dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, when our mosquito specimens were parasitized by other insects, it was not possible to bioinformatically filter out rRNA reads belonging to the parasite. For these rare cases, we used the " --trusted-contigs" option of the SPAdes assembler  (Bankevich et al. , 2012) , giving it access to the 28S and 18S rRNA sequences of the mosquito closest in terms of taxonomic distance. By doing this, the assembler was able to reconstruct the rRNA of the mosquito as well as the rRNA of the parasitizing insect. All assembled rRNA sequences from this study have been deposited in GenBank with accession numbers OM350214-OM350327 for 18S rRNA sequences and OM542339-OM542460 for 28S rRNA sequences.
COI sequence analysis Forward and reverse COI DNA sequences were end-trimmed to remove bases of poor quality (Q score < 30). At the 5' ends, sequences were trimmed at the same positions such that all forward sequences start with 5'-TTTTGG and all reverse sequences start with 5'-GGNTCT. Forward and reverse sequences were aligned using BLAST to produce a 621 bp consensus sequence. In cases where good quality sequences extends beyond 621 bp, forward and reverse sequences were assembled using Pearl (https://www. gear-genomics. com/pearl/) and manually checked for errors against trace files  (Rausch et al. , 2019 (Rausch et al. , , 2020)). We successfully assembled a total of 106 COI sequences. All assembled COI sequences from this study have been deposited in GenBank with accession numbers OM630610-OM630715.
COI validation of morphology-based species identification We analysed assembled COI sequences with BLASTn against the nucleotide collection (nr/nt) database to confirm morphology-based species identification. BLAST analyses revealed 32 cases where top hits indicated a different species identity, taking <95% nucleotide sequence similarity as the threshold to delineate distinct species (Appendix 2-table 1). In these cases, the COI sequence of the specimen was then BLAST-aligned against a GenBank record representing the morphological species to verify that the revised identity is a closer match by a significant margin, i. e. , more than 2% nucleotide sequence similarity. All species names reported hereafter reflect identities determined by COI sequence except for cases where COI-based identities were ambiguous, in which case morphology-based identities were retained.
In cases where matches were found within a single genus but of multiple species, specimens were indicated as an unknown member of their genus (e. g. , Culex sp. ). Information of the highest-scoring references for all specimens, including details of ambiguous BLASTn results, are recorded in Appendix 2-table 1. Within our COI sequences, we found six unidentified Culex species (including two that matched to GenBank entries identified only to the genus level), four unidentified Mansonia species, and one unidentified Mimomyia species. For An. baezai, no existing GenBank records were found at the time this analysis was performed.
Phylogenetic analysis Multiple sequence alignment (MSA) were performed on assembled COI and rRNA sequences using the MUSCLE software  (Edgar, 2004; Madeira et al. , 2019). As shown in Figure  3 -figure supplement 2, the 28S rRNA sequences contain many blocks of highly conserved nucleotides, which makes the result of multiple alignment particularly obvious. We therefore did not test other alignment programs. The multiple alignment of the COI amplicons is even more evident since no gaps are necessary for this alignment. Phylogenetic tree reconstructions were performed with the MEGA X software using the maximumlikelihood method (S. Kumar et al. , 2018). Default parameters were used with bootstrapping with 500 replications to quantify confidence level in branches. For rRNA trees, sequences belonging to an unknown species of parasitic water mite (genus Horreolanus) found in our specimens served as an outgroup taxon.
In addition, we created and analysed a separate dataset combining our 28S rRNA sequences and full-length 28S rRNA sequences from GenBank totalling 169 sequences from 58 species (12 subgenera). To serve as outgroups for the COI tree, we included sequences obtained from GenBank of three water mite species, Horreolanus orphanus (KM101004), Sperchon fuxiensis (MH916807), and Arrenurus sp. (MN362807).
with three water mite species to serve as outgroups. Outgroup sequences obtained from GenBank are annotated with filled circles and their accession numbers are shown. Values at each node indicate bootstrap support (%) from 500 replications. Each specimen label contains information on taxonomy, origin (as indicated in 2-letter country codes), and specimen ID. Specimen genera are indicted by colour: Culex in coral, Anopheles in purple, Aedes in dark blue, Mansonia in dark green, Limatus in light green, Coquillettidia in light blue, Psorophora in yellow, Mimomyia in teal, Uranotaenia in pink and Eretmapodites in brown. Scale bar at 0. 05 is shown.
