dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, the chromatographic scale (nano-vs. micro-) has inevitably influenced quantification performances, as exemplified for few peptides. Under the conditions of our study, PRM provided the best sensitivity and dynamic range performances, and SWATH-MS outperformed SRM regarding these metrics. However, all three strategies showed quite similar quantification precision and accuracy performances, although the linear correlation between SRM and SWATH-MS quantification was higher in this context. Then, we extended the proteome coverage by adding extracted protein amount estimates for all proteins detected in SWATH-MS using a universally applicable approach. This offered a proteome-wide quantitative repertoire of the samples. Of course, profiling comprehensiveness in SWATH-MS depends on the SL used for data extraction.
Associated data Complete DDA datasets have been deposited to the ProteomeXchange Consortium via the PRIDE  [42]  partner repository with the dataset identifier PXD020638 and 10. 6019/PXD020638. The full Skyline data files and raw data are publicly available on Panorama public (https://panoramaweb. org/SWATH-MS_PRM_SRM_BovineMuscles. url, ProteomeXchange ID PXD020680). c ) are significantly different from each other at P ≤ 0. 05. Gene ontology annotations within the biological process category were identified using the PROTEINside werb service (https://www. proteinside. org/;  [43] ).
Subjects Four male rhesus monkeys (Macaca mulatta), here referred to as D, E, F, and G, were tested in the experiment (mean weight, 12. 65 kg; 9. 9 years of age). They were socially housed (group of four animals) and kept on a 12-hour light/dark cycle, with access to water for 12 to 16 hours on testing days and with ad libitum water otherwise. Before task training, the animals underwent aseptic surgery to implant an MRI-compatible head post (Rogue Research). The head post was necessary to prevent head movement during the TUS and imaging data acquisition. All procedures were conducted under licenses from the U. K. Home Office in accordance with the U. K. Animals (Scientific Procedures) Act 1986 and with the European Union guidelines (EU Directive 2010/63/EU).
Statistical analyses To analyze the behavior of the animals in the three experiments and estimate the effects of no ultrasound stimulation (sham), 47/12o TUS, and aPFC TUS on reward-guided learning and decision-making, we used multiple linear or logistic regressions as implemented in MATLAB. For logistic functions, we used a logit link with categorical predictors. All regressors were normalized to ensure between-session and between-condition commensurability of the regression coefficients. For each session, one  regression weight was extracted for each regressor. These were then tested for statistical significance across all animals/sessions. All data were shown as means ± SEM. Probabilities of P < 0. 05 were considered as significant.
MRI data acquisition Awake animals were head-fixed in a sphinx position in an MRIcompatible chair. fMRI data were collected using a whole-body 3T MRI scanner with a custom-made four-channel phased-array local receive coil in conjunction with a radial transmit coil (Dr. H. Kolster, Windmiller Kolster Scientific, Fresno, CA, USA). Whole-brain bloodoxygen-level-dependent (BOLD) fMRI data were acquired using a gradient echo T2* echo planar imaging (EPI) sequence with 1. 5-mm 3 isotropic voxel size resolution, repetition time (TR) = 2. 28 s, echo time (TE) = 30 ms, and flip angle = 90°, and reference images for artifact corrections were also collected. Proton density-weighted images using a gradient-refocused echo (GRE) sequence (TR = 10 ms, TE = 2. 52 ms, and flip angle = 25°) were acquired as references for body motion artifact correction.
MRI data preprocessing Magnetic resonance images were preprocessed and analyzed using tools from the FMRIB Software Library (FSL)  (45)  and the Magnetic Resonance Comparative Anatomy Toolbox (MrCat; www. neuroecologylab. org). The T1-weighted images were processed in an iterative fashion cycling through a macaque-optimized implementation of FSL's brain-extraction tool (BET), radio frequency (RF) biasfield correction, and linear and non-linear registration (FLIRT and FNIRT) to the Macaca mulatta McLaren template in F99 space as implemented in MrCat. The GRE image was used to carry out the T2* EPI image reconstruction by an offline-SENSE reconstruction method (Dr. Kolster, Windmiller Kolster Scientific, Fresno, CA, USA). A T1-weighted group template of the four animals included in the study was constructed by cycling through a two-iteration approach of (i) registration to the initial McLaren template in F99 space, (ii) group averaging, and (iii) registration to the new group template.
