dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods A formal literature review was conducted on PubMed between September 2021 and January 2022 in accordance with the PRISMA 2020 guidelines. Two reviewers (F. C. and P. B. ) conducted the search and data extraction inde-pendently. The search term "spectroscopy" and "spectroscopic" was paired individually with the following keywords: "amyotrophic lateral sclerosis", "motor neuron disease", "PLS", "SBMA", "Kennedy's disease", "PMA", "SMA", "ALS-FTD" (Fig. 2 ). Selection criteria included original research articles pertaining to spectroscopy brain studies, published during the past three decades. Only human studies were reviewed. Only articles in English were selected for review. Opinion pieces, previous review articles, case reports, research studies without imaging were not reviewed. The reference list of identified papers were also reviewed for articles that might have been missed.
We developed an MS EXCEL-type database including specific fields based on which we comprehensively and quantitatively reviewed all studies. The following fields/variables were included for data extraction: first author; year of pub-lication; study sample size; patients' sample size; healthy controls' sample size; patients' and healthy controls' age (mean ± SD); study design (cross-sectional; longitudinal); type of longitudinal study; MRI field strength; MRS tech-nique; MRS acquisition method (single voxel, multi-voxel, whole-brain); MRS target region; MRS processing soft-ware; MRS metrics (absolute values, ratios, institutional units); supporting imaging data; available clinical data (El Escorial diagnostic criteria; ALSFRS/ALSFRS-R; disease duration; progression rate; onset type; treatment with rilu-zole; respiratory metrics; motor tapping rate; neuropsychological measures); supporting genetic data; supporting postmortem data; MRS findings; correlations between MRS findings and clinical data.
Field Strength, Acquisition (Single-Voxel, Multi-Voxel, Whole-Brain) and Post-Processing Techniques Forty-five studies use 1. 5 T (four are published between 2011-2016;  [61, 84, 106] ), one study uses 2 T  [62] , thirty-seven studies use 3 T (among which one is published before 2000;  [133] ), one study uses 4 T  [120] , one study uses 3 T and 7 T  [102]  and two studies use 7 T  [121, 122]. Forty-nine studies apply a single-voxel technique, 23 studies apply a multi-voxel technique, one study uses both single-voxel and multi-voxel techniques while four studies apply whole-brain spectroscopy. Two studies use diffusion tensor spectroscopy  [100]  and diffusion weighted spectroscopy  [93]  while one study uses cranial phosphorus-31 MRS (31P-MRS;  [125] ) instead of 1 H-MRS. With regards to data analysis software, 14 studies do not provide specific details, 24 studies use the LCModel, five studies use the MI-DAS, three studies use the jMRUI, one study uses the TAR-QUIN, and 38 studies use either in-house or manufacturerprovided software.
Conclusions MRS remains one of the most promising MR-based imaging modalities in MND, which offers invaluable metabolic data non-invasively. Existing studies have already cemented the academic role of MRS by characterising neurodegenerative processes in MND. An urgent priority of future studies is to define, gauge, and establish the clinical role MRS in expediting the diagnosis, monitoring disease progression and assessing response to therapy. tive Disease Research (JPND), the Andrew Lydon scholarship, and the Iris O'Brien Foundation.
We use functional Magnetic Resonance Imaging (fMRI) to explore synchronized neural responses between observers of audiovisual presentation of a string quartet performance during free viewing. Audio presentation was accompanied by visual presentation of the string quartet as stick figures observed from a static viewpoint. Brain data from 18 musical novices were obtained during audiovisual presentation of a 116 s performance of the allegro of String Quartet, No. 14 in D minor by Schubert played by the 'Quartetto di Cremona. ' These data were analyzed using intersubject correlation (ISC). Results showed extensive ISC in auditory and visual areas as well as parietal cortex, frontal cortex and subcortical areas including the medial geniculate and basal ganglia (putamen). These results from a single fixed viewpoint of multiple musicians are greater than previous reports of ISC from unstructured group activity but are broadly consistent with related research that used ISC to explore listening to music or watching solo dance.
