dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We presented a first comparison of two approaches for estimating IED activity and their dynamic connectivity non-invasively from MEG data, namely independent component analysis (ICA) and linearly constrained minimum-variance beamformer (LCMV-BF). We conclude, after a simulation study and a test with SEEG data recorded simultaneously in 8 epileptic patients, that the two methods are comparable in performance and complementary in identifying the sources of interictal activity. Specifically, ICA reconstructed the sources more accurately in terms of time series activity than the LCMV-BF, but it was inferior in term of localization on simulated data. On real simultaneous MEG/SEEG recordings, results showed an overall better performance of the ICA (i. e. , higher time series correlation, lower distance between the source estimated position and correlated SEEG contact).
Acknowledgements S. C. was founded by  Fondazione CRUI (GO for IT program ), Italy. This research was also supported by grants  ANR-16-CONV-0002  ( ILCB ),  ANR-17-HBPR-0005  (SCALES) from  Agence Nationale de la recherche  and, A *  MIDEX  project  ANR-17-RHUS-0004  (EPINOV) funded by the  'Investissements d'Avenir ' managed by the  French National Research Agency. This work has received support from the  French government  under the  Programme « Investissements d'Avenir », Initiative d'Excellence d'Aix-Marseille Université  via  A * Midex  funding ( AMX-19-IET-004 ), and  ANR  ( ANR-17-EURE-0029 ). It was performed on a platform member of  France Life Imaging network  (grant  ANR-11-INBS-0006 ).
PA and SB features Participants were requested to wear a tri-axial accelerometer (GENEActiv Original; Activinsights Ltd, Kimbolton, UK) on their non-dominant wrist for nine consecutive 24-h days. Accelerometer data, sampled at 85. 7 Hz and expressed relative to gravity (1 g = 9. 81 m/s 2 ), were processed using GGIR R package (version 2. 3-3). 18 uclidean Norm of raw accelerations Minus One with negative numbers rounded to zero was calculated  19  and corrected for calibration error and non-wear time. 18 Sleep periods were detected using a validated algorithm guided by a sleep log. 20 Average acceleration over 60-s epochs <40 milligravity (mg) during waking period was classified as SB, 40-99 mg as light-intensity PA (LIPA), and ≥100 mg as MVPA. 18, 21 e searched the most commonly used movement behaviour features in the literature that could be extracted from accelerometer data using the GGIR R package and identified 21 features covering six dimensions of daily movement behaviours.
All analyses were undertaken using R version 4. 1. 2 (http://www. r-project. org) with a two-sided p < 0. 05 considered statistically significant. All relevant R scripts and documentation can be accessed via the project repository (https://github. com/MathildeChen/PA-SBdimensions-mortality-Whitehall). Technical information regarding raw accelerometer data processing can be found in the GGIR repository (https://github. com/ wadpac/GGIR).
Results Among the 6308 participants in the 2012-2013 wave of data collection in Whitehall II, 4880 were invited to participate in the accelerometer sub-study, of whom 4008 had valid accelerometer data (Supplementary Figure  S1 ). Excluding those with missing covariates led to an analytical sample of 3991 (mean age: 69. 4 years, 25. 8% women). Over a mean follow-up of 8. 1 (standard deviation [SD] = 1. 3) years, 410 deaths were recorded. Deceased participants were more likely to be older, married/cohabitating, less educated, have a worse cardiovascular risk profile, more comorbidities, and more functional limitations compared to those who survived over the follow-up (Table  1 ). Participants who died spent more time in SB, which was accumulated in longer, uninterrupted episodes, and less time in higher activity intensities compared to surviving participants.
Overall activity  14, 15  and active to sedentary transition probability,  15, 16  a marker of the fragmentation of activity bouts as mean duration of activity bouts in the present study, were selected in two different studies, but heterogeneity between studies in included features and in ranking methodologies makes further comparisons difficult. None of these studies examined the external validity of findings, as recommended for studies using a machine learning approach,  28  limiting the generalisability of the findings. We extend previous studies by developing and validating a PA and SB composite score for mortality risk using a machine learning approach adapted to correlated data that simultaneously performs variable selection and dimension reduction. Dimensions investigated in previous studies were included as well as two additional dimensions (frequency and intensity distribution).
