dataesr/scientific-paragraphs-categorization huggingface dataset:
(2020)  not only failed to replicate past findings with the Big 5 personality traits, but also failed to find any statistically significant associations in their whole-brain exploratory analyses. These findings are therefore also consistent with enduring difficulties linking brain structure to personality, which likely have their basis in a combination of construct-specific (reliability and validity) and statistical-power-related limitations. In conclusion, our results indicate that absorption is not reliably associated with individual differences in brain structure or function. Using two datasets from independent labs (n = 201 and n = 68), we were unable to find any reliable association between trait absorption scores and a range of neuroimaging measures. It is indeed possible that the true effect size of such associations is much lower than suggested here, and that replication in a larger (i.
e. , n > 1000) dataset would uncover more subtle relationships between absorption and the brain  (Marek et al. , 2022). Nonetheless, our results motivate a closer critical look at absorption as a construct, and call into question the general idea that complex personality traits can be straightforwardly associated with baseline neurobiological signatures. Future work should evaluate whether absorption is truly a stable personality trait that is uniformly expressed in individuals who score highly on the TAS/MODTAS, or whether it integrates multiple neurobiologically distinct tendencies that can be evoked in varying ways depending on context  (Luhrmann et al. , 2021). The differential effects across absorption subscales reported above also suggests that particular dimensions of this trait may vary in the extent to which they can be grounded in a reliable neurocognitive mechanism.
After 3 washes of 10 minutes in TBS 1X, Tween 20 0. 05%, the membranes were incubated with 1:5000 goat anti-rabbit-HRP conjugate (Biorad) for GltJ and with 1:5000 goat anti-mouse-HRP conjugate for Neon-Green in blocking buffer. Then the peroxidase reaction was developed by chemiluminescence (SuperSignal West Pico (or Femto) Chemiluminescent substrate, Thermo Scientific), scanned and analyzed with ImageQuant LAS4000 and TL analysis software (GE Healthcare Life Sciences). cf Single cell motility assays TIRF microscopy. Cells were grown ON at 32°C until reach OD600 = 0. 4-0. 6, then resuspended in TPM at OD600=2 and 2µl were spotted on a TPM 1. 5 % agar pads poured inside a gene frame (Thermo Scientific™ -Gene Frame) taped to a microscope slide. An SR cover glass (Marienfeld Superior -Precision cover glasses No.
Automated cell tracking and statistics The tracking workflow was designed as follows: Cells to be analyzed were selected manually and kymographs were generated under FIJI  37  to determine the displacement of the cell center and the two poles. The fluorescent foci were then annotated manually by drawing polygons delineating foci traces directly on the kymograph. A FIJI macro was then used to produce data files in. csv format which contain all the information of the cell: movement, type of cell, type of foci, number of foci, displacement of foci, etc. The obtained files were then subjected to an R-script which produced a pdf report for each cell: The calculated parameters were: 1 -cell movement = sum of cell displacements relative to the substrate 2 -the relative motion of each cluster = sum of the displacements of the cluster with respect to the center of the cell 2 -the mean square displacement of the cell (MSD) 3-the mean square displacement of the focus 4 -the periods of movement and stop of the cell These parameters are calculated raw or normalized to the duration of the movement.
Isothermal titration calorimetry ITC was used to evaluate the thermodynamics parameters of the binding between GYF GltJ or Nt1-222 and PRS AglZ protein domains. Titrations were carried out on a MicroCal PEAQ-ITC (Malvern, UK) microcalorimeter at 25°C. For each experiment including controls, 19 injections have been set with the first injection set at 0. 4µL and the 18 following injections at 2µL. Duration of each injection was 4s and the interval between each injection was 150s. All experiments were performed in 10mM NaPO 4 pH 7 ITC buffer. PRS AglZ was loaded into the sample cell and GYF GltJ or Nt1-222 were loaded into the syringe. Control experiments were performed in which the ITC buffer was titrated into PRS AglZ alone to account for heat released due to dilution. This background was subtracted from test data before final dissociation constant was calculated.
