dataesr/scientific-paragraphs-categorization huggingface dataset:
2003). We compared our haplotypes with the 854 CSD nucleotide sequences available in the Gen-Bank database for the same region. The analysis of exon 7 and introns 6 and 7 shows that 101 of the haplotypes identified here were new: 50 specific to each of the Algerian and European subspecies and one shared by both. The analysis of the exon 7 sequences revealed 85 haplotypes that had not been previously described: 45 were  found only in Algeria, and 39 in Europe. One haplotype was common to the Algerian and European subspecies.
3 We compared the obtained amino acid sequences with 650 CSD sequences available in GenBank database. Among the 119 haplotypes, 81 haplotypes were newly described, of which 45 found only in Algeria, 36 only in Europe. The remaining 38 haplotypes were identical to sequences previously found in GenBank. The set of new nucleotide sequences identified in this study have been deposited to GenBank (www. ncbi. nlm. nih. gov/ genba nk) nucleotide sequence database and have been assigned the accession numbers (MZ674091-MZ674175). In exon 7, the number of non-synonymous substitutions (N = 80) was much higher than the number of synonymous substitutions (N = 27). Testing for positive selection was done by calculating the Ka/Ks ratio. The number of nonsynonymous substitutions per non-synonymous site (Ka) was equal to 0.
A striking fact is that all 119 nucleotide haplotypes correspond to 119 protein haplotypes, meaning that each nucleotide haplotype has at least one non-synonymous mutation. When comparing our data to the CSD haplotype sequences found in the international databases, forty-five new haplotypes were found only in the 122 samples from Algeria (excluding A. m. sahariensis drones coming from the same hive), which represent 37%, and 36 new European haplotypes from 183 samples, which represent 19%. Therefore, sequencing new subspecies such as A. m. intermissa and A. m. sahariensis contributed to the identification of new haplotypes that had not been studied with European subspecies. However, although it appears that each subspecies has its own set of specific haplotypes, the phylogenetic tree, the haplotype network, and the PCA analyses of the sequences obtained failed to reveal any structure following the expected subspecies pattern.
ACKNOWLEDGEMENTS We thank  Hamzaoui Mohamed  and the  "Association nationale des apiculteurs profesionnels" (ANAP)  for providing honeybee samples from  Medea ,  Annaba , and  El Bayadh  (Algeria);  Saci Soumia  for samples from  Ain Sefra (Algeria) ;  Andrew Abrahams  for samples from  Colonsay (Scotland) ;  Cecilia Costa  for samples from.
The transcript gene expression and the gene ontology analysis associated with the proinflammatory and profibrotic MEFs induced in vitro were consistent with previous data on profibrotic and proinflammatory polarizations of fibroblasts  4, 10, 22. We selected the most representative pathways, chose representative genes of each pathway and combined them to illustrate the proinflammatory and profibrotic polarizations. Second, we assessed the gene signature on dMPF isolated from an experimental murine model of inflammation-mediated fibrosis in a time-dependent manner. Maria et al. showed that daily injections of HOCl triggered inflammation and led to cellular polymorphous infiltrates (inflammatory stage), following by a progressive development of established fibrosis, less inflammatory, and made of disorganized collagen fibers destructuring all skin layers  19.
Cells were pelleted by centrifugation, washed twice, and grown in DMEM containing 10% FBS. Cells were used at the second passage. Histological evaluation. Skin samples embedded in paraffin were sliced into serial 4-μm sections. Dermal thickness at the injection site was assessed by performing a hematoxylin eosin (HE) staining and measuring the distance between the epidermal-dermal junction and the dermal-subcutaneous fat junction at a 20-fold mag-nification using the ImageJ software (U. S. National Institute of Health) (https:// imagej. nih. gov/ ij/ index. html). Twenty random measurements per section were performed by two blinded investigators and averaged for each section. Measurement of collagen deposition. Collagen deposition in the skin was evaluated using Picrosirius red staining. The color was prepared by a 5-min bath of hematoxylin diluted 1/3 in water (Bio Optical).
