dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding AP was funded by the  Andalusian Autonomic Government  (Grant Ref. :  PAIDI 21_00207 ). SF was funded by the  German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)  under Germany's  Excellence Strategy-EXC 2002 /1 ,  Science of Intelligence  (Project Ref. :  390523135 ) and the  Einstein Foundation Berlin. MS was supported by a  European Union 's  Horizon 2020 research and innovation program , Grant agreement  852570 , and by Grant  BOF17-GOA-004  from the  Research Council of Ghent University. CG-G was supported by Grant  IJC2019-040208-I  and Project  PID2020-116342GA-I00  funded by  MCIN/AEI/10. 13039/501100011033 , and Grant  RYC2021-033536-I  funded by  MCIN/AEI/10. 13039/501100011033  and by the  European Union NextGenerationEU/PRTR.
1 ) using a tree structure. This paper is not a systematic review of the terminology used but a critical analysis of the terms generally used based on a literature review on musculoskeletal modeling (mainly on upper limb). To this end, we discuss the different terms used in the literature based on a basic description of the different model components and examples and we provide definitions of key terms. Firstly, we assess the strategy with which the problem is solved: forward or inverse. Secondly, we evaluate the different types of problems: static or dynamic. Thirdly, we explain, using computer science terminology, how these problems are solved via optimization processes. Fourthly, we show how additional data such as electromyography (EMG) are integrated and tracked to obtain more physiologically realistic results.
A static optimization process determined the optimal values of these parameters to minimize the difference between the net joint torque estimated by the model and the same values determined through experimental data. Likewise, Lin and Pandy  [36]  used static optimization to find a set of muscle excitations to drive a model using OpenSim software  [37]. They collected gait data and calculated the set of muscle excitations that reproduced the experimental data over one stride cycle, minimizing the difference between the estimated and measured ground reaction forces. These two optimizations conducted by  [19]  and  [36]  in a model with forward solving strategy were performed in one step for all the data from the experimental situation of interest (the time is not divided into time intervals, it is a continuous variable) and so are continuous-time static optimizations  [32].
| Statistical analysis Descriptive statistics were generated using Microsoft 365 Excel Data Analysis, and other statistical analyses were performed using Statistical Analysis System (SAS University Edition 9. 04. 01M6P11072018). Continuous variables are presented as median and range (minimum; maximum). The PAC, potassium concentration, and SBP at the different time points were compared using Wilcoxon's signed rank test. Comparisons of baseline potassium concentration, SBP, aldosterone concentration, and AVR between groups of healthy cats receiving placebo, healthy cats receiving telmisartan at 1 and 2 mg/kg, and cats with PHA were made using the Kruskal Wallis test followed by paired comparison using the Mann-Whitney test when relevant. Results are represented graphically by box-and-whisker plots.
Balance assessments Rotarod. A rotarod apparatus (Bioseb, Chaville, France) was used to test the balance and motor coordination of Wfs1 E864K mice. Ten to fifteen Wfs1 WT and Wfs1 E864K mice were included in this assessment. Familiarization of the mice with the rotating rod was immediately followed by a training session on day 1 (P24). The mice were trained to stay on the rod, with a progressively increased speed (from 4 rpm up to 10 rpm). Following each fall, the mice were replaced on the rotating rod (total of 4 trials on day 1). On day 2 (P25) and 3 (P26), the mice were tested with a fixed speed of 10 rpm. During these trials, the latency to fall was measured up to 180 s. Each animal underwent 4 trials per day. For each training and test day, the data were averaged per mouse and subsequently per group (Wfs1 WT and Wfs1 E864K homozygous mice).
The sections were then washed in 0. 1% Triton X-100/PBS three times, incubated for 1 h at room temperature with secondary antibody solution (Alexa-488 conjugated goat anti-mouse IgG (1:500); Alexa-546 conjugated goat anti-rabbit IgG (1:500); Invitrogen) with 3% HS in 0. 1% Triton X-100/ PBS. Nuclei were counterstained using 4',6-diamidino-2-phenylendole (DAPI; 1:5000, Sigma-Aldrich). Cryosections were rinsed three times with 0. 1% Triton X-100/PBS, three more times with PBS and mounted on glass slides with ProlongGold Antifade mounting medium (Invitrogen). All images were acquired using a Zeiss LSM880 Fast-airy-scan scanning confocal microscope. The cross-sectional area of Rosenthal's canal as well as the number of SGN were measured, using the NIH ImageJ software. The number of neurons was then divided by the cross-sectional area to determine the SGN density (n = 4 to 5 sections per cochlea, 3 to 4 cochleae per genotype).