Acknowledgements We thank members of the  Saleh lab  for valuable discussions and to  Dr Louis Lambrechts  for critical reading of the manuscript. We especially thank all medical entomology staff of  IP Bangui, IP Cambodge (Sony Yean ,  Kimly Heng ,  Kalyan Chhuoy ,  Sreynik Nhek ,  Moeun Chhum ,  Kimhuor Sour  and  Pierre-Olivier Maquart ),  IP Madagascar , and  IP Guyane  for assistance in field missions, laboratory work, and logistics, and  Inès Partouche  from  IP Paris  for laboratory assistance. We are also grateful to  Dr Catherine Dauga  for advice on phylogenetic analyses, and to  Amandine Guidez  for providing a French Guiana-specific COI reference library. Finally, we thank our reviewers, including  Leslie Vosshall  and  Katherine Young , for their constructive reviews of this manuscript.
Availability of data and materials The multiple sequence alignments that gave rise to the presented phylogenetic reconstructions are included as source data files linked to the relevant figures. All sequences generated in this study have been deposited in GenBank under the accession numbers OM350214-OM350327 for 18S rRNA sequences, OM542339-OM542460 for 28S rRNA sequences, and OM630610-OM630715 for COI sequences (Appendix 1-table  1 ).
Research in context Evidence before this study Research predominantly conducted in high-income countries has shown a high prevalence of geriatric syndromes in older people living with HIV, as well as an early presentation of these conditions. Consequently, efforts to study this issue indepth, and to provide better services to a population with specific needs, are already underway. However, in low and middle-income countries, where this phenomenon is probably also occurring, there are very few studies documenting it, and there is insufficient awareness in the social and health services to address it. In this sense, we conducted a PubMed database search without language restriction for studies evaluating geriatric syndromes in people with HIV published between April 1, 2013, and April 1, 2023.
Statistical analysis Continuous data is represented as either means (standard deviation) or medians (interquartile range), based on the distribution determined by the Anderson-Darling normality test. In the case of categorical variables, the absolute frequency and associated percentage are provided. Statistical analyses were carried out using R Studio (Version 4. 2. 1). We used the mice R Package (Version 3. 14. 0) to impute continuous missing values, assuming the missing data are at random. The imputation was done using multiple chained equations, and five imputed datasets were created. 19 The imputed datasets were combined using Rubin's rules for a maximum of five iterations. The percentage of missing values for continuous variables, as well as density histograms and summary statistics for both the original and imputed variables are reported in Supplementary Material-Figure  S5.
Association of AICGS and HIV assessment First, to evaluate the correlation of the AICGS scale with the normalized transformation of the last measurement of CD4+, and CD4+ cell nadir counts, the Pearson product-moment correlation coefficient was used. The differences between AICGS scale and the categorized VL were assessed using a Mann-Whitney U test. To visualize the relationship of both variables, we plotted dispersion and boxplots graphs using ggplot 2 package (Version 3. 3. 5). Then, to assess an association of our HIV assessment, we fitted linear regression model using the AICGS scale as dependent variable and as our independent variables the normalized values of the last count of CD4+, CD4+ cell nadir along with the VL, sex, education, and the number of comorbidities.
K-mean cluster analysis To test whether the accumulation of GS could be the result of an adverse clinical profile related to HIV trails independently of chronological age, we performed kmean clustering analysis. Variables for our clustering model were selected on the premise that the AICGS scale along with the current and CD4+ cell nadir counts would help us to better characterize subjects that have different geriatric syndrome and HIV traits. The cluster analysis was performed using a k-means algorithm with standardized centered values with a mean value of 0 and a SD of 1. The kmeansruns function was tested using a k value of 3 and 4 with a 100-run selection from the fpc package (Version 2. 2. 9). Then, cluster selection was assessed by resampling the dataset 100 times and computing the Average Silhouette Width and the Calinski-Harabasz criterion.