NMR structure determination ZnR GltJ and GYF GltJ isolated domains have been produced as uniformly  13  C,  15  Nlabeled proteins. Backbone and side-chains resonance assignments have been previously performed  24  and are available under BMRB accession number 51104 and 51096 for ZnR GltJ and GYF GltJ , respectively. The three-dimensional protein structures were calculated with the program CYANA using NOE distance restraints obtained from NOESY-based NMR experiments, and angular restraints (phi and psi) derived from chemical shifts of backbone atoms using the TALOS+ software program  39. In addition, hydrogen bonds restraints were extracted from HNCO-COSY experiments  40  and then included for structure calculations. For ZnR GltJ , distance restraints for the zinc ion coordinated to the corresponding cysteines were introduced as pseudo-NOEs  41.
NMR titrations Series of 1 H,  15  N-HSQC experiments were recorded on 0. 1 mM 15 N-labeled domains (ZnR GltJ , GYF GltJ , Nt1-222 or linker-GYF GltJ ) upon protein or peptide partner addition. Different molar ratios were used and indicated in the figures. For GYF GltJ /PRS AglZ titration, the buffer was 10 mM NaPO 4 pH 7. 1 H 2 O: 2 H 2 O ratio was 90:10 v:v. Data were obtained at 303K. For all other titrations, the interaction buffer was 20 mM Tris-HCl pH 7, 50 mM NaCl, 10 mM MgCl 2 , 1 mM DTT, 1 mM GTP or GDP. 1 H 2 O: 2 H 2 O ratio was 90:10 v:v. Data were obtained at 298K.
Estimating reactions constants Values for K m and k cat were calculated by fitting the data to the Michaelis-Menten equation as described below with nonlinear regression curve fitting using GraphPad Prism (version 9. 4. 1, GraphPad Software Inc. ). We constructed the following model based on available literature  27, 28, 43, 44  : The velocity equation for this random system can be derived from rapid equilibrium assumptions: 𝑘 𝑜𝑏𝑠 = 𝑣 [𝑀𝑔𝑙𝐴] 𝑡 = 𝑘 𝑐𝑎𝑡 [𝑀𝑔𝑙𝐵][𝐺𝑇𝑃] 𝐾 𝐵 𝐾 𝑆 [𝐺𝐷𝑃] + 𝐾 𝐵 ′ [𝐺𝑇𝑃] + 𝐾 𝑆 [𝐺𝐷𝑃][𝑀𝑔𝑙𝐵] + [𝑀𝑔𝑙𝐵][𝐺𝑇𝑃] Rearranging equation terms: 𝑘 𝑜𝑏𝑠 = 𝑣 [𝑀𝑔𝑙𝐴] 𝑡 = 𝑘 𝑐𝑎𝑡 (1 + 𝐾 𝐵 ′ [𝑀𝑔𝑙𝐵] ) [𝐺𝑇𝑃] 𝐾 𝑆 [𝐺𝐷𝑃] (1 + 𝐾 𝐵 [𝑀𝑔𝑙𝐵] ) (1 + 𝐾 𝐵 ′ [𝑀𝑔𝑙𝐵] ) + [𝐺𝑇𝑃] with 𝑘 𝑐𝑎𝑡 𝑎𝑝𝑝 = 𝑘 𝑐𝑎𝑡 1+ 𝐾 𝐵 ′ [𝑀𝑔𝑙𝐵] ; 𝐾 𝑚 𝑎𝑝𝑝 = 𝐾 𝑆 [𝐺𝐷𝑃] ( 1+ 𝐾 𝐵 [𝑀𝑔𝑙𝐵] 1+ 𝐾 𝐵 ′ [𝑀𝑔𝑙𝐵] ) ; 𝑘 𝑐𝑎𝑡 𝑎𝑝𝑝 𝐾 𝑚 𝑎𝑝𝑝 = 𝑘 𝑐𝑎𝑡 𝐾 𝑆 [𝐺𝐷𝑃](1+ 𝐾 𝐵 [𝑀𝑔𝑙𝐵] ).
Sequence structure and comparative genomics analyses Sequence analysis Previously known bona fide GYF domains were obtained from the National Center for Biotechnology Information (NCBI) Genbank database  45. Sequence similarity searches were performed using the PSI-BLAST  46  and JackHMMER  47  programs against the NCBI non-redundant (nr) database clustered down to 50% sequence identity using the MMseqs program  48. The profile-inclusion threshold was set at 0. 01. Profile-profile searches were performed with the HHpred program  49. Alignments were constructed using the Mafft and Famsa programs followed by refinements using HHpred profile-profile matches or HMM-align. These MSAs were also finally manually adjusted, guided by structure superimpositions.