These features relate to dimensions of movement behaviour that go beyond total duration in MVPA or SB. Whether interventions to modify these features among older adults are easier to implement and overcome existing individual  34, 35  or environmental  36  barriers warrants further research. Contributors SS conceived the idea for the study. MC, MAB, VvH, BL, and SS designed the study methodology. MC, MAB, BL, and SS did the investigation. MC, MY, VvH, MAB, PMV, and SS accessed and validated the dataset. MC, MAB, and VvH did the formal data analysis. MC, VvH, PMV, and SS curated the data. MC, BL, and SS prepared the first draft of the manuscript. All authors reviewed and edited the manuscript. MC and MAB were responsible for the figures. MC is the guarantor. SS and MAB supervised the study.
Acknowledgments The authors thank all participating civil service departments and their welfare, personnel, and establishment officers; the  British Occupational Health  and Safety Agency; the  British Council of Civil Service Unions ; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team. The Whitehall II Study team comprised research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants, and data entry staff who made the study possible. These individuals and organizations were not compensated for this work. The authors thank CoLaus study participants and team members. The  Whitehall II study  was and is supported by the  US National Institutes of Health National Institute on Aging  (grants  R01AG056477  and  RF1AG062553 ), the  UK Medical Research Council  (grants  R024227 ,  S011676 , and  K013351 ), the  British Heart Foundation  (grant  RG/16/11/32334 ), and the  Wellcome Trust  (grant  221854/Z/20/ Z ).
The CoLaus study was and is supported by research grants from  GlaxoSmithKline , the  Faculty of Biology and Medicine of Lausanne , and the  Swiss National Science Foundation  (grants  3200B0-105993 ,  3200B0-118308 ,  33CSCO-122661 ,  33CS30-139468 , and  33CS30-148401 ). Dr Chen ,  Dr Yerramalla , and  Dr Sabia  were supported by the  French National Research Agency  (grant  ANR-19-CE36-0004-01 ). Mikaela Bloomberg  is supported by the  Economic and Social Research Council  ( ES/ P000592/1 , https://esrc. ukri. org/).
Data sharing statement Data, protocols, and other metadata of the Whitehall II study are available to the scientific community via the Whitehall II study data sharing portal (https://www. ucl. ac. uk/epidemiology-health-care/research/ epidemiology-and-public-health/research/whitehall-ii/data-sharing). Information related to CoLaus data access is available to qualified, interested researchers at https://www. colaus-psycolaus. ch/professionals/ how-to-collaborate/. All responses to data sharing requests must comply with the ethical and legal constraints of Switzerland.
The angle we adopt is that of online or incremental learning, which refers to approaches that handle data in a sequential manner resulting in more efficient solutions in terms of memory usage and overall energy consumption. For UAD, we propose to investigate mixtures of probability distributions whose interpretability and versatility have been widely recognized for a variety of data and tasks, while not requiring excessive design effort or tuning. In particular, the use of multivariate Gaussian or generalized Student mixtures has been already demonstrated in many anomaly detection tasks, see  [1, 26, 31]  and references therein or  [21]  for a more general recent review. However, in their standard batch setting, mixtures are difficult to use with huge datasets due to the dramatic increase of time and memory consumption required by their estimation traditionally performed with an Expectation-Maximization (EM) algorithm  [25].
The anomalies detected by our method are consistent with the Hoehn and Yahr (HY) scale  [16] , which describes how the symptoms of Parkinson's disease progress. The results provide additional interesting clinical insights by pointing out the most impacted subcortical structures at both HY stages 1 and 2. The use of such an external scale appears to be an original and relevant indi-rect validation, in the absence of ground truth at the voxel level. Energy and memory consumptions are also reported for batch and online EM to confirm the interesting performance/cost trade-off achieved. The code is available at https://github. com/geoffroyO/onlineEM.