H 2 O was used as negative control (neg). C Sanger sequencing chromatograms confirming the G > A transversion at position c. 2590 in Wfs1 E864K/+ and Wfs1 E864K mice. D Comparative analysis of the time spent on fixed speed rotating rod (10 rpm) before falling (latency to fall in seconds) for P24, P25, and P26 Wfs1 WT (blue; n = 14) and Wfs1 E864K (red; n = 15) mice. Mutant mice showed impaired performance compared to Wfs1 WT mice. Each mouse was subjected to 4 trials of 180 s duration per day. Data are mean ± SEM (two-way ANOVA followed by Tukey's test, ***p < 0. 001). E Evaluation of vestibular deficits in P21, P24, P27, and P31 Wfs1 WT (blue; n = 15) and Wfs1 E864K (red; n = 13) mice. Six different tests were evaluated (scored from 0 to 4) and averaged to obtain a vestibular deficit index (in arbitrary units (AU)).
ACKNOWLEDGEMENTS We thank  Dr Barrett  ( University of Birmingham, UK ) for providing the pCMV-HA-Na + / K + ATPase β1 and pCMV-Myc-WFS1 WT plasmids. We acknowledge the imaging facility MRI, member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»). We thank the breeding facility from the  University of Montpellier  ( RAM-CECEMA ) and from the  Institute of Neurosciences of Montpellier  for their help in animal breeding and handling.
FUNDING This work was supported in part by external resources of the  University of Montpellier (Fondation Pour l'Audition FPA   RD-2019-13 ,  Agence Nationale pour la Recherche  ( ANR-12-JSV1-0008-01 ),  Fondation Maladies Rares , to BD). EMR is supported by  The Snow Foundation ,  Eye Hope Foundation  and  Fondation de France , and LC by  Association syndrome de Wolfram  and the  Région Occitanie.
Whole-body PET data of 77 non-cachectic and 80 cachectic lung cancer patients were analyzed through the 3-step ENHANCE-PET framework. Disparities between correlation patterns were observed in both groups in subcutaneous fat and the psoas muscle, hinting towards a possible metabolic fingerprint which might be unique to cachexia. A critical bottleneck for AI-based methods is their limited capability in the heterogeneous domain of PET imaging, due to the variety of scanners and tracers. Xue et al. developed a simple way to integrate domain knowledge in deep learning for CT-free PET imaging  [1]  (OP242). In contrast to conventional direct deep learning methods, they decomposed the complex end-to-end generation into anatomy-independent textures (relating to tracers and diseases) and anatomydependent correction.
A dataset of 117 patients was analyzed through a total of 28 clinical, biological, and PET/CT features. The clinical endpoint to train the prognostic model was the progression-free survival at 6 months and the overall survival at 12 months. Features were sorted according to their selection frequency using a LASSO logistic regression and an exhaustive cross-validation. The model performances were analyzed using either the top 4 features, the top 8 features, or all of the 28 features. For both outcomes, the models using the top 4 or the top 8 features outperformed the model using all the 28 features without selection, as well as the simple baseline with no biomarker. This work underlined the interest of combining clinical, biological, and imaging biomarkers for predicting the outcome, but also the importance in rigorously selecting the features to improve model performances.
Oncology highlights -license to detect cancer The most relevant newcomers in diagnostic oncology this year were the inhibiting molecules for the fibroblast activation protein (FAP), a target that is overexpressed in cancerassociated fibroblasts of several tumor entities. With only a few submitted abstracts in 2021, FAPI (n = 20) surpassed PSMA (n = 19) this year as a target among the highly rated abstracts. This underlines how incredibly dynamic the field is moving forward. Novruzov et al. from Azerbaijan presented data from 100 patients with primary breast cancer imaged with [ 68 Ga]Ga-FAPI-46 and [ 18 F]FDG PET/CT. Patients were scanned after 10, 30, and 60 min. A significant increase in tumor to background ratio was seen from 10 to 30 min followed by a plateau phase; therefore, 30-min uptake time might be sufficient for adequate imaging.