Acknowledgements Funding: This work was funded in part by the  National Center for the Prevention and Control of HIV/AIDS in Mexico (CENSIDA)-National Ministry of Health. Project number:  Proy2da-2017-0033. The funding source had no role in the design, analysis, and data interpretation, writing, or reporting of the manuscript. NEAV and VHR were supported by the  National Council for Science and Technology (CONACYT)  in Mexico.
Analysis The thicknesses of the atrial wall and its constituent sub-layers (endocardium, myocardium, and epicardium) were manually measured at multiple evenly distributed locations (N=15). Differences in thickness in both control and diseased hearts, and in right vs left atrium were compared by Student's t-test. Normality of data was assessed with a Shapiro-Wilk test. Assessment of the orientation of aggregates of myocytes in a mid-ventricular slice of the control heart, and of 3D mapping of the extent of mitral annular disjunction were performed as described in Supplemental Methods.
c) Vascular exemplar: The coronary arterial and coronary venous tree The volumetric nature of HiP-CT data allowed for 3D visualisation of vascular anatomy without the need for further sample preparation or vascular casting. Full-field images of the whole heart allowed segmentation of the coronary arterial and venous systems down to pre-arteriolar and pre-venular level (Fig. 6d ). Even small vessels, such as arteries supplying and branching from the sinus and atrioventricular nodes, as well as smaller arteries (0. 25-0. 75mm diameter) were clearly visible for segmentation in 20um/voxel datasets (Fig. 6c, f ). The 2. 2um/voxel local tomography scan enabled the observation of arterioles down to pre-capillary level including micro-vasculature within the SAN, AVN (Fig 4 ), and penetrating bundle of His (data not shown).
Potential for resolving current anatomy controversies Multiple challenges and controversies still exist in the field of cardiac anatomy which HiP-CT has the potential to resolve. For example, comprehensive analysis of the cardiac conduction system for the interpretation of arrhythmia  (34). Tracing normal and abnormal pathways or tracts between the SAN and underlying working myocardium have proven to be an arduous task and only possible by destructive serial sectioning. Our analysis shows that they have the potential for being traced in HiP-CT data, that connections surrounding the SAN are likely multiple, and are present, but attenuated, in the setting of increased adiposity. Likewise, we were able to track the course of the AVN and its vasculature supply in our datasets and show that it ran in close proximity to the overlying right vestibular myocardium and atrial septum in the control heart.
This pattern, along with potential connections with the atrial septum (fast pathway), was disrupted by fatty infiltration in the diseased heart. Given the well-known association between obesity and arrhythmia(35) these findings are not only relevant to the onset of cardiac rhythm abnormalities but also to the efficacy of ablation strategies to cure them  (36). Furthermore, connections between AVN and ventricular myocardium (nodo-ventricular connections), or between the His bundle (fasciculo-ventricular) or bundle branches and the ventricular septum are notoriously challenging to document histologically, not least because sections are often lost or omitted, even with expert serial sectioning. Again, we were able to trace these connections uninterrupted in the 3D datasets. Of note, we show a novel association between such connections and the vascular supply branching from the AV nodal artery which warrants further analysis in larger numbers of hearts.
Acknowledgements We thank  S. Bayat  ( INSERM ) for help during the test phase,  P. Masson  ( LADAF ) for dissections of donors' bodies,  H. Dejea  ( ESRF ) for help during the fiber orientation algorithm development,  H. Reichert  ( ESRF ) for general support of the project, and  E. Boller ,  C. Muzelle ,  R. Homs ,  C. Jarnias ,  F. Cianciosi ,  P. Vieux ,  P. Cook ,  L. Capasso  and  A. Mirone  for their help in setup developments and improvements. We also thank  R. Engelhardt ,  A. Muller Brechlin ,  C. Petzold ,  N. Kroenke  and  M. Kuhel  for help with histology and autopsies. This project has been made possible in part by  CZI  grant  DAF2020-225394  and grant DOI https: // doi. org /10. 37921/331542rbsqvn  from the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation  (funder  DOI 10.