Whole-brain fMRI analysis After transforming each statistical map for each of the four animals from native space to the group-template standard space, group-level statistical analyses of the functional data were performed adopting GLM and tools implemented in FSL  (45, 47). Analysis at the group level was performed on the whole-brain using mixed-effects analysis on the basis of FSL's FMRIB's Local Analysis of Mixed Effects (FLAME) 1 + 2. As already noted, to increase the number of fMRI sessions available and improve the quality of the analysis, sham sessions from both experiment 1 (n = 20; five sessions per animal) and experiment 2 (n = 20; five sessions per animal), thereby creating a pool of 40 no-stimulation sham sessions that were contrasted with either 20 47/12o TUS sessions (five sessions per animal) or 20 aPFC TUS sessions.
Acknowledgments: We thank all the  Biomedical Services Staff  for the care of the macaques. Funding: This work was supported by the  Wellcome Trust  grant  105238/Z/14/Z  (to D. F. ),  Medical Research Council  grant  MR/T023007/1  (to E. F. ),  Biotechnology and Biological Sciences Research Council  grant  BB/M011224/1  (to L. R. ),  Clarendon Fund  (to L. T. ),  Wellcome Trust  grant.
Pharmaceuticals and their transformation products (TPs) are continuously released into the aquatic environment via anthropogenic activity. To expand knowledge on the presence of pharmaceuticals and their known TPs in Luxembourgish rivers, 92 samples collected during routine monitoring events between 2019 and 2020 were investigated using nontarget analysis. Water samples were concentrated using solid-phase extraction and then analyzed using liquid chromatography coupled to a high-resolution mass spectrometer. Suspect screening was performed using several open source computational tools and resources including Shinyscreen (https://git-r3lab. uni. lu/eci/shinyscreen/), MetFrag (https://msbi. ipb-halle. de/MetFrag/), PubChemLite (https:// zenodo. org/record/4432124), and MassBank (https://massbank.
17 However, setting up analyses, both experimentally and computationally, is no trivial matter. Despite these challenges, the information that can be obtained from such analyses has a wide breadth of utility, especially for environmental studies. ] [19] [20]  Nevertheless, the interpretation of HRMS data presents challenges that highlight the need for computational tools to enable the proper identification and annotation of the chemical components in environmental matrices. 21 etFrag (https://ipb-halle. github. io/MetFrag/) 22  is an open source tool for compound identification, including in silico fragmentation, mass spectral matching, and metadata functions. 23, 24  MetFrag enables spectral matching with experimental data via the spectral library MassBank of North America (MoNA, https://mona.
fiehnlab. ucdavis. edu) 25  and prioritization using metadata from various sources. Me Frag first retrieves candidates by exact mass or molecular formula from one of many available compound databases. PubChem (https://pubchem. ncbi. nlm. nih. gov/)  26  is an open chemistry database at the National Institutes of Health (NIH) containing more than 110 million compounds. 27  While such a large database provides access to many chemicals, it can lead to (tens of) thousands of candidates per unknown when performing nontarget screening of hundreds of masses. 28  For this work, an early version of PubChemLite was used, which contains ∼300,000 compounds selected to be highly relevant for environmental investigations based on annotation content, including information relevant for pharmaceuticals. 28, 29  PubChemLite has been shown to outperform other databases such as the whole of PubChem and CompTox for well-known chemicals  28  and delivers important metadata that can be used during identification with MetFrag.
Suspect Screening Suspect screening was performed using two suspect lists. The first list contains 816 unique pharmaceutical compounds (Supporting Information, Table  S1  CNS "Caisse Nationale de Sante" Suspects, also available on the NORMAN Suspect List Exchange, NORMAN-SLE)  30, 32  that were curated from the Luxembourgish National Health Fund's "List of marketed medications in Luxembourg". 33 These drugs have marketing authorization in Luxembourg from the Ministry of Health and are therefore potentially in use domestically. For suspect screening, MS-ready SMILES of these compounds were obtained via the EPA CompTox Chemistry Dashboard's batch search function. 34, 35 sing MS-ready SMILES as a structural identifier ensures that the structure being used for data analysis is consistent with what is measured by the mass spectrometer and at the same time remains traceable within online chemical databases.