A significant challenge to studying brain mechanisms of audiovisual perception of a string quartet is how to design the acquisition and analysis of brain data that preserves the intrinsic properties of the string quartet performance. Our solution to this was to use the technique of Intersubject Correlation (ISC) which enables examination of brain activity to complex stimuli extended over minutes  (Hasson et al. , 2004). ISC is a model-free approach that measures how, among a set of observers, activity at different regions of the brain is correlated among the observers. Evidence has emerged showing the role of correlated activity amongst observers, on a neural, behavioral and physiological level, in shared experience of a musical performance (e. g. ,  Ardizzi et al. , 2020; Hou et al. , 2020).
Stick figures were chosen over natural video since the stick figures allow us to control the visual salience of the players so there would not be a preference to view any particular player based on surface appearance. While being scanned participants had no task and were told simply to enjoy the performance. The data were analyzed using ISC to reveal which brain areas had correlated activity among the group of observers. They were also examined to see whether a relationship could be found between brain activity and physical measures of the sound and vision of the string quartet. Brain regions revealing ISC amongst observers indicate how brain activity becomes time-locked to external stimuli  (Hasson et al. , 2004) , with sensory regions typically showing activity synchronized to audio  (Abrams et al.
Participants The fMRI data were collected from 18 healthy participants (eight males) with a mean age of 26 years. All subjects had normal hearing and normal or corrected to normal vision. All participants were right-handed, as assessed by the Edinburgh handedness inventory  (Oldfield, 1971). Recruitment information included criteria for non-expert or novice musical listeners. Participants either received a number of course credits, or a small cash payment for their participation. Ethical approval for this study was obtained from the College of Science and Engineering ethics committee, at the University of Glasgow.
Another section used a fugato style, where there is no leading part as such. Moreover, the piece is a staple of the quartet's repertoire and thus could be played consistently at a high quality. The recording has been used in previous research of  Glowinski (2013)  and  Glowinski et al. (2014)  and is described in more detail by  Badino et al. (2014). Recording The upper body stick figure display of the string quartet the 'Quartetto di Cremona'. was done by the InfoMus research group at the University of Genoa and took place in a 250-seat auditorium that provided an ecologically valid location for the performance. Audio and visual information were synchronized using EyesWeb XMI software  (Camurri et al. , 2005; Glowinski, 2013).
Auditory analysis of the string quartet Auditory analysis was performed using custom-made Matlab scripts. Three different features were extracted from the audio of the stimulus, for the feature analysis: Loudness (measured in sones), SCG (measured in Equivalent Rectangular Bandwidth rate units) and Ap0 (measured in dB). Loudness and SCG were derived from time-varying specific loudness of the sound signals that were computed using the model of  Glasberg and Moore (2002). Ap0 was computed using the YIN model of De  Cheveigné and Kawahara (2002). For each feature the median statistics were obtained for each 2 s segment of the 116 s performance. This provided 58 values for each feature that corresponded to the number of functional brain volumes obtained. However, only the first 57 values were used due to concerns of accumulated timing errors leading to a mismatch in the synchronization between the scanner volumes and the movie file that could lead to artifactual computations in the final volume.
Visual analysis of the string quartet Visual analysis was performed using custom-made scripts in Matlab. A visual analysis of the kinematic features of the string quartet focused on analysis of the speed of the different player's motions as well as the similarity of the motion of the different players. Given the complex motion of each player there are many possibilities of what motion feature to examine. We chose to focus on the three-dimensional motion of the bow of each player to characterize movement. The bow is the endeffector of a complex kinematic chain and has been useful to characterize emotional arm movements  (Pollick et al. , 2001)  as well as musical conducting movements  (Luck and Sloboda, 2009; Wöllner and Deconinck, 2013). To perform this, we took the original 100 Hz data sample and first calculated the average x-y-z position of the two markers on the bow for each player.