All voxels v whose proximity r(y v ; Θ H ) is below τ α are then labeled as abnormal. In practice, while f H is known explicitly, the probability distribution of r(Y; Θ H ) is not. However, it is easy to simulate this distribution or to estimate τ α as an empirical α-quantile  [1]. Unfortunately, learning f H on huge datasets may not be possible due to the dramatic increase in time, memory and energy required by the EM algorithm. This issue often arises in medical imaging with the increased availability of multiple 3D modalities as well as the emergence of image-derived parametric maps such as radiomics  [14]  that should be analysed jointly, at the voxel level, and for a large number of subjects. A possible solution consists of employing powerful computers with graphics cards or grid-architectures in cloud computing.
Brain abnormality exploration in de novo PD patients Data description and preprocessing. The Parkinson's Progression Markers Initiative (PPMI)  [24]  is an open-access database dedicated to PD. It includes MR images of de novo PD patients, as well as of healthy subjects (HC), all acquired on the same 3T Siemens Trio Tim scanner. For our illustration, we use 108 HC and 419 PD samples, each composed of a 3D T1-weighted image (T1w), Fractional Anisotropy (FA) and Mean Diffusivity (MD) volumes. The two latter are extracted from diffusion imaging using the DiPy package  [13] , registered onto T1w and interpolated to the same spatial resolution with SPM12. Standard T1w preprocessing steps, comprising non-local mean denoising, skull stripping and tissue segmentation are also performed with SPM12.
The experiments were run on a CPU with Intel Cascade Lake 6248@2. 5GHz (20 cores), and a GPU Nvidia V100-32GB. Online mixtures exhibit significantly lower energy consumption, both for training and inference. In terms of memory cost, DRAM peak results, as measured by the tracemalloc Python library, also show lower costs for online mixtures, which by design deal with batches of voxels of smaller sizes than the batches of patches used in AE solutions. These results highlight the advantage of online mixtures, which compared to other hardware-demanding methods, can be run on a minimal configuration while maintaining good performance.
Conclusion and perspectives Despite a challenging medical problematic of PD progression at early stages, we have observed that energy and memory efficient methods could yield interesting and comparable results with other studies performed on the same database  [34, 28]  and with similar MR modalities  [36, 8, 26, 27 ]. An interesting future work would be to investigate the possibility to use more structured observations, such as patch-based features  [28]  or latent representations from a preliminary pretext task, provided the task cost is reasonable. Overall, we have illustrated that the constraints of Green AI  [35]  could be considered in medical imaging by producing innovative results without increasing computational cost or even reducing it. We have investigated statistical mixture models for an UAD task and shown that could account for multivariate reference models, and their much simpler structure made them more amenable to efficient learning than most ANN solutions.
Search strategy The literature search aimed at sourcing all scientific studies evaluating the accuracy of detector dogs to screen people directly or through derived biological samples for SARS-CoV-2 infection. The Population, Intervention, Control, Outcome (PICO) framework was used to specify the review question. The following inclusion criteria were considered for evaluation: Studies that did not meet the inclusion criteria were excluded, as were duplicates and preprints that were subsequently published in a peer-reviewed journal and, therefore, were considered obsolete. For the literature search, electronic search engines were used on databases such as PubMed (www. ncbi. nlm. nih. gov/PubMed), Web of Science (www. webofscience. com), CAB Abstracts (www. cabdirect. org), and Google Scholar (www.
scholar. google. com) performed by one author (SM) from the current study. The following search terms were used in all mentioned electronic databases: ("canine detection" OR "dog detection" OR "detection dog" OR "detection dogs" OR "sniffer dog" OR "sniffer dogs" OR "scent dog" OR "scent dogs" OR "trained dog" OR "trained dogs" OR "tracking dog" OR "tracking dogs") AND (SARS-CoV-2 OR COVID-19). Manual searching for relevant data and papers was also performed, for example, via screening of reference lists of published literature and via correspondence with co-authors and expert groups in the field. All items retrieved from the search engines and manual searches were recorded and entered into the screening process. The authors have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for diagnostic test accuracy during the development of the systematic review (www.