Structure analysis The JPred program  50  was used to predict secondary structures using MSAs (see above). Structure similarity searches were performed using the FoldSeek program [10. 1101/2022. 02. 07. 479398] and the structures were rendered, compared, and superimposed using Mol*  51. Structural models were generated using the RoseTTAfold program  52. Multiple alignments of related sequences (>30% similarity) were used to initiate HHpred searches for the step of identifying templates to be used by the neural network deployed by RoseTTAfold. e. Combined chemical shift deviations (CSP) of Nt1-222 bound to MglA-GTP. f. Spectra region of the 1 H,  15  N HSQCs of  15  N-labeled Nt1-222 free (green) and after addition of 1 (magenta), 2 (light purple) and 2 (purple) molar ratio of MglA-GTP. 1  H,  15  N HSQC of free ZnR GltJ (salmon) is also superimposed.
Acknowledgements Intramural Funds  of the  National Library of Medicine, USA (LA and VA). Financial support from the  IR INFRANALYTICS   FR2054  for conducting the research is gratefully acknowledged. We would like to thank  Françoise Guerlesquin  for helpful discussions and  Deborah Byrne  for her technical assistance during ITC experiments. JP Castaing  was supported by a  Fondation Bettencourt Schueller "coup d'élan pour la recherche française  » to TM,  Céline Dinet  was supported by a  CENTURI Post-doc fellowship  and an  ERC-Advanced grant  (JAWS) to TM.
more than 4000 exoplanets have been discovered thanks to advanced space telescopes such as Kepler  23, 24  , TESS  25  , and CHEOPS  26. The discoveries suggest that there could be ~ 100 billion Earth-like planets throughout our galaxy alone that may offer habitable conditions  27. One exciting target for exploration is the planet Proxima Centauri b. It is part of the Alpha Centauri star system and is located at a distance of 4. 2 light years from Earth. The planet orbits the red dwarf star Proxima Centauri, the closest star to our planet. Proxima Centauri b is in its habitable zone, at a distance of about 7. 5 million km, and has an orbital period of 11. 2 days  28. The Breakthrough Starshot initiative aims to send thousand centimeter-sized, gram-scale probes mounted on a spacecraft with 'light sails' to the Alpha Centauri system to gather and signal back data to the Earth  [29] [30] [31].
Optical properties and optical simulations The optical properties of the photoactive blends were obtained experimentally from transmission and reflection measurements. The transmission and reflection spectra of the D:A bulk-heterojunction films were measured using a UV-vis-NIR spectrophotometer (Lambda 1050, Perkin Elmer). The thicknesses of the films were measured using a profilometer (Dektak XT Stylus Profiler). Modeling beyond the TMM method was carried out in python code based on the equations presented in the manuscript. Optical Simulations were carried out using the freely available Transfer Matrix Method software and the optical properties of the electrode materials were used as reported in previous work. 74, 75.
Acknowledgements This work is supported by the  Collaborative Research Program Grant of Nazarbayev University  Grant No. 11022021CRP1505  and the  Faculty-Development Competitive Research Program Grant of Nazarbayev University  Grant No. 11022021FD2915. M. S. thanks the  Polish National Agency for Academic Exchange (NAWA)  for financial support within the  Ulam NAWA Program  (No. BPN/ULM/2021/1/00230 ). N. S. acknowledges financial support from the  Link Energy Foundation  fellowship, N. S. and T. -Q. N. thank the  US Office of Naval Research  (Award No. N00014-21-1-2181 ) for the support. E. A. is partially supported by the  Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan  (Grant No. AP13067834  and  AP08856149 ) and the  Nazarbayev University   Faculty Development Competitive Research Grant Program  No  11022021FD2912.
PD Diagnosis Using Motion Data Several types of sensors have been investigated to collect motion data depending on the movements of interest. Wahid and colleagues  [10]  investigated the discrimination between PD patients and healthy controls using gait data collected during self-selected walking. They extracted spatial-temporal features, such as stride length, stance time, swing time, and step length, from the signals and investigated different strategies of data normalization using dimensionless equations and multiple regression and different machine learning algorithms such as naive Bayes (NB), k-nearest neighbors (kNN), support vector Mirelman and colleagues  [11]  also investigated gait and mobility measures that are indicative of PD and PD stages. They gathered data from sensors adhered to the participant's lower back, bilateral ankles, and wrists, during short walks, and extracted gait features.
