dataesr/scientific-paragraphs-categorization huggingface dataset:
org/10. 23708/XXXXX. The AutoMitoG pipeline, codes and scripts used in this study are available via github (https://github. com/jorgeamaya/automatic_genome_assembly and https://github. com/jorgeamaya/malaria_mitogenome). Table  2. Analysis of Molecular Variance (AMOVA) describing the variance partitioning at three hierarchical levels: between species, between populations within species, and within populations.
Acknowledgment We would like to acknowledge the Anopheles gambiae 1000 genomes consortium (https://www. malariagen. net/projects/ag1000g#people), and especially  Nick Harding ,  Mara K. N. Lawniczak ,  Martine Donnelly ,  Dominic P. Kwiatkowski  (deceased),  Carlo Costantini ,  Nora J. Besansky , and  Frédéric Labbé  for providing resources, supports, and help during the development of this study. We also thank the  Center for Information Technology of the University of Groningen  for their support and for providing access to the Peregrine high-performance computing cluster. We also wish to acknowledge the ISO 9001 certified  IRD i-Trop HPC  (member of the  South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper (https://bioinfo.
Data availability Short-read data used in this study to assemble mitogenomes come from two consortium projects: the MalariaGEN Anopheles gambiae 1000 genomes projects phase-2 AR1 data release (https://www. malariagen. net/data/ag1000g-phase-2-ar1; see also The Ag1000G Consortium (2020)); and the Anopheles 16 genomes project  (Fontaine, et al. 2015;  Neafsey, et al. 2015)  (National Center for Biotechnology Information, NIH, BioProject IDs: PRJNA67511 and PRJNA254046). Mitochondrial genome sequences have been deposited on NCBI and the mitogenome alignment on the IRD DataSud repository:.
Controller The documentation for building the controller is provided in the Appendix of this article and can also be found at https://controller-doc. readthedocs. io/en/latest/. It contains a Bill of Materials (BOM) of the commercial parts necessary to build the controller, detailed assembly instructions, source code for the microcontroller, and a download section for the 3D models and Gerber files. The 3D model files are available either as. STL files, which can be readily sent to a 3D printer, or as. STEP files for manipulation and modification using Computer-Aided Design (CAD) software. Gerber files describe the different layers of the Printed Circuit Board (PCB) that need to be sent to a manufacturer for production. The buttons layout of the controller is presented in Fig  1. Design choices.
Our pilot controller featured a 10m long fiber optics cable, which was sufficient to connect the controller in the MRI room with the PCB in the control room. Software. A Teensy 3. 5 (Teensy 1 ) microcontroller board was programmed to parse the optical signals and manage communication between the PCB and a stimulation computer. Programming was done in the Arduino Integrated Development Environment (IDE; Arduino) using the Teensyduino add-on (https://www. pjrc. com/teensy/teensyduino. html), which allows programming Teensy family boards using most of the Arduino libraries. This software must be uploaded to the Teensy board located atop the PCB.
Task design The datasets used in this study come from the Courtois NeuroMod databank (https://www. cneuromod. ca/gallery/datasets/). For the purpose of this project, we only utilized data from 4 subjects of 6 participants available in the databank, specifically the 4 subjects that performed the gamepad task. The code used for data analysis is available at https://doi. org/10. 5281/ zenodo. 7847577 (or on GitHub https://github. com/courtois-neuromod/controller_ validation). Subjects. Four participants (2 female) were scanned while they performed a variety of tasks in the MEG and MRI scanners. All four subjects were right-handed and had normal hearing for their age, ages ranged between 31 and 47 at first data set acquisitions in 2018 (i. e. hcptrt dataset), and the timespan between first and last dataset acquisition (i.
e. gamepad dataset, see Control datasets section) was approximately 2 years and 7 months. Subjects were recruited as part of the Courtois NeuroMod project (CNeuroMod), a dense fMRI database that is being collected on 6 subjects using both naturalistic and controlled stimuli from a broad range of cognitive domains and the main motivator for building the controller. Although the participants of the gamepad task do not properly reflect the population of videogame players, we expected their behavior to be illustrative of the controllers capability as the task didn't include videogame components per se. Ethics statement. The study was approved by the "Comited'ethique de la recherche vieillissement neuroimagerie" of the CIUSSS du Centre-Sud-de-l'ı ˆle-de-Montreal (CIUSSS-CSMTL) under the number CER VN 18-19-22 and all participants provided written consent.