Study selection The screening process was limited to studies published since 2019. A two-stage process of screening was applied: STAGE 1-studies of relevance to the systematic review question were identified, which fulfilled the inclusion criteria i) and ii) and reported original findings related to scent dog detection of COVID-19-associated smell. At this stage, only the titles and abstracts of the studies were screened. Duplicates have been excluded at this stage. Titles and abstracts in non-English languages have been verified by a person who speaks the respective language and/or by using translation software (DeepL Translator and Google Translate). STAGE 2-studies providing details for evaluation of applied methodology and robustness of outcome measures related to the review question were identified.
Assessment of quality of outcome measures The following result metrics were extracted from the publications: SEN, SPE, and ACC (number of true positive and negative canine indications/number of all indications). If those metrics were only partially reported, missing metrics were calculated from the provided data. In a first attempt, contingency tables were extracted per dog involved in each DTE, and the median, interquartile range, and overall range of the mentioned metrics were calculated or adopted among dogs for each DTE. If it was not possible to resolve the data by individual dogs (e. g. , due to a single general contingency table reported or due to the definition of sample status by screening of more than one dog), overall metrics were adopted or calculated from the common contingency table per DTE.
Search results The literature search for corresponding original studies published between 2019 and 2022 yielded 13 records on CAB Abstracts, 840 records on Google Scholar, 41 records on PubMed, and 33 records on Web of Science. Nine records were considered for analysis from the manual search of reference lists and correspondence. In total, after excluding the non-eligible records, duplicates, and obsolete preprints or manuscripts that were later published in peer-reviewed journals, 27 reports with the respective inclusion criteria were found and entered in the final analysis. Those included two current preprints (Fig. 1 ). Studies are from 15 countries and differ in their designs. Ten DTEs of a total of 59 DTEs among the 27 studies were conducted as cross-sectional test evaluations  [39] [40] [41] [42] [43] [44] [45].
In addition, information about sample storage is versatile; for example, data on the duration of SARS-CoV-2 sample storage that allows the preservation of VOCs are not yet available. This highlights the need for optimization of the storage of biological samples for canine scent detection work, with the aim to establish robust training sample sets. Kantele et al. stated that they used storage time of sweat samples of 0-5 months for the validation trial (first DTE) with results of overall SEN of 92% and SPE of 91%  [43]. One approach for enhancing standardization processes is to use animal models of SARS-CoV-2  [91]  for sample generation, supernatants of cell cultures  [57] , or even viral proteins, which is currently under evaluation. However, aspects of adequate levels of generalization and discrimination are crucial here in the context of effective pandemic containment (e.
However, while in bacteria mRNAs from operons remain polycistronic, in C. elegans they are processed into monocistronic units in a similar fashion as to how trans-splicing and polyadenylation are coupled in trypanosomes  12. Early global analyses of trans-splicing estimated that 70% of C. elegans messengers are trans spliced  9, 13. A meta-analysis of RNA-seq datasets amended this number to 85% and confirmed a trend that the least expressed genes had the most propensity for not being trans-spliced  14. This was suggestive of a lack of detection of trans-splicing for rare mRNAs rather than a distinct subset of genes bypassing the generic mRNA maturation process. This prompted us to hypothesise that the actual fraction of trans-spliced genes could be higher still. To test this hypothesis we decided to apply a different RNA-seq method to see wether it could improve our characterization of 5' extremities.