A 60 min bath of Picrosirius Red, which specifically dyes collagen fibers, was then carried out, obtained by mixing Direct red 80 (Sigma Aldrich) diluted to 1% in saturated picric acid (Sigma Aldrich). The sections were mounted between slide and coverslips and acquired under an automated microscope as described above, then analyzed using the ImageJ software (U. S. National Institute of Health) (https:// imagej. nih. gov/ ij/ index. html). A semi-quantitative analysis of the collagen deposition was performed using a method of Color deconvolution and the area occupied by collagen was quantified (expressed in %). Leukocytes count in skin sample. After collection, the skin biopsies were digested with type II dispase (1 mg/ mL to 1. 88 units/mL) and type IV collagenase (0. 5 mg/mL to 215 units/mL) for 2h30 at 37 °C.
The suspension was then centrifuged at 300 g for 10 min. Cell counting was performed by flow cytometry (NaviosTM, Beckman Coulter) using calibrated beads (Flow-Count Fluorospheres, cat. # 7547053, Beckman Coulter). 50 μL of cell suspension were incubated with 2 μL of anti-murine Fc receptor antibody (Fc block, BD Pharmingen) for 5 min at 4 °C. They were then incubated with 5 μL of CD45-APC (Biolegend) for 20 min at 4 °C. The samples were then incubated with 2. 5 µL of propidium iodide (Bioscience) for 5 min at 4 °C. Data were analyzed using Kaluza Analysis software (Beckman coulter, version 2. 1) (https:// www. beckm an. fr/ flow-cytom etry/ softw are/ kaluza). Evaluation of polarizations of fibroblasts. Gene expression analysis using RNA microarray. Library preparation and data acquisition.
Total RNA yield and quality were assessed on the Agilent 2100 bioanalyzer (Agilent Technologies. Massy, France). One color whole Mouse (074809_D_F_20171030 design) 60-mer oligonucleotides 8 × 60 k microarrays (Agilent Technologies) were used to analyze gene expression. cRNA labelling, hybridization and detection were carried out according to supplier's instructions (Agilent Technologies). For each microarray, Cyanine 3-labeled cRNA were synthesized with the low input QuickAmp labeling kit from 50 ng of total RNA. RNA Spike-In were added to all tubes and used as positive controls of labelling and amplification steps. The labelled cRNA were purified and 600 ng of each cRNA were then hybridized and washed following manufacturer's instructions. Microarrays were scanned on an Agilent G2505C scanner and data extracted using Agilent Feature Extraction Software© (FE version 10.
7. 3. 1). Microarray data have been submitted to the GEO database under the accession number GSE191223. RNA microarray data analysis. We analyzed RNA microarray data using Linear Models for Microarray Analysis (LIMMA) package in R software (version R 3. 6. 0) (https:// www. r-proje ct. org) to identify genes that were expressed differentially between datasets. The threshold for statistical significance was set to q-value (Benjamini-Hochberg false discovery rate (FDR) at < 0. 05. Normalization with Z-score was determined, and hierarchical clustering was constructed with Euclidean distance. Protein identification using LC-MS/MS. Sample preparation and LC-MS/MS Orbitrap eFASP. Samples were prepared using a modified enhanced Filter Aided Sample Preparation (eFASP) in order to increase proteome coverage and sample recovery for quantitative proteomic experiments  41.
LC-MS/MS protein analysis was performed on an Orbitrap Q Exactive plus Mass Spectrometer hyphenated to a U3000 RSLC Microfluidic HPLC System (ThermoFisher Scientific). Detailed procedure is available in the Supplementary file 1. Quantification MaxQuant. Analysis of raw file from LC-MS/MS eFASP digestion data was performed using MaxQuant v1. 5. 3. 30. and Andromeda search engine was used for database searching against the UniProtKB/ Swiss-Prot Mouse database (Mus musculus, January 2018, Sequences: 91. 097). MaxQuant also contains common contaminates proteins identified in proteomics analysis. MaxQuant analysis included an initial search with a precursor mass tolerance of 20 ppm, a main search precursor mass tolerance of 6 ppm and a fragment mass tolerance of 20 ppm, respectively. Trypsin was selected as an enzyme and 3 missed cleavages are included.