In the mock setup we used an MRI simulator made by Psychology Software Tools (https:// pstnet. com/products/mri-simulator/), and used an in-house 3D printed replica of the Siemens 64 channel head coil used in the MRI. An in-house built mirror mount replicating the Siemen's mirror attached to the head coil was used to present images to the participants. Instead of a projection, visual stimuli were presented using a small monitor placed within the mock bore, closely replicating the manner in which visual stimuli are presented in the MR scanner. A replica of the SNES™ controller (iNNEXT Retro USB Super Classic Controller, iNNEXT) was installed inside the mock scanner, and directly connected to the stimulation computer via USB (Fig  3 ). Participants were instructed they would have to perform a cued-response task, in which, using visual cues, they would have to press one of the 8 buttons present on either the MRI or SNES controllers, for a short or long period of time.
Each participant performed 8 runs per setup (Mock and MRI) split in 4 sessions, for a total of 1 280 trials (80*8*2). Control datasets. To illustrate the values of keypress durations that naturally occur when playing action platformer videogames, and use them as reference to interpret the results of our gamepad task, we computed the durations observed in the training phase of the shinobi dataset. The data for the shinobi_training dataset were obtained using the commercial controller (i. e non-MRI controller that was also part of our Mock scan condition of the gamepad task). This dataset comprised the same 4 subjects that performed the gamepad and shinobi tasks, and totals 1 165 trials across the 3 selected levels of Shinobi III: Return of the Ninja Master  [26]. The game was played and recorded at 60 frames per second, allowing us to compute keypress durations with a precision of 16ms.
In order to estimate the amount of motion induced by our controller both in controlled and naturalistic conditions, we compared the motion (quantified by Framewise Displacement, FD) observed during the fMRI acquisitions of the gamepad and the shinobi datasets respectively with other datasets obtained on the same 4 subjects, chosen as a reference because they did not involve the controller. One exception is the shinobi dataset as sub-03 did not participate, and thus we only included data from 3 of the 4 subjects. The control datasets (friends, hcptrt-gamble, hcptrt-motor and hcptrt-rest) were selected to reflect the variety of motion amounts one can expect during conventional active and passive tasks. We used around 38h of fMRI data from the friends dataset during which the subjects were watching seasons 1 up to 4 of the Friends TV show  [27].
Given the engaging nature of the stimuli and the passive nature of the task, we expected the associated FD values for this dataset to indicate relatively low motion  [28]. We also used data from the hcptrt dataset, namely the resting state, gambling and motor tasks. All tasks in the hcptrt dataset were designed by the team at the Human Connectome Project  [29]  and each task was repeated up to 15 times. The resting state task was repeated 4 or 5 times per subject and consisted of staring at a cross (no response). The gambling task was repeated between 15 and 17 times per subject; using a standard MRI response box subjects were instructed to guess if they thought a hidden number (represented by a "?") was above or below 5. The motor task was repeated between 15 and 18 times per subject. In this task, participants were presented a visual cue and were asked move a body part (ie.
tongue, fingers, toes, hand, feet) in response to that cue. We expected these three datasets to reflect relatively low, moderate and high levels of motion for the resting, gambling and motor tasks respectively. Lastly we used fMRI data from 3 of the participants in the shinobi dataset, which consists of about 10h (approximately 56 runs) of fMRI data per subject. During the shinobi task, subjects played three selected levels of Shinobi III: Return Of The Ninja Master  [26] , a retro videogame released on Sega Genesis™, using our custom videogame controller. We expected this last dataset to reflect the amount of motion typically observed during fMRI acquisitions involving videogame play with the CNeuroMod controller. For more information on each of the control datasets, see the Courtois NeuroMod's Project documentation page: (https://docs.