We then filtered these data with a 6th order dual-pass Butterworth filter with a cutoff frequency of 10 Hz to remove high frequency noise from the data. These filtered data were then differentiated to find the three-dimensional velocity vector. From this velocity vector, we calculated the scalar value of speed, corresponding to the rate at which a bow was moving through three-dimensional space. This calculation of speed was done for each bow. Because the sampling rate for the fMRI was one sample every 2 s, we averaged the speed over 2 s epochs to find a total of 58 average speed values to represent a visual speed signal that could be compared to brain activity. We were also interested in whether the similarity of the four players might be related to brain activity, and to examine this we performed a measure of movement similarity based on a calculation of the average correlation of speed between players for each of the 58 epochs calculated.
Procedure The string quartet stimulus was presented in a single run using the software Presentation (Neurobehavioral systems, Inc. ). The video of the string quartet performance was preceded and followed by 20 s of a blank screen, resulting in a run consisting of 20 s of blank screen, followed by 116 s of stimuli and ending on 20 s of blank screen. The scanning session included acquisition of fMRI data for other experiments not related to this study and this experiment came second in the run order after the first experiment of approximately 5 min. At the completion of all functional data experimental runs an anatomical scan was performed. The video was projected using an LCD projector on a translucent screen, while participants watched them in an angled mirror in the scanner. The soundtrack of the video was presented using in-ear headphones (model S14 by Sensimetrics, Malden, United States).
Data acquisition and preprocessing Data were acquired using a Siemens 3 Tesla TIM Trio magnetic resonance imaging scanner (Erlangen, Germany). Functional scans were taken to obtain brain volumes with 32 sagittal slices covering the brain volume, with 3 mm × 3 mm × 3 mm voxel size (Echo Planar 2D imaging PACE; TR = 2,000 ms; TE = 30 ms; imaging matrix of 70 × 70). The 116 s stimulus was contained in 58 volumes and was preceded and followed by 10 volumes for a total of 78 volumes. At the end of the functional scans, high resolution anatomical T1 weighted 3D magnetization prepared rapid acquisition gradient echo (MP-RAGE) images were acquired, with 192 sagittal slices, a 256 × 265 matrix size and a 1 mm × 1 mm × 1mm voxel size and a TR of 1,900 ms. The data files were pre-processed using Brainvoyager QX V2.
8 (Brain Innovation B. V. , Maastricht, Netherlands). The functional images underwent slice scan time correction, using cubic spline interpolation. 3D Motion correction was then also applied using trilinear detection and sinc interpolation. This was followed by normalization of functional scans into the common Talairach space  (Talairach and Tournoux, 1988) , and co-registration of functional and anatomical data. Spatial smoothing was then obtained using a Gaussian filter with a full width and half maximum (FWHM) of 6. 00 mm. Finally, the functional data were trimmed to contain only the 58 volumes during the string quartet performance and these data were converted into MATLAB (The Mathworks, Natick, MA, United States) based mat files for ISC analysis.
clinicaltrials. gov/ct2/show/ NCT04263948). Our results stress the need for further longitudinal telemonitoring epidemiologic studies to firmly establish chest rest-activity and temperature rhythm as early warning signals and measurable surrogates of disease risk at subclinical stages. Such indicators could be used to design medically sound interventions for adapting work schedules to individual workers in a new era of personalized prevention medicine. workers. Thus, poor health can result from circadian and sleep disruption, due to frequent changes in the environmental light and other cycles which synchronize the circadian timing system (CTS). 1, 9 In health, the CTS rhythmically regulates behaviour and physiology, as well as hormonal secretions, metabolism, proliferation, DNA repair, and apoptosis over the 24-hours.