Together with variable modifications such as methionine and proline oxidation, deamidation on asparagine or glutamine and serine, threonine phosphorylation, glutamate to pyroglutamate conversion and with the fixed modification carbamidomethyl cysteine. The minimal peptide length was set to six amino acids and the maximum number of missed cleavages to three. The match-between-runs function was used (match time window = 2 min, alignment time window = 20). The FDR was set to 0. 01 for both peptide and protein identifications. The proteins identified by the same sets of peptides were grouped and reported as one protein group. Proteomic data have been submitted to the PRIDE database under the accession number PXD034078. Bioinformatic treatment by Perseus. The statistics were calculated using Perseus software (version 1.
60. 2. Max Planck Institute of Biochemistry, Martinsried, Germany) (https:// maxqu ant. net/ perse us/)  42. The MaxQuant data were filtered for reverse proteins identifications (false positives), contaminants, and proteins "only identified by site". The label free intensities were transformed in log2 then the data were filtered with x valid values in at least one group. The missing data were replaced from normal distribution (width 0. 3. down shift 1. 8). Significant proteins were determined using a two-sample analysis t-test and multiple sample test with Benjamini-Hochberg FDR at 0. 05. Normalization with Z-score was determined, and hierarchical clustering was constructed with Euclidean distance.
25 μg proteins were separated on a 4-12% SDS-PAGE then transferred to nitrocellulose membrane. After blocking for 1 h in 10% BSA in TBS Tween buffer, membranes were probed with the following antibodies specific for collagen I (1:1000, #MA1-26771Thermofischer scientific), α-SMA (1:5000, ab7817, Abcam), decorin (1:400, #AF1060, R&D systems), jagged1 (1:1000, #70109, Cell Signaling Technology Inc. ). Horseradish peroxidase-conjugated secondary antibodies were used at 1:2000 for 1 h then detection was carried out by enhanced chemiluminescence. Optical density of target bands will be determined by using the ImageJ software (U. S. National Institute of Health) (https:// imagej. nih. gov/ ij/ index. html). ELISA assays. CXCL1 and MMP3 protein levels in supernatant samples were assessed in duplicate using ELISA assays (Mouse Total MMP-3 Duoset Elisa, cat.
Ontology annotation and canonical pathway enrichment. To analyze Gene Oncology (GO), REACTOM, and KEGG pathways, we subjected differentially expressed genes (DEGs) (or proteins, DEPs) with cut-off q-value at < 0. 05 to the online tool Metascape (http:// metas cape. org/ gp/ index. html#/ main/ step1)  43. All significant enriched terms (p-value < 0. 05) were hierarchically clustering into a tree based on Kappa-statistical similarities (threshold > 0. 3) among their gene membership. The clusters identified have been named according to the membership. Among the terms within each cluster, some of them (q-value < 0. 05) were selected for charting. Molecular signatures database (MSigDB) (http:// softw are. broad insti tute. org/ gsea/ msigdb/ index. jsp) and Ingenuity Pathway Analysis (Qiagen, Inc) (https:// digit alins ights.
Data availability Transcriptomics datasets related to this article can be found at https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE19 1223, an open-source online data repository hosted at NCBI/GEO. GEO accession number: GSE191223. Proteomic data have been submitted to the PRIDE database under the accession number PXD034078.
Materials and Methods Participants. Fifty-four healthy adult German speakers were included in the analyses. This sample consisted of 26 participants with DD and 28 control participants, matched in age, sex, handedness, and nonverbal IQ (SI Methods and Table  S1 ). Participants with DD performed worse than controls on tests of literacy (spelling, reading speed and comprehension), rapid automatized naming of letters and numbers (RANln), and word and non-word reading (SI Methods and Table  S1 ). acquired additional fMRI and diffusion-weighted imaging data from the participants, the results of which will be reported elsewhere. MRI Experiments: Setup. In each fMRI experiment, visual stimuli were front-projected onto a translucent screen positioned on the participants' chest. Participants viewed the screen in the MRI system through a mirror mounted just above their eyes.
During the fMRI experiments, we also recorded cardio-respiratory data from the participants. This was done to account for physiological noise in the BOLD signal during data processing in order to increase the signal-to-noise ratio in the LGN  (31). For more details on display settings, visual stimulation software, and physiological recordings, see SI Methods. Experimental Design: LGN Localizer. This experiment was used to functionally localize the LGN in each participant (14) (Fig. 1A ). The stimulus consisted of a flickering radial checkerboard with 100% contrast, with its contrast polarity reversed at 4Hz (for the full cycle). The checkerboard covered half the screen while the other half contained a uniform gray background. The checkerboard alternated between the two visual hemifields in a block-design fashion.