Overview of results Here, we advanced from our previous microbiome profiling (with 16S rRNA amplicon sequencing) to understand the temporal dynamics of the epiphytic phyllosphere microbiome of the perennial biofuel feedstocks miscanthus and switchgrass  32  to now investigate the functional genes and seasonal activities of the focal phyllosphere populations. In our previous study, we identified "core" members of these microbiomes based on their temporal persistence and occupancy. Here, we assembled metagenome-assembled genomes to focus analysis on the most persistent and abundant populations and their seasonal dynamics, and then subsequently recruited metatranscripts to the focal MAGs to understand their seasonal transcript dynamics for switchgrass. We first report overlap between the two different datasets in the detection of "core" and "focal" populations and their taxonomy, then move to describe the general seasonal dynamics of MAGs and their seasonal transcript dynamics, and finally explore the consistent pathways detected and activated across focal MAGs that may be suggestive of a leaf-associated and/or host-responsive lifestyle.
MAGs were detected in metagenomes from different field sites, crops, and years We next asked if these focal MAGs were detected more broadly in other crop metagenomes. Because leaf metagenomes were not available, we obtained related soil metagenomes from local cropping systems in Michigan and also from the publicly available data within the Integrated Microbial Genomes (IMG) database, which serves as a repository for the Joint Genome Institute's sequencing efforts through the U. S. Department of Energy. We were surprised to find that, for each metagenome investigated, reads could be mapped to this set of MAGs. This included soil metagenomes from both switchgrass and miscanthus fields in three locations (Iowa, Michigan, and Wisconsin) and sampled across five different years (Fig. 8 ). These results suggest that the detected and analyzed MAGs can be widely distributed in midwestern agroecosystems and potentially of general importance for perennial crop environments.
Source data are provided as a Source Data file. hypothesized to be important survival strategies of microorganisms in the phyllosphere, with supporting evidence from isolate genome analyses  44. Here, we show both trehalose and betaine to be prominent among MAG populations and activated consistently on the leaf surface, suggesting that they are not seasonally activated but rather necessary for the leaf-associated lifestyle. In bacteria, trehalose metabolism prevents cellular overflow metabolism and carbon stress by redirecting glucose-6-phosphate from conversion to pyruvate  42. Trehalose biosynthesis is common among bacteria and archaea that live in arid, saline, thermal, or seasonally dry environments (e. g. ,  [45] [46] [47]  ) and it has also been reported to be induced in a pseudomonad by ethanol  48  , which can originate inside plant cells  49  or more generally in the roots (e.
These data suggest that S28 has several mechanisms to engage or respond to the host via phytohormones. Next, MAG M9, identified as Hymenobacter, was of interest because it was associated with the most numerous taxonomic group detected in our prior 16S rRNA gene survey  32  and not among the most typically investigated phyllosphere lineages in the literature. While M9 populations were first detected early in the season, their transcripts were enriched in the late season (Figs. 4 and S2 Group 5). MAG M9 had detected and activated galactonate, N-acetylglucosamime, and lactose metabolisms, which were not common among the focal MAGs. It also had activated benzoate, curcumin, and putrescine degradation, as well as cyanide detoxification, type VI secretion, and dihydrogen oxidation. While M9 also had some pathways that were common among these MAGs (e.
Our work is an untargeted bacterial metatranscriptomic work performed on the leaf phyllosphere of field-grown crops. Other recent leaf metatranscriptome studies have focused the viral communities of tomato and pepper  82  , soybean  83  , and rice  84. A key strength of this work is the challenging integration of phyllosphere metagenome and metatranscriptome data, leveraging the higher coverage of the metagenomes with the activity information available from the metatranscriptomes. Despite the relatively limited coverage of the MAGs (due to substantial host and ribosomal DNA contamination), the analysis proved successful by integrating both datasets and focusing on genome-centric interpretation. Thus, there are likely many more prevalent and functionally active populations of the phyllosphere that were not captured in this study, including those players known to be key in the phyllosphere (e.
1 , Supplementary Data 1, Supplementary Data 2). We collected leaves for RNA isolation at three phenology-informed switchgrass time points in 2016 (emergence, peak growth, and senescence) according to GLBRC standard phenology methods [https://data. sustainability. glbrc. org/protocols/165] to assess the potential for sufficient mass and quality RNA extraction from the switchgrass leaf surface, and then expanded to include leaves from all switchgrass sampling time points in 2017. Leaves for RNA isolation were flash-frozen in liquid nitrogen immediately and stored at -80 o C until processing.