Features that passed QC through manual curation including peak shape, peak width, peak intensity, and alignment of the MS1 and MS2 peaks were then analyzed using MetFrag to achieve tentative identifications. Scripts used for this work are available on GitLab. 39 ubChemLite was used as database, available as a local. csv file,  29  to find chemicals that match the exact mass (within 5 ppm) of the suspect pharmaceutical. Both in silico fragmentation (mzabs = 0. 001, frag_ppm = 5) and experimental MS/MS matching through MoNA records (built within MetFrag) were performed to obtain the fragmenter (scoring term 1) and MoNA (scoring term 2) scores. Metadata were also collected for the candidates by querying the database for patent count (scoring term 3), number of PubMed references (scoring term 4), PubChem annotation count (scoring term 5), pharmacology and biochemistry information (scoring term 6), and drug and medication information (scoring term 7).
Further inspection using MetFrag and database matching suggested that the second and third peaks belong to the positional isomers 2-hydroxycarbamazepine and 3-hydroxycarbamazepine, metabolites of the anticonvulsant carbamazepine. The t r matching using a standard confirmed that the peak at 14. 85 min is indeed 3hydroxycarbamazepine, while the peak at 14. 31 min can be assigned as 2-hydroxycarbamzepine (level 3), despite the lack of standards, due to the similarity of its mass spectrum with 3hydroxycarbamazepine. However, the first and biggest peak proved to be challenging. Inspection of the MS1 spectrum at 13. 95 min shows another peak with m/z 271. 1075 (mass difference equivalent to the loss of water, Figure  7B ) can be found whose MS2 spectrum is very similar to the 253. 0972 peaks at 14.
Finally, as experimental databases increase in size and coverage, the ability to screen for more compounds higher confidence with these open source methods such as the one presented here will also increase, highlighting the need for the community at large to continue to contribute to publicly available databases. One main factor limiting TP suspect screening is the lack of available information in open databases that is standardized and thus suitable to be extracted consistently and reproducibly to form meaningful suspect lists. Of the 816 parent compounds on the CNS list, only 44 had associated TP information (i. e. , one or more TPs) that could be extracted from PubChem as performed in this study. Certainly, there are far more pharmaceutical metabolites/TPs than those that are identified here, but this information is not yet available in a readily extractable form suitable for an automated workflow within PubChem (the efforts within the NORMAN Suspect List Exchange have just commenced recently).
* sı Supporting Information The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acsenvironau. 1c00008. The suspect list used in this work is available online as LUXPHARMA (S76) on Zenodo (DOI: 10. 5281/zenodo. 4587356), CompTox (https://comptox. epa. gov/dashboard/ chemical_lists/LUXPHARMA, PubChem (https://pubchem. ncbi. nlm. nih. gov/classification/#hid=101), and NORMAN-SLE (https://www. norman-network. com/nds/SLE/). The data (as. mzML files) are available as data set MSV000087190 from the GNPS MassIVE repository (https://massive. ucsd. edu/ProteoSAFe/static/massive. jsp), citable under DOI: 10. 25345/C5D81C and accessible via ftp://massive. ucsd. edu/MSV000087190/ and https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?accession=MSV000087190. Both Shinyscreen (https://git-r3lab.
uni. lu/eci/shinyscreen/) and MetFrag (http://ipb-halle. github. io/MetFrag/) are open source; additional support scripts mentioned are available from the ECI GitLab repository (https://git-r3lab. uni. lu/eci/ pubchem). All code used to run MetFrag in the command line using R, generate the Transformation Products suspect list, and plot Figures  3 4 5 6 is available via https://git-r3lab. uni. lu/ adelene. lai/additional_si_luxpharma_singh_et_al. All other code and databases used as part of MetFrag identification are likewise openly available (links inline throughout this article). Tables of CNS suspects, pharma IDs, negative mode, positive mode, positive concentration, and the original file names and their corresponding names in this paper (the original file names were kept to allow traceability to the original sample files stored locally at the University of Luxembourg) (XLSX).
Molecular epidemiology is a discipline that combines the techniques of classical epidemiology with molecular techniques, in order to determine the transmission dynamics of infectious diseases, including TB. The method most commonly used previously for the genotyping of Mycobacterium tuberculosis complex (MTBC) was IS6110-restriction fragment length polymorphism  [5]. Being particularly cumbersome, this technique was gradually replaced by PCRbased spoligotyping and 24-loci MIRU-VNTR typing  [6, 7]. The association of these 2 methods is particularly helpful since spoligotyping based on the polymorphism of the DR locus  [8]  simultaneously classifies individual MTBC isolates in broad genotypic families, and helps predict lineages from genotypic patterns using an international database  [9, 10].