Data availability The direct-cDNA datasets used in this study have been deposited in the Sequence Read Archive (SRA) under the following accession code: PRJNA822363. and listed in the Supplementary Table  3. The dataset table, generated by processing all of the obtained alignment files, and used in all computational analysis has been deposited on Figshare, along with all the other files generated as part of our analysis (DOI: 10. 6084/m9. figshare. 19131260). A companion web-app, allowing the reader to view our results regarding any of the genes found in our sequencing experiments is directly available here: https://share. streamlit. io/florianbrnrd/ elegans-trans-splicing/main/app/streamlit_app. py.
Results and Discussion Using data from two surveys at the two upstream levels of the value chain (farmers and co-operatives), we calculated the relative importance of internal and external barriers and levers for adopting the intercropping systems. Our results mainly show that barriers of high importance for farmers are internal and external to the farm, while those of importance for the co-operatives are only internal to the organization (storing and storage of the mix) (Section 4. 1). Regarding levers, farmers and co-operatives converge on the importance of the same external levers (Section 4. 2). This finding provides a sound basis for further studies to test the robustness of the farms and farmers typology. A limitation of this study is the availability of precise sociodemographics variables of the farmers and co-operatives that that would have allowed multivariate analyses.
3. The methodology is further specified in Section 2. 6. -Finally, the LRF as a positive radiative feedback in the Arctic locally enhances the Greenhouse effect. Therefore, we relate its strength to changes in the long-wave radiation budget at the top of the atmosphere (TOA). Again, we consider the area north of 66°N to derive Pan-Arctic averages. Details are described in Section 2. 7. -In an outlook perspective, to augment the observational data sets derived during MOSAiC, we further conducted daily Large-Eddy Simulations (LES) for the whole MOSAiC drift (Section 2. 8). These simulations aim to discuss processes at turbulence-and cloud-resolving scale, as they are largely underrepresented in the literature covering the Arctic LRF.
In addition, using the T2m tower data reduces the impact of the ship on the near-surface temperature. We derive the inversion strength as the difference between the temperature-profile maximum (Tmax; between the surface and 250 hPa) and T2m. The temporal resolution for the inversion data follows the frequency of radiosonde launches during the MOSAiC expedition (approximately every 6 hours). For the model-to-observation comparison, we consider 6-hourly temperature diagnostics for the period 2010-2014 that were co-located to MOSAiC in space and time. Since the climate models are free-running coupled models, it is not essential to use the exact years of 2019 / 2020 (assuming that 2010-2014 and 2019-2020 are roughly the same climate). Instead, the correct time (i. e. , time of day and season), and spatial location are co-located.
The model output data is chosen corresponding to which time step and grid-box midpoint is closest to each individual MOSAiC radiosonde launch time. Essentially, the model data "follow" the MOSAiC track in space and time of the year. We equally derive the temperature inversion in the model data as the difference between Tmax and T2m. Note that there are no inversion data available at MOSAiC between 9 May 2020 to 10 June 2020, and 29 July 2020 to 25 August 2020 when the ship was in transit through the sea ice. Figure  3  shows the entire drift of RV Polarstern with time attribution according to the radiosonde launches as color coding. The black dots following the drift depict the locations where observational data was available for our study (limited by the availability of T2m tower data). Once CMIP6 model output and NSA radiosonde data are processed to be comparable, we estimate the inversion strength as in the MOSAiC comparison, as the difference between Tmax and T2m, and at 6-hourly time resolution.
We follow the approach described in  Crasemann et al. (2017)  and determine the circulation regimes in a reduced state space spanned by the five leading empirical orthogonal functions (EOFs;  Dawson and Palmer, 2015). The coordinates in this state space are provided by the corresponding unnormalized Principal Component (PC) time series. For the subsequent clustering, the number of clusters k has been set to k = 5 following  Crasemann et al. (2017). For the analysis of the CMIP6 data we apply a projection approach described in  Fabiano et al. (2021)  where the state space spanned by the ERA5-EOFs serves as the reference state space for the CMIP6 simulations. The coordinates for each simulation are provided by projecting the SLP anomaly data onto the reference state space, obtaining five Pseudo-PCs, for each model simulation.