Actual work and rest schedules were captured from daily self-reported diaries. A "workday" was defined as 24-hour timespan starting at 00:00 for DS and at the time when night shift work began (20:00/21:00) for NS, the remaining timespan defined as "free day". During a 7-day study period, they were telemonitored by an anterior chest-worn thoracic sensor (Movisens, Karlsruhe, Germany) as part of the PiCaDo mobile e-Health platform (supplementary Figure  S1 ). 30 Per-minute accelerations measuring physical activity (PA) and chest surface temperature (Chesttemp) were measured and teletransmitted daily. Using PA data collected by wearable sensors to monitor circadian rhythm and sleep pattern has been adopted as a study protocol outside laboratory settings. 35À37 Details on the actual activity such as walking, running, or sitting are not visible in the current telemetric setting.
All parameters and indicators are estimated from the fitted HHMM or 2-HHMM (see Supplementary Material, SM-2). Cluster analysis. Health professionals allocated to the same shift type were further classified into subgroups that shared common rest patterns by applying cluster analysis to the average daily 'rest profiles' (see the List of parameters and indicators). The rest profiles for work and free days were concatenated into a vector which thus contained 576 elements for each participant, i. e. 288 entries for each rest profile evaluated at 5-minute intervals from 20:00 to 19:59 with data extracted from the averages of either just work or just free days. Cluster analysis provides an exploratory method to extract the most common patterns by identifying subgroups of subjects with maximal similarity within subgroups and maximal difference between subgroups.
Discussion Longitudinal telemonitored data of high quality were gathered from 140 health professionals working as DS (77 individuals) or NS (63 individuals). The distribution of the circadian parameters in rest-activity and chest surface temperature of the DS in Circadiem was consistent with earlier results from telemonitoring "dayactive" people using the same system,  30, 48, 49  which further highlights the significance of the alterations found in the NS. Indeed, NS experienced significantly worse estimated rest quality (p1-1) and circadian activity rhythm (RI) and a delayed centre time of rest, although they achieved similar rest duration by taking more frequent and longer naps. This finding is consistent with prior studies 50 using diaries or wrist actigraphy. Both NS and DS extended rest duration on free days to compensate for the loss of sleep on workdays (indicated by increased rest amount on free days).
Large-scale epidemiological studies would further assess the relevance of individual circadian disruption exposure to risks of cancers and cardiovascular diseases. Toward this goal, our technology has recently been automatized for facilitating real-time decision making in a clinical trial involving the telemonitoring of circadian and sleep cycles in pancreatic cancer patients (Clin-icalTrials. gov Identifier: NCT04263948). This trial is also paving the way for ethical and GPDR considerations when applying telemonitoring in field studies. These include participants' understanding of the technology system and expected outputs, as well as their engagement and training as a user. The deidentification of time series records and automated analyses is needed to protect privacy, with encoding only known to a physician-investigator.
Contributors PG, FL, EC, DL and BF designed the collection of data from diaries and questionnaires, and FL, BFF, QH, SK, and AMA designed the data collection for the PiCADo platform. PG, FL, RA, GA and AMA organised the logistics for recruiting and telemonitoring health professionals volunteers. EC and PG organised and maintained the data base and advised on the use of the explanatory variables. DL and BF performed sleep debt analyses and advised on the interpretation of sleep scores. JAB, BFF, FL conceived the current project, designed the analysis pipeline for this work and supervised YZ in carrying out the analysis and synthesising the statistical results. YZ, JAB, BFF, FL interpreted this study and wrote the manuscript. SK advised on aspects of study design, and, together with AMA and QH, performed data collection and curation, and preliminary analyses of the PiCADo time series.
YZ, EC, SK, QH, JAB, BFF and FL verified the underlying data. All authors contributed to the article and approved the submitted version. Research Council, London, United Kingdom (Award Number: Grant MR/M013170). FL is also supported by CRUK (Grant C53561/A19933). The statistical analyses were carried out as part of the doctoral dissertation of YZ, supervised by JAB and BFF and funded by the Chancellor's International Scholarship and the Department of Statistics, University of Warwick. We would like to gratefully acknowledge meeting and travel support by the Warwick Network on "E-health applications for physical activity, sleep and circadian rhythms" and the Alan Turing Institute.