Further improvement in NET therapy might be achieved with the use of an alpha emitting nuclide. Dr. Tworowska et al. from Houston, USA, presented data using [ 212 Pb] Pb-DOTAMATE in patients that were progressive after regular PRRT. In this prospective phase I study, they included 10 patients that received up to four, 8-week cycles of [ 212 Pb]Pb-DOTAMATE. Response to treatment was measured per RECIST 1. 1 with [ 68 Ga]Ga-or [ 64 Cu] Cu-DOTATATE PET/CT. In 7 of 10 patients, partial response could be observed. Finally, the use of FAP as a target for therapy was also present at EANM 2022. Of four abstracts suggested as highlights, we selected two, prospective multicenter studies: a basket phase 1/2 study including patients with advanced or metastatic solid tumors, the LuMIERE trial and a study focusing on solitary fibrous tumors.
Dr. T. Hope et al. CA, USA, presented the multicenter LuMIERE data evaluating the safety and tolerability of [ 177 Lu]Lu-FAP-2286. Patients with advanced solid tumors with three or more therapy lines were selected and planar and SPECT/CT scans performed at 4, 24, 48, and 168 h after each dose in each cycle to calculate organ and tumor dosimetry. The recruiting included already dose levels from 3. 7 to 7. 4 GBq. In this range, the safety profile was manageable, and some preliminary antitumor activity could be observed  [1]  (OP355). The second abstract presented by Dr. Fendler et al. Essen, Germany, focused on the use of FAP as a theranostic target in solitary fibrous tumors (SFT). The work was based on an extensive immunohistochemistry and immunofluorescence study on a pan cancer cohort with over 800 patients and 126 tumor entities.
Conclusions We report data from the scientific literature obtained in experimental models of painful diabetic neuropathy with high translational value since the pharmacology is close to that observed in humans and the clinical symptoms of neuropathic pain are robust and reproducible. Most behavioral studies carried out to assess neuropathic pain rely on the application of acute nociceptive (to assess hyperalgesia) or non-nociceptive (to assess allodynia) stimuli to elicit a painful response. Spontaneous pain behavior was not evaluated. The majority of studies was performed in male rats whereas diabetes and painful diabetic neuropathy affect in equal proportion men and women. While we focused on diabetic neuropathic pain, chemotherapy-induced peripheral neuropathy and trauma-induced neuropathic pain models often but not systematically share identical pathophysiological mechanisms  [65].
Funding: This research was funded by the  Agence Nationale de la Recherche (ANR)  (No. ANR-19-CE18-0018-01  and " Investissements d'Avenir "  ANR-16-IDEX-0001 CAP 20-25 ), the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Auvergne-Rhône-Alpes Région (France) , the  Université Clermont Auvergne. N. M. received a scholarship from the french  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Demographic and questionnaire data The two groups of participants were matched for gender, age, education, and BMI (ps > 0. 065). They did not differ in subjective ratings of hunger (p > 0. 162). As expected, participants with High PS scored significantly higher than participants with low PS on all the CAPE sub-scales (all ps < 0. 001) and the BDI-II scale (p = 0. 002). No differences emerged in the Digit Span forward test (p = 0. 479), on the TEPS anticipatory (p = 0. 270) and consummatory scales (p = 0. 718), as well as on the BVAQ questionnaire (p = 0. 981). Lastly, High PS participants showed higher levels of impulsivity on the BIS-11 questionnaire compared to Low PS participants (p = 0. 001) (See Table  1 ). See supplementary information for detailed statistics.
We found that the latent model more faithfully reproduces the empirical data compared with the source model (Figure  1D , rightmost panel). We determined the brain regions contributed to each of the nine latent modes previously found in terms of brain functional networks. We created a set of surrogate signals in the latent space by introducing standard Gaussian noise into one latent dimension while keeping the other eight dimensions devoid of any signal. We repeated by changing the noise signal for each of the nine modes. We then decoded the surrogate signals in each case to obtain the corresponding spatial pattern in source space. We then associate these patterns with the activation (mode positive) or deactivation (mode negative) of the seven resting state networks from Yeo  39  (Figure  1E ).
