the datasets generation process, as detailed later in this paragraph, and not included in the training architecture, given their non-differentiability. Three different datasets were used for validating the proposed approach, two from real acquisitions and a semisynthetic one. The first real dataset (phantom) was recorded on the benchtop, using a plastic phantom model of the human digestive system. During the acquisition both the endoscope and the plastic model were moved, in order to avoid having a static background appearance. The second real dataset (in-vivo) was recorded during a 4-day set of in-vivo experiments on porcine models  1. This dataset presents several challenges compared to standard retinal, laparoscopic and benchtop datasets, including low foreground/background contrast, highly cluttered and changing background, frequent occlusions and bleedings, and strong tool-tissue interactions.
The semi-synthetic dataset was created by blending background-only images, automatically extracted from the in-vivo dataset by parsing the associated kinematic information (although imprecise), with rendered robot instruments, obtained using the VTK model of tools and renderer, and random kinematic configurations k. In order to simulate a realistic imprecise kinematic information k s for this dataset, we added to the nominal value k a normally distributed noise, whose range was empirically chosen to match the real-dataset noise. The full process followed to build the semi-synthetic dataset is shown in fig. 4. For the semi-synthetic dataset, the ground truth (GT) joint configuration is known a-priori, due to the way the dataset was built. For the phantom and invivo datasets, the GT joint configuration is unknown, since every measurement attempt is affected by uncertainties, resulting on the imprecise estimation k s.
Instead, the evaluation on these two datasets was performed indirectly: the 3-D shape of the instruments is reconstructed according to the predicted kinematics k, and projected on the camera plane: the projected mask m can be then compared to the instruments ground truth location on the images, obtained via manual segmentation, providing an indirect evaluation of the estimated k. In order to train and evaluate the physical module, a dataset was built generating random couples {k i , m i }, using the VTK model of instruments and renderer. For the training of the inpainting network, two separate datasets were built. The first one, real backgrounds, was built automatically extracting background-only images from the in-vivo dataset, according to the associated kinematic information (although imprecise), and used to train the backgroundizer for the semi-synthetic and in-vivo experiments; the second one, phantom backgrounds was built by extracting a single background-only image from the phantom dataset, strongly augmented through operations like rotation, cropping, lighting etc.
IV. RESULTS In this section we present the validation results obtained by the proposed approach on the three datasets. In order to assess the actual contribution of the method, we compared its performances to two baseline models. They both consist of solely the regressor model ψ trained by minimizing only the loss function L a defined in eq. 5. The first baseline model BSupK s was trained by setting the tolerance parameter t to 0, thus resulting in a strongly-supervised training, having k s , which is unreliable/imperfect, as target; the second baseline model BSupSoftK s was trained by setting the parameter t to the same value as the one used for our main model, thus resulting in a softer supervision. Together with the baselines, also the raw measured kinematics k s provided by the robotic system was evaluated, according to the same modalities.
B. Regressor evaluation For the semi-synthetic dataset, the GT kinematic configuration k of the instruments is known, and the mean absolute error for each joint could be computed. Reprojection of the estimated instruments shape on the image plane was also evaluated through the standard segmentation metric Intersection over Union (IoU). Results are reported in table II. For the real phantom and in-vivo datasets the reprojection error with respect to the manually annotated GT was evaluated, via IoU. Results are reported in table III. Qualitative results for the three datasets can be observed in fig. 5. Results on the three datasets, despite not reaching a stateof-the-art accuracy, show a consistent improvement provided by the proposed approach. This is shown directly by the improved kinematics estimation in the semi-synthetic dataset, and indirectly by the higher reprojection accuracies on the semi-synthetic and real datasets, with respect to both the baselines and the raw kinematics.
In the semi-synthetic and in-vivo datasets the baselines perform better than the raw kinematics k s , while in the phantom dataset the performances are comparable. This may be due to the smaller size of the phantom dataset, which prevents the networks from learning to average out the random noise of k s with respect to the GT value k. Our model, on the contrary, is not affected, proving its capability to learn also from reduced amount of noisy data. The evaluation of BSupK s provides an ablation study of our method, showing that the improvements reached are not solely caused by the soft loss. In the in-vivo dataset, a lower improvement is reached. This can be explained by two main reasons: 1) the dataset is more challenging due  to the factors mentioned in section III (e. g. low instrumentsbackground contrast), which can affect our model more than the baselines, since the training is almost completely imagebased; 2) phenomena as tool-channel interaction and slackening  [42] , more evident under strong tool-tissue interaction, can deform the flexible instruments in configurations that our model, based on constant curvature assumption, cannot reach.