cneuromod. ca/en/latest/). fMRI phantom recordings. Thirty runs of 60 volumes were acquired using a fBIRN gel phantom with the custom controller in the scanner, and 30 more without the controller, all following the imaging sequence described in the MRI acquisition section. Each run lasted about 2 minutes, and a total of 60 runs were acquired on a single day. MEG empty room recordings. Empty room noise recordings were acquired as part of the routine procedure for MEG acquisitions. The data was collected on separate days with a MEG CTF-275 (with 270 functioning channels) while no participant was inside the MEG shielded room. In each recording, 35 seconds of signal were acquired at a sampling frequency of 1200Hz. No offset removal nor filters were used during these acquisitions. We used the empty room recordings acquired between 02/04/2019 and 09/06/2021 (N = 80), and split them into 4 subsets of 20 consecutive recordings.
Motion assessment (custom controller VS control tasks). Framewise Displacement (FD) values were obtained from the confounds file generated by the fMRIprep preprocessing pipeline  [25]  applied to each of the the datasets listed above. For each run of every dataset, the median of FD values were computed, then compared across datasets separately for each subject using two-tailed Kolmogorov-Smirnov tests for independent samples with FDR (Benjamini-Hochberg) correction for multiple comparisons. Specifically, we tested the differences between the gamepad dataset and the friends, hcp-rest, hcp-gamble and hcp-motor datasets for all 4 subjects, and the differences between the shinobi dataset and all others datasets for the 3 subjects that performed the shinobi task. fMRI temporal SNR (with versus without controller).
Temporal Signal-to-Noise Ratio (SNR) maps were computed on each run then averaged per condition (data from the tasks was divided into one of two conditions; with or without the controller present in the scanner) using Nipype  [30]. For each voxel, the mean and standard deviation across the distribution of runs were computed and displayed separately for the two conditions. Additionally, we extracted the voxels located within an EPI mask (i. e. voxels located in the gel phantom) and compared the distributions of mean and standard deviation using independent T-Tests. MEG noise covariance (with versus without controller). Noise covariance matrices were computed on each empty room acquisition run using the MNE-python toolbox  [31]. Because covariance matrices are symmetric, only the upper part of the matrices were used for subsequent analysis.
Motion assessment (custom controller versus control tasks) Analysis of run-median FD values (Fig 6  and Table 4 ) showed no significant differences of motion between the gamepad dataset and the other datasets that didn't use our MRI-compatible controller, namely the hcp-rest, hcp-motor and hcp-gamble datasets and the friends dataset (all p > 0. 05), with the exception of 2 subjects that showed significant differences between the gamepad and friends datasets (p < 0. 01). For all 3 subjects, the shinobi dataset had significantly higher run-median FD values than all the other datasets (all p < 0. 05), except for one subject that showed no significant difference between the shinobi dataset and the hcptrt resting-state dataset.
Reproduction A reproduction of the CNeuroMod controller was built by the engineering team at Neurospin and members of the MIND team at Inria (Paris-Saclay, France; Fig  9 ). The MIND team was not involved in the design process. The reproduced controller was built according to the design plans and documentation made available online and was fitted to their local MRI system. Minor modifications were implemented by the MIND team, for example the shutter and the cap were printed together instead of assembled after printing and the fiber optic cable was 12m long instead of 10m. Other minor assembly issues (buttons getting stuck) were reported and troubleshooted directly with AC, although other teams will also be able to obtain support via GitHub. The controller was tested by 9 different players during gameplay on Super Mario  Bros  [32]  within the MRI scanner and player reported satisfying device useability as measured by the utility ratings.