Throughout all blocks, participants were instructed to maintain fixation on a central gray fixation point (0. 2° of radius) while viewing the stimuli. Each block lasted 16 s and a run was composed of 8 blocks of each type (i. e. , motion and static) for a total of 16 blocks. Participants completed one run which lasted ~5 mins. High-Resolution MRI Data Acquisition. High-resolution functional and structural MRI data were acquired on a 7 Tesla Magnetom MRI system (Siemens Healthineers, Erlangen, Germany) equipped with a 32-channel head coil (Nova Medical, Wilmington, MA, USA). In Definition of M-and P-LGN. M-and P-LGN were defined using the M/P mapping experiment as previously described (  14 ): For each participant, we computed Beta M-P maps in native space by subtracting the Beta maps obtained from the general linear model (GLM) estimation corresponding to the M-and P-stimulus conditions of the M/P mapping experiment, respectively.
To do this, we computed individual M/P subdivision maps also in MNI standard space. This step permitted comparability between participants by aligning all LGNs in a common reference space. MNI Beta M-P maps were masked by a probabilistic LGN atlas (12, in MNI 1mm standard space) (Fig. 2A ) and the same 20/80% volume allocation criterion was applied to define M-and P-LGN maps, respectively (Fig. 2B ) (SI Methods). The MNI standard space analysis only subserved the quality control analysis of the spatial configuration of LGN-M/P maps. All reported quantitative analyses on the M/P-LGN localization accuracy between groups are based on the data in participants' native space. Extraction of signal change. Beta estimates corresponding to the conditions of interest were extracted from all voxels within the LGN (i.
e. , for the LGN localizer experiment) or the M-and P-LGN (i. e. , for the M/P mapping and motion experiments) in each participant using an in-house toolbox and converted to % signal change. The % signal change (PSC) was computed as: PSC = βcondi tion x SF / βconstant x 100 (wherein: βcondi tion = parameter estimate of the condition of interest, βconstant = parameter estimate for the constant term, SF = scale factor of the design matrix)  (34). Finally, the mean PSC within each region was extracted for each participant and experimental condition of the fMRI experiments and subjected to mixed-design ANOVAs for statistical analysis (SI Methods). For the motion experiment, we used the M-and P-LGN to mask responses in the contrast motion vs. static. Data Availability. The scripts used to generate the LGN hemifield and M/P stimuli are publicly available  (14).
The motion experiment and fMRI analysis scripts have been made publicly available on the Open Science Framework (osf; https://osf. io/bge75/). Raw MRI data cannot be made available as sharing these personal data is not covered by the ethics clearance. Single-subject data in native and MNI space (i. e. , individual LGN, M-and P-as well as beta M-P maps) are available on osf. degrees of visual angle. Stimuli were generated on Linux using the Psychtoolbox  (12, 13) , implemented in GNU Octave, version 4. 2. 0  (14) , and presented at a refresh rate of 60 Hz. Physiological Data Recordings. We recorded participants' cardio-respiratory data throughout each fMRI experiment using an MRI-compatible Biopac System (Biopac Systems, Inc. , Goleta, CA, USA). Cardiac signals were recorded through a pulse oximeter placed on participants' left index finger with a sampling frequency of 100Hz.
Respiratory data were recorded through thoracic movements using a non-electrical pressure pad placed on participants' chest in combination with a respiration transducer. MR trigger pulses were also recorded to synchronize physiological parameters to each MR volume. The reduction of the flip angle in these participants was well within the normal range of actual flip angle variation throughout the brain at 7 Tesla  (15). In addition, we acquired one whole-brain EPI image with matching parameters to facilitate registrations between the functional and structural MRI data. To correct images for geometric distortions induced by magnetic field inhomogeneity, in each MRI session we acquired two gradient-echo datasets (ΔTE = 1. 02 ms) from which session-specific B0 field-maps (voxel displacement) were computed.