Quality filtering of metagenomes and metatranscriptomes We proceeded with bioinformatic analysis (Fig. 2 ) of 192 metagenome (Supplementary Data 1) and 78 metatranscriptome (Supplementary Data 2) observations that met JGI standards for raw data quality based on the Illumina proprietary software. We used Trimmomatic (v0. 39)  88  to remove adaptors and filter low-quality reads from fastq files using the following arguments: P. E. -phred33 ILLUMINACLIP:TruSeq3-PE-2. fa:2:30:10:8:TRUE LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. After assembly, plant host reads were filtered out (for both metagenomes and metatranscriptomes) by removing all reads that mapped to the switchgrass genome (Panicum virgatum v1. 0, DOE-JGI, http://phytozome. jgi. doe. gov/) or miscanthus genome (Miscanthus sinensis V7.
1 http://phytozome. jgi. doe. gov/) using bowtie2 (v 2. 4. 1), samtools (v 1. 13), and bedtools (v2. 30. 0)  [89] [90] [91]  (Fig. S1 ). To remove the fungal reads and improve the prokaryotic signal in metagenome samples, we also filtered reads against seven fungal genomes 92-95 that represent close relatives of the most abundant fungal species in this system that we assessed and reported in our prior work  96  (Table  S1 ). The genomes of these fungal species were retrieved from the JGI Genome Portal or GenBank.
1. 13 with the lineage_wf option)  100. Among a total of 238 MAGs assembled from the switchgrass or miscanthus phyllosphere metagenomes (Supplementary Data 3), we selected a subset of MAGs based on: completeness greater than 50% and contamination less than 10%. We identified replicated bins associated with MAGs using dRep (v3. 2. 0, Olm et al. 2017), resulting in the removal of a single MAG. An additional MAG was removed due to insufficient read recruitment in metatranscriptomes (described below) for a final total of 40 focal MAGs, including seven high-quality and 33 medium-quality  38  (Supplementary Data 3). Read recruitment was performed to the 40 focal MAGs  38, 101. The metagenome abundance of contigs in MAGs in each was estimated based on the median coverage of filtered metagenome reads associated with each MAG contig.
Specifically, Bowtie2 (v2. 2. 2) was used to align reads to all focal MAG contigs (using the default setting and allowing for a single read to map to only one reference). Bedtools (v2. 28) was used to estimate the coverage of each base pair within the contig. The estimated abundance of a contig was based on the median basepair coverage of all reads mapped to the contig, and the estimated abundance of a MAG was based on the average median coverage of all contig within its bins (Supplementary Data 4). The metatranscriptome abundance was estimated based on protein-encoding genes identified in MAGs. For each MAG contig, open reading frames (ORFs) and functional genes were identified using Prodigal (v2. 6. 3, default parameters). Transcripts were mapped to ORFs associated with each MAG to estimate the median base coverage of each ORF (Bowtie2, default parameters, no multiple mappings allowed).
) For metatranscriptome analyses, only ORFs with the top 75% of observed abundances and detected in at least 10% of samples were considered. Functional annotation of ORFs in focal MAGs was done with the DRAM tool (v1. 1. 1  104  , using UniRef90, MEROPS, PFAM, dbCAN-HMMdb (v8) databases (all compiled with DRAM on February 12, 2021), and the KEGG database which we manually added to the DRAM pipeline (release January 1, 2018). To obtain functions related to terpene metabolism, ORFs were selected that were associated with any KEGG annotation that contained the phrase "terpen. " MAGs were assigned taxonomy using GTDB-tk (v1. 4. 0,  105  ). Assembled focal MAGs were aligned against the chloroplast genome of Panicum virgatum (NC015990) (BLAST, 2. 10. 1). We detected partial matches to eight bins and no full alignments, confirming that those bins were not chloroplast genomes.
Additionally, we compared the focal MAGs taxonomy to our previously detected 16S rRNA gene core cohort  32. This cohort consists of 61 phylogenetically diverse bacterial OTUs (97% clustering). The taxonomy of these 61 OTUs was compared with the 40 MAGs at the genus level. Due to differences in nomenclature between the GTDB and SILVA databases, we removed extensions for MAGs classified as Pseudomonas_E or Aeromonas_A.