Discussion We built the CNeuroMod videogame controller to be compatible with both fMRI and MEG. We wrote the documentation, plans needed by other teams to reproduce the controller and its software and made it publically available under an Open Source Hardware license. A feasibility study showed that our controller consistently registers button presses and releases with only small differences in timing compared with a commercial controller. We showed that the CNeuroMod controller can be used without inducing abnormal motion levels. The presence of the CNeuroMod controller in the scanning environment had small effects on tSNR maps and noise covariance matrices with fMRI and MEG, respectively.
Reactivity of the CNeuroMod videogame controller The study showed that the CNeuroMod controller consistently registers button presses as well as releases with a temporal precision comparable to a commercial controller. Although no differences were observed in button press reaction times, small significant differences were detected in release reaction times (for long presses) and in press duration (for short presses), showing longer release times with the prototype than with the reference controller. This result might indicate the presence of mechanical friction during the button release, an aspect that could be improved in further design iterations. This difference in release reaction time could impact game play performance, particularly in games that require dense repetitions of button presses, but is likely negligible when playing most other games, as suggested by the successful collection of the shinobi dataset as well as the pilot implemented by the MIND team with Super Mario Bros  [32].
Motion comparison across various data sets The analyses conducted on fMRI runs collected on a wide range of tasks showed similar amounts of motion during our gamepad task compared to control tasks that did not involve the use of a videogame controller. This suggests that the CNeuroMod controller does not increase the amount of motion in the scanner. Conversely, the comparison between the motion measured during the shinobi dataset to that of other control datasets indicated significantly higher levels of motion for the gaming task (shinobi), likely to be attributable to the videogame content and not to the controller itself. Even in the case of the shinobi dataset, many runs had a fairly low level of motion, with median FD levels below 0. 2 or even 0. 1. Overall, the usage of the Courtois NeuroMod controller seems compatible with the collection of usable fMRI data, and in some instances even high-quality low-motion data.
fMRI and MEG compatibility Analyses performed on fMRI data acquired on a gel phantom head showed a small impact of the CNeuroMod controller on temporal SNR. These differences are statistically significant but are relatively small compared to the amount of noise typically observed during such recordings. Because fMRI recordings are known to be sensitive to thermal noise  [33] , which can fluctuate across the time of the day, the observed differences might be attributable to the fact that these two series of recordings were acquired within the same day but at different time and thus, at slightly different room temperatures. Replicating these results with other datasets would further confirm that the amount of noise induced by the controller stays well within acceptable range. The MEG noise covariance matrices showed no significant impact of the CNeuroMod controller.
Context. Vacuum-ultraviolet (VUV) photons are important drivers of chemical processes in space. Thus, it is important to accurately characterize and constrain photorates in different radiation fields, via the photodissociation and photoionization cross sections of individual atoms and molecules. These have been available in the Leiden VUV photodissocation and photoionization cross section database. Aims. Experimental and theoretical advances in the past decade or so have allowed multiple new cross sections to be obtained, particularly photoionization cross sections of radicals. The database is hereby updated by including these more recent cross sections and is also expanded with several astronomically relevant species. Methods. The cross sections have been used to calculate photodissociation and photoionization rates in several different radiation fields as well as from cosmic-ray-induced VUV fluxes.
The reduction of rates in shielded regions was calculated as a function of dust, molecular and atomic hydrogen, atomic carbon, and self-shielding column densities. The relative importance of these shielding types is molecule and atom dependent, as well as the assumed dust absorbance. All the data are publicly available from the Leiden VUV cross section database. Results. The Leiden VUV cross section database has been updated with 14 new astrophysically relevant molecular species and 16 updates to previous entries. The database update is accompanied by a brief review of the basic physical processes, particularly photoionization processes which have not been reviewed in the context of previous database updates.