PD Diagnosis Using Voice Data Voice data is usually recorded from high-quality microphones or from smartphones during specific vocal tasks focused on characteristics such as phonation and speech. Features are then extracted from the corresponding signals and used as input to machine learning classification algorithms. Amato and colleagues  [14]  analyzed specific phonetic groups in native Italian speakers, extracted several spectral moments from the signals, and trained a SVM algorithm on these extracted features to distinguish PD patients from HC. They first worked on a public data set called Italian Parkinson's Voice and Speech, 1 with data recorded in ideal publications, and obtained great performance on the validation and test sets. They then merged this public data set with a data set that they collected, with data being recorded in more realistic, suboptimal conditions, and obtained good but lower performance on the validation and test sets of this merged data set.
In another study, Jeancolas and colleagues  [16]  investigated the differentiation between early PD patients and patients with idiopathic rapid eye movement sleep behavior disorders (iRBD), which are important risk factors to develop PD in the near future. They extracted features related to prosody, phonation, speech fluency, and rhythm abilities from speech recordings. They once again obtained a higher predictive performance among men than women in the PD vs HC classification tasked and a better discriminative power for this classification task than for the iRBD vs HC one, suggesting that discriminating iRBD patients from HC using voice data is a much harder task, but it is also probably a most useful one in practice. Quan and colleagues  [17]  investigated the extraction of global static features (from the whole signals) and local dynamic features (using a sliding window on the signals) from voice data during articulation tasks.
They analyzed data from 374 PD patients and 169 HC from the Parkinson's Progression Markers Initiative 2 (PPMI) cohort and included white matter, gray matter, and cerebrospinal fluid measurements from 98 regions of interest. The combination of the proposed feature selection/extraction method and classifier achieved the highest predictive accuracy (0. 819), being significantly better than almost every other combination of a feature selection/ extraction method and a classification algorithm. Solana-Lavalle and Rosas-Romero  [22]  investigated the use of voxel-based morphometry features extracted from T1-weighted anatomical MRI to perform a PD vs HC classification task. Their pipeline consisted of five stages: (i) identification of regions of interest using voxel-based morphometry, (ii) analysis of these regions for PD detection, (iii) feature extraction based on firstand second-order statistics, (iv) feature selection based on principal component analysis, and (v) classification with tenfold crossvalidation based on seven different algorithms (including k-nearest neighbors, support vector machine, random forest, naive Bayes, and logistic regression).
They obtained good predictive performance in most binary classification tasks and showed that midbrain, basal ganglia, and cerebellar peduncles were the most relevant regions. A landmark study on this topic was published in 2019 by Archer and colleagues  [27] , with diffusion-weighted MRI data being collected for 1002 subjects from 17 MRI centers in Austria, Germany, and the USA. They extracted 60 free-water and 60 freewater-corrected fractional anisotropy values from diffusionweighted MRI data, and the other features consisted of the third part of the Movement Disorder Society-Sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS III), sex, and age. They trained several SVM models and showed that the model trained using MDS-UPDRS III (with sex and age also, for all the models) performed poorly in most classification tasks, whereas the model trained using DWI features had much higher predictive performance (particularly for the MSA vs PSP task), and adding MDS-UPDRS III to this model did not improve the performance.
More recently, Chougar and colleagues  [28]  investigated the replication of such differential diagnosis models in clinical practice on different MRI systems. Using MRI data from 119 PD, 51 PSP, 35 MSA-P, 23 MSA-C, and 94 HC, split into a training cohort (n = 179) and a replication cohort (n = 143), they extracted volumes and diffusion tensor imaging (DTI) features (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) in 13 regions of interest. They investigated two feature normalization strategies (one based on the data of all subjects in the training set and one based on the data of HC for each MRI system to tackle the different feature distributions, in particular for DTI features, because of the use of different MRI systems) and four standard machine learning algorithms, including logistic regression, support vector machines, and random forest.
Freezing of Gait Freezing of gait (FOG) is a common motor symptom and is associated with life-threatening accidents such as falls. Prompt identification or prediction of freezing of gait episodes is thus needed. Ahlrichs and colleagues [37] investigated freezing of gait in 20 PD patients (8 with FOG, 12 without FOG), split into a training set (15 patients) and a test set (5 patients). They collected sensor (accelerometer, gyroscope, and magnetometer) data during scripted activities (e. g. , walking around the apartment, carrying a full glass of water from the kitchen to another room) and non-scripted activities (e. g. , answering the phone). Two recording sessions were considered, one in "OFF" motor state and one in "ON" motor state, and the data was labeled by experienced clinicians based on the corresponding video recordings.