Genotyping and molecular data analysis DNA extracts from grown cultures were used for genotyping using spoligotyping and 24-loci MIRU-VNTR typing. Spoligotyping was performed as reported previously  [8] , using a commercial membrane (Mapmygenome India limited). Interpretation was performed manually according to the absence or presence of each of the 43 spacers; it was reported as 0 (absence of hybridization) or 1 (presence of hybridization). 24-loci MIRU-VNTR typing was performed as described previously  [17]. Briefly, the DNA extracts were subjected to individual PCR, and the PCR products were subjected to electrophoresis in 2% agarose gel stained with SYBR Safe (Invitrogen) for 180 minutes at 120 V. The visualization was performed by the photo-documenting equipment of ChemiGenius gels (Syngene) and GeneSnap1 software.
Molecular weight determination was performed with GeneTools1 software version 6. 07. The number of repetitions was determined using the conversion table provided by Supply et al  [17]. Database analysis was performed using the SITVIT2 database of Pasteur Institute of Guadeloupe  [10]  (publicly available at http://www. pasteur-guadeloupe. fr:8081/SITVIT2/), which is an updated version of the SpolDB4 and SITVITWEB  [18]  databases; and the MIRU-VNTRplus application  [19, 20]  available at https://www. miru-vntrplus. org/MIRU/index. faces. The MIRU-VNTRplus portal was also used for the construction of dendrograms by using the UPGMA (Unweighted Pair Group Method with Arithmetic Average) method according to the results of spoligotyping and MIRU-VNTR, obtaining by this way the number of formed clusters.
The SITVIT2 database was used to assign genotypes and to compare them with those reported in other regions worldwide. The genotyping results were combined with epidemiological data obtained from the questionnaires to indicate whether the patients grouped in the same cluster were potentially involved in a transmission chain. Finally, BioNumerics software version 6. 6 (Applied Maths, Sint-Martens-Latem, Belgium; http://www. applied-maths. com/ bionumerics) and SpolTools software  [12, 13]  (available at http://spoltools. emi. unsw. edu. au/), were used to draw minimum spanning trees (MSTs) and a spoligoforest (displayed as an hierarchical layout) respectively, in order to highlight potential relationships between clinical isolates.
Although it is accepted that transmission in communities with high incidences of tuberculosis occurs through household contacts (i. e. , by family members or close friends); transmission also occurs outside the home, yet inside the community  [25]. The BCG immunization was determined by vaccination scar, with 47/74 (63. 51%) of patients having a scar; while in the 1-14 age group, only 14/29 (48. 28%) patients presented a scar (data not shown). Although there is no specific correlation with BCG immunization, studies have shown that vaccination reduces the risk of infection  [26] , and this could explain, in part, the higher number of cases in our pediatric patients. This is the first study describing transmission of MTB using spoligotyping and 24-loci MIR-U-VNTR in indigenous and non-indigenous population of the Colombian Amazon  (Puerto Nariño).
4% 12 de Octubre 254 4,0% 4 5. 4% Atacuary 243 3,9% 7 9. 5% 7 de Agosto 230 3,6% 4 5. 4% San Juan del Soco193 3,1% 3 4. 1% Tipisca 191 3,0% 3 4. 1% Patrullero 155 2,5% - - Puerto Rico 155 2,5% 3 4. 1% Buhayahuazu154 2,4% 6 8. 1% Nuevo Paraı ´so 122 1,9% - - Villa Andrea 113 1,8% - - Palmeras 98 1,6% - - Valencia 88 1,4% 2 2. 7% Santa Teresita 63 1,0% 1 1. 4% Santarem 57 0,9% - - Tarapoto 37 0,6% - - Others 6 0,1% - - � Note that 19/74 (25. 7%) patients were diagnosed with TB both by smear microscopy and culture, while 55/74 (74. 3%) were diagnosed only by culture. ��Peoplewhodo not remember age or date of birth. https://doi. org/10. 1371/journal. pone. 0245084. t001found in the SITVIT2 database, and corresponded to an orphan pattern. 53/74(71. 62%)isolates corresponded to the LAM sublineage and 20/74 (27.
03%) to the T sublineage. SITVIT2 database analysis revealed that 2 (2. 70%) isolates belonged to SIT42/LAM9, 14 (18. 92%)isolates to SIT1355/LAM, 20 (27. 03%) isolates to SIT392/T2 and 37 (50. 00%)isolates to SIT95/ LAM6. SIT392 was probably mislabeled as belonging to the T family due to peculiar absence of spacers(21-29, 33-36, and 40). The spoligotyping pattern designed as an orphan in this study (with a supplementary absent spacer 15) was similar to SIT392. Both spoligotypes could be relabeled as belonging to LAM family (according to MIRU-VNTR analysis). The 24-loci MIRU-VNTR typing revealed that 66/74 (89. 2%) clinical isolates shared 6 iden-.