Introduction At the time of writing, almost a century has passed since the first propositions that atomic and molecular abundances in interstellar environments are governed by photodissociation and photoionization processes  (Eddington 1928). Today it is indeed apparent that vacuum-ultraviolet (VUV) radiation is undeniably an important driver of chemical reactions in space. The VUV regime is characterized by photons with wavelengths in the 10-200 nm range which can only propagate under vacuum (hence the name); however more importantly, VUV photons possess the unique energetic quanta capable of breaking molecular bonds and/or ionizing atoms and molecules. These characteristic photodissociations and photoionizations are both species-and ⋆ Leiden photodissociation and photoionization cross section database: https://home.
strw. leidenuniv. nl/~ewine/photo/ wavelength-dependent, and hence, VUV-driven photodissociation and photoionization processes have been accumulated and database-formatted for wide usage in the astrochemical community. These are the principle parameters when it comes to quantifying photodestruction rates (or photorates) of molecules in different interstellar or galactic environments, which makes their implementation crucial not only in astrochemical modeling  (van Dishoeck 1988; Gredel et al. 1989; Roberge et al. 1991; van Dishoeck et al. 2006; van Dishoeck & Visser 2015; Heays et al. 2017) , but in combustion science as well where on average larger molecules such as hydrocarbons and complex organic molecules are typically implemented  (Kameta et al. 2002; Yang et al. 2012; Keller-Rudek et al.
Accommodating new molecules in astrochemical models is a titanic task requiring an enormous amount of reaction rates. The previous update of the Leiden VUV cross section database  (Heays et al. 2017 ) collected photodissociation and photoionization cross sections for 102 atoms and molecules of astronomical interest. These were used to calculate dissociation and ionization rates under different radiation fields (including cosmicrays) both with and without dust attenuation. An update to the database is timely to meet the data needs of the ever-growing astrochemical user community, which also presents an opportunity to highlight recent experimental and theoretical advances that have allowed cross sections of (in particular) radicals to be measured in absolute terms and will continue to produce datasets relevant to interstellar photoprocesses.
Section 2 also gives an account of the radiation fields utilized to compute photodestruction rates in this work and a brief description of the dust and molecular line shielding components utilized to derive shielding functions of the computed photorates. Section 2 concludes with a description of the cosmic-ray ionization derived photorates and a brief account of the effects of temperature on cross sections and caveats concerning the applicability of the cross sections for exoplanet atmospheres. Section 3 gives an overview of all the updated cross sections in the database. And Sect. 4 is devoted to a discussion of the updates and prospects for the future.
Typical intensities of predissociative states are usually higher than that of (directly) dissociative states and typical values are around 10 -16 cm 2 , but these are significantly varied. Finally, (iv) spontaneous radiative dissociation is where a bound state radiates back into the vibrational continuum of a lower state with line-dependent probability. For H 2 , this is the dominant photodissociation pathway  (Stecher & Williams 1967)  and peak cross sections may reach 10 -14 cm 2 over a width of less than 0. 1 nm, making the cross section highly structured. Since many of the database updates outlined below concern new photoionization cross sections, it is worth formulating valence photoionization processes a bit further noting that several literature reviews of valence photoionization processes are available  (Hatano 1999a (Hatano ,b, 2001;; Dyke 2019).
This leads to the separation of nuclear and electronic coordinates: Ψ(x, R) = Ψ el (x, R)Ψ nuc (x, R). (2) This separation allows the electronic eigenvalues to be extracted via: H el (x, R)Ψ el (x, R) = E el (x, R)Ψ el (x, R), (3) where the electronic energies (E el ) represent the potential energy curves or surfaces of the molecular system. When rovibrational energy levels of a diatomic molecule in a 1 Σ ± electronic state are treated, the solution represents a well-studied mathematical problem  (Simon 2000)  for which many efficient numerical methods are available  (Yurchenko et al. 2016 and references therein) , most notably the iterative Cooley-Numerov method  (Noumerov 1924; Cooley 1961; Cashion 1963)  which is used in the program LEVEL from Le  Roy (2017). The situation is, however, usually much more complicated.