The task was a binary classification task (FOG vs no FOG) for each window. They extracted sub-signals from the whole signals using a sliding window and then extracted features and in the time and frequency domains for each sub-signal. They trained two SVM algorithms (one with a linear kernel, one with a Gaussian kernel) and obtained high and better results with the linear kernel. Aich and colleagues  [38]  gathered sensor data for 36 PD patients with FOG and 15 PD patients without FOG from 2 wearable triaxial accelerometers during clinical experiments. They extracted features, such step time, stride time, step length, stride length, and walking speed, from the signals. They trained several classic machine learning classification algorithms (SVM, kNN, DT, NB) and obtained good predictive performances with all of them, although the SVM model had the highest mean accuracy on the test sets of the cross-validation procedure.
Borzı `and colleagues [39] collected data from 2 inertial sensors placed on each shin of the 11 PD patients during the "timed up and go" test in order to investigate FOG and pre-FOG detection. They extracted features in the time and frequency domains and trained decision tree algorithms. They obtained great predictive performance to detect FOG episodes, but lower performance to predict pre-FOG episodes, with the performance decreasing even more as the window length increased. Dvorani and colleagues  [40]  were interested in detecting foot motion phases using a shoe-placed inertial sensor in order to detect FOG episodes. They extracted ten features, including stride length, maximum gait velocity, and step duration, from each motion phase and trained a SVM algorithm to detect FOG episodes. They obtained great performance when using features from the current and two preceding motion phases, but lower performance when using only features from the two preceding motion phases, highlighting the higher difficulty to predict FOG episodes in advance.
Kim and colleagues [43] performed a study in which they investigated tremor severity using three-dimensional acceleration and gyroscope data obtained from wearable device. They investigated a convolutional neural network to automatically extract features and perform classification, compared to extracting defined features from the time and frequency domains and training standard machine learning algorithms (random forest, naive Bayes, linear regression, support vector machines) using these features. They obtained better higher predictive performance with the deep learning approach than the standard machine learning approach. Eskofier and colleagues [44] obtained similar results using inertial measurement units collected during motor tasks.
Lu and colleagues [52] investigated gait in PD, as measured by MDS-UPDRS item 3. 10, which does not include freezing of gait. They collected video recordings of MDS-UPDRS exams from 55 participants which were scored by 3 different trained movement disorder neurologists, and the ground truth score was defined using majority voting among the 3 raters. They performed skeleton extraction from the videos and trained a convolutional neural network, with regularization using rater confusion estimation to tackle noise in labels, to predict gait severity. They obtained correct performance on the test set (72% accuracy with majority voting, 84% accuracy with the model predicting at least one of the raters' scores). Gao and colleagues [53] investigated falls in two data sets independently collected at two different sites.
Prediction of Future Motor and Nonmotor Symptoms Prediction of future symptoms and individual disease trajectories was the main focus of several studies. Oxtoby and colleagues [56] aimed at estimating the sequence of clinical and neurodegeneration events, and variability in this sequence, using data-driven disease progression modelling, with a focus on PD patients with higher risk of developing dementia (defined as PD patients being diagnosed at age 65 or later). They analyzed baseline visit data from two separate cohorts: a local discovery cohort (100 PD patients and 33 HC) and a replication cohort (PPMI study, 350 PD patients and 127 HC). They considered 42 features, including 8 clinical/cognitive measures, 6 vision measures, 4 retinal measures, 8 regional measures of cortical thickness, 4 measures of white matter neurodegeneration in the substantia nigra, and 12 regional measures of brain iron content.
Treatment efficacy was defined based on motor symptom improvement from the state of medication off to medication on, as assessed by MDS-UPDRS III total score. They obtained great discriminative performance between both groups, even though no significant difference in clinical data was observed between both groups. The mainly contributed regions for both models included the bilateral primary motor cortex, the occipital cortex, the cerebellum, and the basal ganglia. These results suggest the potential utility of amplitude of low-frequency fluctuation as promising predictive markers of dopaminergic therapy response in PD patients. Kim and colleagues  [64]  investigated the use of reinforcement learning to predict optimal treatment for reducing motor symptoms. They derived clinically relevant disease states and an optimal combination of medications for each of them by using policy iteration of the Markov decision process.