51, North CO = 30. 95, America = 6. 35 PE = 12. 70, US = 6. 35, GF = 3. 97 392 ◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◽◽◽◽◽◽◽◽◽◾◾◾◽◽◽◽◾◾◾◽◾◾◾ 777777600160731 20 (27. 03) 80. 0 T2 ��� CO = 80. 00, South America = 100. 00 BR = 16. 00, AR = 4. 00 1355 ◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◽◽◽◽◽◾◾◾◾◽◾◾◾◽◽◽◽◾◾◾◽◾◾◾ 777777407560731 14 (18. 92) 7. 04 LAM PE = 81. 41, South America = 89. 45, CO = 7. 04, IT = 5. 03, Southern Europe = 6. 53, North US = 3. 52 America = 3. 52 � The 2 letter country codes are according to https://en. wikipedia. org/wiki/ISO_3166-1_alpha-2; countrywide distribution is only shown for SITs with �3% of a given SIT as compared to their global distribution in the SITVITEXTEND database. �� Worldwide distribution is reported for regions with more than 3% of a given SIT as compared to their global distribution in the SITVITEXTEND database.
The brain and behavior are under energetic constraints, limited by mitochondrial energy production capacity. However, the mitochondria-behavior relationship has not been systematically studied on a brain-wide scale. Here we examine the association between multiple features of mitochondrial respiratory chain capacity and stress-related behaviors in mice with diverse behavioral phenotypes. Miniaturized assays of mitochondrial respiratory chain enzyme activities and mitochondrial DNA (mtDNA) content were deployed on 571 samples across 17 brain regions, defining specific patterns of mito-behavior associations that vary across brain regions and behaviors. Furthermore, multi-slice network analysis applied to our brain-wide mitochondrial dataset identified three large-scale networks of brain regions with shared mitochondrial signatures.
Our study design first aimed to profile animal-to-animal differences in mitochondrial phenotypes across a broad set of brain regions known to be associated with anxiety-related behavior, social behaviors, cognition, or mitochondrial disorders, and to relate these measures to each animal's behavioral phenotypes. In total, we enzymatically and molecularly phenotyped 571 samples covering 17 cortical, sub-cortical and brainstem brain regions isolated by bilateral punches at defined stereotaxic coordinates (Table  1 , Extended Data Fig. 3 ). To eventually compare the specificity of our findings related to brain and behavior, 5 peripheral tissues were also collected and analyzed from each animal, which we expected to show somewhat related but more modest associations with behavioral outcomes.
Brain MHI correlates with specific behaviors We next evaluated the extent to which mitochondrial phenotypes in different brain regions was associated with behavior across all animals (Figure  2a ). Behavioral scores were transformed so that higher scores on each test indicate higher avoidance/anxiety-like behaviors (as in  24  ) (See Methods, Extended Data Fig. 9 ). Behavioral scores were then correlated with the 6 measures of mitochondrial phenotypes, where higher values indicate higher mitochondrial content or RC functioning. A frequency distribution of the effect sizes (Spearman r) for all mitochondrial-behavior pairs revealed a significant non-zero correlation between MHI in the 17 brain regions and behavior on OFT (p<0. 01), EPM (p<0. 0001), and SI (p<0. 0001), but not for NSF (Figure  2b , Gaussian frequency distributions for the other mitochondrial features are shown in Extended Data Fig.
Representing all 17 brain regions, and across 6 mitochondrial features, we found 3 separate communities, or networks: 1) Cortico-striatal network: CPu, visual, motor, mOFC, mPFC and NAc; 2) Salience/Spatial navigation network: cerebellum, vestibular nuclei, VTA, thalamus, hippocampus (CA3), and dentate gyrus (dorsal and ventral); and 3) Threat response network: amygdala, hypothalamus, substantia nigra, and periaqueductal grey (Figure  3f ). To examine whether this functional organization of the brain revealed using mitochondrial phenotypes was also evident cross-modally, we used two Allen Brain Atlas datasets of brain-wide gene co-expression  62  , and EYFP-labeled axonal projections that define the structural connectome  63  , developed in the same mouse strain. Specifically, we examined whether gene expression correlations and structural connectivity within brain regions that are functionally grouped together as a network, based on mitochondrial features, is higher than expected by chance.