Models of the coupled manifold modes outperform high dimensional models in the original state space We employed a VAE to obtain a low-dimensional manifold representation of fMRI data obtained from resting-state healthy participants from the HCP dataset. The data was obtained by applying a coarsegrained parcellation scheme comprising 62 regions, following our previous findings. Subsequently, we modelled the dynamics of each neural mode (i. e. manifold dimension) using nonlinear Stuart-Landau oscillators, which exhibit dynamic characteristics determined by a Hopf bifurcation (see Methods for the mathematical expression of the model). Typically, these models are fitted to reproduce empirical observables, such as the functional connectivity (FC)  41, 42. In our case we built a manifold model to fit the empirical functional connectivity between latent space modes ሺ ‫ܥܨ‬ ா by subgroups of 10 participants and created 100 models that fit these average matrices.
Participants The neuroimaging dataset used in this investigation was obtained from the HCP (Human Connectome Project) March 2017 public data release. From the total of 1,003 participants available in the release, a sample of 1000 to the first part when only considered rest and 990 participants was chosen for the second part due to not all participants performing all tasks. The HCP task battery comprises seven tasks, namely working memory, motor, gambling, language, social, emotional, and relational tasks. Detailed descriptions of these tasks can be found on the HCP website  62. The tasks were intentionally designed by HCP to cover seven major cognitive domains, which aim to capture the diversity of neural systems: 1) Visual, motion, somatosensory, and motor systems; 2) Working memory, decision-making, and cognitive control systems; 3) Category-specific representations;4) Language processing; 5) Relational processing;6) Social cognition; 7) Emotion processing.
Neuroimaging extraction of functional time series The processing and extracting data from both resting-state and task-based fMRI datasets is detailed in the HCP (Human Connectome Project). The preprocessing steps, described in detail on the HCP website, utilized standardized methods from FSL (FMRIB Software Library), FreeSurfer, and the Connectome Workbench software. The process included correcting spatial and gradient distortions, addressing head motion, intensity normalization, bias field removal, registration to the T1-weighted structural image, and transformation to the 2-mm Montreal Neurological Institute (MNI) space. The FIX artefact removal procedure  63, 64  and ICA+FIX processing  65, 66  were employed to remove structured artefacts and head motion parameters, respectively. The preprocessed time series data of all grayordinates were in HCP CIFTI grayordinates standard space and available in surface-based CIFTI files for each participant during resting-state and each of the seven tasks.
A custom-made MATLAB script using the ft_read_cifti function from the FieldTrip toolbox was used to extract the average time series for all the grayordinates in each region of the each parcellation used, as defined in the HCP CIFTI grayordinates standard space. The BOLD timeseries were filtered using a second-order Butterworth filter with a frequency range of 0. 008-0. 08Hz and extracted the first and last 5% volumes to avoid the borders effect of the filtering.
Parcellations The neuroimaging data underwent processing using the three standard parcellations with different amount of brain cortical regions: i) the Mindboggle-modified Desikan-Killiany parcellation, called DK62  36  ; ii) the Schaefer considering 500 regions; and iii) the Schaefer parcellation considering 1000 regions This DK62 parcellation consists of a total of 62 cortical regions, with 31 regions in each hemisphere, as defined by Klein and Tourville in 2012. Consequently, the DK80 parcellation contains a total of 62 regions, precisely defined in the common HCP CIFTI grayordinates standard space. Then, the Schaefer 500 and 1000 parcellation was also used with 250 and 500 cortical regions per hemisphere respectively, and well defined in the HCP CIFTI. In this parcellation  37.
The intrinsic frequencies ߱ (which lie in the 0. 008-0. 08Hz band) were estimated from the empirical data as the averaged peak frequencies of the narrowband the signals of the different modes. Finally, the mode's bifurcation parameter is ܽ which defines the dynamics of the mode with respect to the Hopf bifurcation. In particular, this bifurcation is characterised by bifurcation parameter (ܽ) that induces qualitative changes in the dynamical behaviour when the system crosses the bifurcation point (ܽ ൌ 0). In the supercritical regime (ܽ 0,), the oscillator generates self-sustained oscillations, whereas in the subcritical regime (ܽ ൏ 0), the dynamics converge to a fixed point. Importantly, the addition of noise to the model causes the dynamics near the bifurcation point (ܽ~0) to stochastically transition between both regimes, resulting in oscillations with complex amplitude modulations.