Acknowledgements The authors are grateful to all the health care professionals for their strong engagement with the study and in particular to  Mrs Nicole Gastiaburu , senior health officer, for her great initiative in promoting the project across the different departments at  Paul Brousse Hospital. The Circadiem study was funded by the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES)  (Grant  EST-2014/1/064 ). QH, FL, and  BFF  were supported by the Medical.
Where the SEEG electrode traversed a sulcus (e. g. collateral sulcus) we did not use pairs of contacts located on both sides of the sulcus. We paired the contact in the grey matter on one side (eg. parahippocampal side for the collateral sulcus) with the adjacent white matter contact in the same structure and assigned the pair to that ROI (e. g. parahippocampus). LF-CES was applied 48 h after implantation and at least two hours after any seizure. Data from electrophysiological signals were processed with Matlab (R2018a, the MathWorks). For a detailed description of the procedure, please refer to Krieg et al. [27]. Briefly, we primarily detected the electrical pulse artefact with an amplitude threshold of 2000 mV. We segmented the signal from -0. 5 to 0. 5 s relative to the onset of the pulse defined as time zero.
Data was filtered with an Infinite Impulse Response (IIR, transition bandwidth 0. 2 Hz, order 6) high-pass filter with 0. 5 Hz cut off frequency which uses reverse filtering to prevent phase distortion of the signal. We estimated probable residuals of the electrical stimulation artifact with an amplitude superior to 10 mV through trials until 14 ms post-stimulation onset. Therefore, we choose our interval of interest from 15 to 300 ms poststimulation in latency, and from 10 mV to 2000 mV in amplitude. Mean CCEP peak detection was performed by first selecting time points whose absolute value was higher than two preceding and two succeeding time points. Then, the validity of detected peaks, in terms of energy, was controlled with the presence of a simultaneous peak in the root mean square of the CCEP computed across trials.
Data analyses Deviation scores: Performance in the enumeration task was defined as the number of lines presented subtracted from the number of lines reported ("deviation"). Hence if the number of lines reported was the same as the number of lines presented, the deviation was zero; reporting more lines than presented yielded deviation scores above zero, and reporting fewer lines than presented yielded deviation scores below zero. Mean deviation scores were analyzed by a generalized linear mixed-effects model specifying the number of lines presented, and spatial arrangement as fixed effects and subject as a random factor using the glmmTMB package  (Brooks et al. , 2017). For model selection, null models (without the fixed effects) and full models (with the fixed effects) were fitted and hierarchically compared.
Similar incremental model building was used to select the minimum degree polynomial that fitted the data. Likelihood-ratio tests with Satterthwaite's approximation for the degrees of freedom were performed for model comparisons, and the Akaike information criterion was used to select the best fitting model  (Matuschek et al. , 2017). Confidence intervals were calculated with the ggpredict function of the ggeffects package  (Lüdecke, 2018). The pseudo R squared statistic (R 2 ) was computed to quantify how well the fixed and random factors explained the performance using r. squaredGLMM() function of the MuMIn package  (Barton & Barton, 2015; Johnson, 2014). Assumptions underlying the models were checked with diagnostic plots of residuals using the DHARMa package  (Hartig, 2017). A second-degree polynomial regression was used to fit the deviation scores on the number of lines presented.
The random effect structure included random slopes and random intercepts for each subject. The full model was selected (R 2 = 0. 43). Confidence ratings: Mean confidence ratings were analyzed by a generalized linear mixed-effects model specifying the number of lines presented, and spatial arrangement as fixed effects and subject as a random factor using the glmmTMB package  (Brooks et al. , 2017). Model selection and checking of the assumptions were the same as in the analysis of the deviation scores. The full model was selected (R 2 = 0. 37). Response speeds: The inverse transformations of reaction times (1/RT; response speed) were analyzed by a generalized linear mixed-effects model specifying the deviation scores, the number of lines presented, and the spatial arrangements as fixed effects, and subject as a random factor using the glmmTMB package  (Brooks et al.