The whole-brain EPI was then also co-registered to this volume and subsequently used as the reference image for registering the structural to the functional data. Unwarped functional data in native space were then smoothed with a Gaussian filter with a FWHM (full width at half maximum) matching the voxel size (i. e. , 1. 25 x 1. 25 x 1. 2 mm). Times-series of each voxel were high-pass filtered (1/128 Hz cutoff) to remove lowfrequency noise and signal drift. The resulting images were used for the definition of LGN M/P subdivisions and the ROI analyses (see 'fMRI Data Analysis' and 'ROI Analyses'). As part of the quality control analysis for the M/P subdivisions in native space, the unwarped functional data were also normalized into standard space (MNI, Montreal Neurological Institute). For this, the anatomical image was segmented into six tissue probability maps (gray matter, white matter, cerebrospinal fluid, soft tissue, bones, image background).
These tissue class images were then non-linearly registered to the 1mm MNI brain template and derived registration parameters were applied to the functional data. The registered functional data were then smoothed with a Gaussian filter with a FWHM matching the voxel size. Finally, times-series of each voxel were high-pass filtered at a 1/128 Hz cutoff. Head Motion. Head motion was assessed by computing the maximum translational and rotational displacements across each run of each fMRI experiment from the 6 motion parameters (3 translation, 3 rotation) obtained from SPM  (17). Maximum translational displacements (TD) corresponded to the maximum difference between total TDs calculated as the square root of the sum of squared x, y, and z-direction displacements. Maximum rotational displacements (RD) corresponded to the maximum difference between total RDs calculated as the sum of the absolute RDs in the three directions.
Physiological regressors also included models of heart rate variability  (21)  and respiratory volume per time  (22). Including such models of physiological noise and motion parameters as nuisance regressors has been shown to substantially increase the signal-to-noise ratio in the LGN at 7 Tesla  (23). Due to technical problems, physiological parameters of 6 participants could not be acquired and were not considered in the respective design matrices. LGN Definition. To segment the LGN in each individual participant and to demarcate it from adjacent visual brain structures, we leveraged a publicly available, high-resolution 7T probabilistic LGN atlas  (24). This atlas is available in high-resolution 0. 4 mm template space as well as in MNI 1mm standard space  (25). To map the bilateral LGNs in each participant's native space, we used the command antsRegistration as implemented in the Advanced Normalization Tools (ANTs) software package  (26) , to register the highresolution LGN atlas template to each participant's T1 image.
Acknowledgments and Funding Sources. We are grateful to our participants for taking part in this study. We also thank the  German federal association for dyslexia and dyscalculia (Bundesverband Legasthenie Dyskalkulie  e. V. , https://www. bvl-legasthenie. de/)for their continued support in advertising our study to potential participants. We further thank  Martina Dietrich ,  Liane Dörr ,  Laura Hüser ,  Kim Lawatsch ,  Lisa Ziggel ,  Hanna Ovesiek , and  Tabea Fleps  for their extensive help in scheduling and conducting the behavioral tests. This work was supported by the  ERC -consolidator-grant  SENSOCOM 647051.
Data Sources We used data from the Japan COVID-19 and Society Internet Survey (JACSIS) conducted in 2020. The JACSIS 2020 is a cross-sectional, internet-based, and self-reported questionnaire survey administered by an internet research agency with 2. 2 million qualified panelists in Japan (Rakuten Insight). The survey panel was recruited through services managed by the research agency group. The inclusion criteria of the survey panel were people who agreed to (1) provide their information, including sex, age, occupation, and residence, and (2) participate in different research surveys in the future. The panelists comprised individuals from diverse socioeconomic backgrounds, such as household income, educational level, and marital status. The research agency randomly sampled 224,389 people from this panel stratified by age, gender, and living prefecture to request participation in the JACSIS 2020 survey by email.
Thus, we adopted multilevel analysis and considered the prefectures each participant lived in as the second-level variables. All analyses were conducted using Stata software (version 16. 0; StataCorp LLC), including the mepoisson command. We incorporated random intercepts at the prefecture level. We conducted subgroup analyses using the same model by age group (aged >65 years and ≥65 years) and gender. To discover the between-group difference, we first estimated the point estimate of aPR for loneliness with 95% CI in each subgroup and calculated P value for interactions using the Altman method  [29]. We used 65 years as a cutoff for age groups based on previous studies and reviews  [30, 31] , as most Japanese people retire at age 65 years  [32] , which can yield lifestyle differences between the groups, such as communication frequencies, communication partners, and how they use technology-based communication tools.