Overall, we use the term Latent Generative Effective Connectivity (LGEC) for the optimised , which reflects the connectivity between the modes. Obtaining sources networks by decoding the latent modes. We created a set of surrogate signals by introducing standard Gaussian noise into one latent dimension while keeping the other eight dimensions devoid of any signal. We repeated this process, varying the dimension with noise and the dimensions without any signal, and we called M "N" with N from 1 to 9 to each one. Subsequently, we decoded each of these nine sets of surrogate latent signals, mapping them from the latent dimensions to the 62 brain regions of the original empirical data for each case. Next, we examined the regions in the source space that were activated in response to the presence of noise in each latent dimension.
Support vector machine classifier We use a SVM with Gaussian kernels as implemented in the MATLAB function fitcecoc. The function returns a full, trained, multiclass, error-correcting output codes (ECOC) model. This is achieved using the predictors in the input with class labels. The function uses K(K -1)/2 binary SVM models using the one-versus-one coding design, where we used K = 8 as the number of unique class labels. In other words, the SVM had as inputs the elements of the LGEC obtained in each of the 100 models for each one of the 7 tasks and the resting-state condition. The output was eight classes corresponding to the conditions (rest and seven tasks). We subdivided into 90% training and 10% validation, repeated and shuffled 100 times. For the classification using the functional connectivity in the original source space, we average the FC across 9 participants, as we did with the LGEC computation, and use as an input the upper diagonal elements of the matrices.
Acknowledgments Y. S. P is supported by  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant  896354. M. L. K. is supported by the  Center for Music in the Brain , funded by the  Danish National Research Foundation  ( DNRF117 ), and  Centre for Eudaimonia and Human Flourishing at Linacre College  funded by the  Pettit and Carlsberg Foundations. ET is supported by grants  FONCYT -PICT  ( 2019-02294 ),  CONICET-PIP  ( 11220210100800CO ), and  ANID/FONDECYT Regular  ( 1220995 ). G. D. was supported by a number of sources:  NEMESIS  project (ref. 101071900 ) funded by the  EU ERC Synergy Horizon Europe ;  AGAUR research  support grant (ref. 2021 SGR 00917 ) funded by the  Department of Research and Universities of the Generalitat of Catalunya ; project  PID2022-136216NB-I00  financed by the  MCIN /AEI /10.
explored from dimensions 5 to 12. 12 The encoder network applies a nonlinear transformation to map the values of each time pattern to Gaussian probability distributions within the latent space. Conversely, the decoder network replicates the architecture of the encoder, generating reconstructed temporal patterns by sampling from these distributions. We trained the VAE with time-by-time spatial patterns for the training set creating from the 90%/10% (training/testing) resting state HCP participants full dataset. The network training procedure consisted of batches with 128 samples and 50 training epochs using an Adam optimizer and the training consists of error backpropagation via gradient descent to minimize a loss function composed the two terms. In the last section of this work, we extended the framework by including to resting state (REST) data the recordings from 7 cognitive tasks from HCP (Social [SOC], Language [LAN], Working Memory [WM], Motor [MOT], Gambling [GAM], Emotion [EMO], Relational [REL].
8 for H 2 R  [73]  as reported in the IUPHAR database [www. guidetopharmacology. org/], although others reported pKi in the 6. 6 range  [74] ). In brief, three main labelled compounds are available to study melatonin receptors: [ 125 I-iodoMLT  [4] , the tritiated melatonin  [70]  and the MT3-targeting [ 125 I]-iodo-MCA-NAT  [75] , this last one presenting also a non-negligible affinity (IC50 in the µM range) for the MT 2 receptor  [76]. Determination of binding affinities of these compounds for different targets is straightforward. Because these compounds are all agonist and are non-specific for either melatonin receptor subtype, we aimed for alternative compounds with high affinity that might show more distinct properties for MT  1   [77, 78]. A summary of their characteristics is presented in Table  2.
Funding:  Agence Nationale de la Recherche :  ANR-19-CE16-0025-01 «mitoGPCR»  and  ANR-21-CE18-0023 «alloGLP1R» ;  ANR :  Appel RA-Covid -19  projet  MELATOVID  ( ANR-20-COV4-0001 );  Fondation pour la recherche Médicale : Equipe FRM  DEQ20130326503. La Ligue Contre le Cancer   N/Ref :  RS19/75-127  and the  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Centre National de la Recherche Scientifique (CNRS). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.