dataesr/scientific-paragraphs-categorization huggingface dataset:
Water Management In addition to the results of the questionnaire as presented above, we also collected data through interviews with key actors in Bandar Lampung and Lyon cities. Here are the excerpts of the interviews. Bandar Lampung's Department of Communication and Information continues to support the realisation of the smart city programme, which was launched in mid-2019. Later, everything will be integrated (Key Actor 1, Bandar Lampung). Additionally, the Smart City concept promotes a more sustainable environment through the use of advanced waste and water management concepts (Key Actor 2, Bandar Lampung). We are currently expanding the network of internet infrastructure in 20 subdistricts and 126 urban villages. Additionally, we will develop an infrastructure network at the Bandar Lampung City Secretariat, consisting of approximately 1,000 metres of fibre optic cable, in order to establish a command centre and data centre (Key Actor 3, Bandar Lampung).
Flow cytometry and antibody list Cells were resuspended in the Grex plate and 50-100 uL cell suspension was transferred to a 96well U-bottom plate. Cells were washed with 150 uL FACS buffer (PBS + 2% FBS + 2 mM EDTA) and centrifuged for 2 minutes at 400 × g. Cells were suspended in 100 µL of FACS buffer containing appropriate cocktail of antibodies, stained for 20 minutes in the dark at 4°C and washed with 150 µL of FACS buffer. After the wash step, cells were suspended in 150 µL of FACS buffer for flow cytometry analysis. Samples were acquired using a Cytoflex flow cytometer (Beckman Coulter) and the data was analyzed using FlowJo version 10. 6. 0. Luminescence background was assessed in wells containing only T cells or media and was found to be negligible. Relative luminescence units (RLU) were converted to percentage of lysed target cells using the formula 100 × [1-(RLUtest/RLUcontrol)].
All sensorgrams were double-referenced with the data from the buffer analyte injections  (Myszka, 1999). Data was fitted to a 1:1 Langmuir binding model with mass transport using Biacore T200 Evaluation Software (version 2. 0). All CD70 antigen single point mutation samples were site-specifically biotinylated via coexpression with BirA, followed by dialysis and filter prior to SPR analysis. All these biotinylated CD70 samples were captured as neat supernatants on the Biacore's CAP surface. All flow cells were then blocked with 20µM Amine-PEG2-Biotin (APB). Buffer and 100nM of all eight anti-CD70 Fabs were injected as analytes for 2 minutes and dissociation was monitored for 15 minutes at 30µL/min. Surface regeneration and data analysis was the same as the kinetic analysis of anti-CD70 Fabs as described above.
N Antigen Detection by Lateral Flow Immunoassays The LFIA COVID-VIRO (AAZ, Boulogne-Billancourt, France), designed for the detection of SARS-CoV-2 N antigen in NP samples, was used by adding 50 µL of NP samples to 50 µL of buffer provided in the kit. Then, the whole mix was loaded onto the cassette. The COVID-VIRO assay was also used to detect N antigen from serum samples, with minimal modification: 100µL of serum were used on the LFIA instead 50 µL of NP sample. LFIA assays were conducted retrospectively with personnel blinded to all other test results and according to manufacturer instructions. NP samples were removed from -80 • C storage ≤1 h and placed at room temperature. No significant change in the test performances was observed using NP samples before and after, without freezing and thawing (data not shown).
For 18 of these patients, serially taken serum samples (over one-month period) were available to observe both the persistence of antigens in the serum and match it with the seroconversion by using the LFIA COVID-PRESTO (Figure  1 ). All results obtained by LFIA were confirmed by ELISA (data not shown). Thus, we noted that until the fifth day after symptoms onset, all the patients had antigens in the serum. Then, the number of positivity decreased until day 13 when the seroconversion turned maximum.
6. 0 [41]   importance of oviposition site availability because burrow presence and abundance are easy to measure empirically. In addition, 141 species of mosquitoes are dependent on crab burrows for oviposition globally, making our findings widely applicable  [34] [35] [36]. Our methodological approach had two major objectives: 1. Estimate spatial heterogeneity in C. carnifex burrow density Field sampling was conducted to characterise the distribution of C. carnifex burrows on motu Tautau. Field data were used to train and validate machine-learning algorithms. Machine-learning algorithms were applied to remote sensing data to estimate the spatial distribution of C. carnifex burrows used by Ae. polynesiensis for oviposition.
Field sampling Adult mosquito trapping data from BG-Sentinel (BGS) traps using BG-Lures (Biogents AG, Regensburg, Germany)  [39, 42, 43]  were available weekly between 20th August 2018 and 15th October 2018 at 12 locations on motu Tautau, collected by Institut Louis Malardé, Tahiti, French Polynesia. Within their protocol, traps were hung from tree branches between 20-50 cm from the ground. Engine grease was applied to the rope to prevent ant predation. Traps were run for the same 24-hour cycle weekly. Collected mosquitoes were frozen, identified and counted by sex and species using taxonomic keys  [44, 45]. Exploitation of C. carnifex burrows by Ae. polynesiensis for oviposition was confirmed by burrow extraction. The distribution of copepods was estimated from extracted samples. Copepods predate heavily on mosquito larvae and have been suggested as a control strategy  [46] [47] [48] [49] [50].
Remote sensing Eleven-band remote sensing data from 16th July 2018 (date closest to field sampling (November 2018) with < 20% cloud cover) were downloaded from NASA's Landsat 8. Data were pre-processed using QGIS version 2. 18 and the SCP plugin  [51]. Dark Object Subtraction (DOS1) atmospheric correction was applied to obtain surface reflectance  [52]. Pre-processed data were cropped to the study area, which was -151. 574 to -151. 556 longitude and -16. 614 to -16. 597 latitude. For land cover classification, three land type classes -easily identifiable from maps -were added to those from cartographical data to account for ocean water types: deep ocean, lagoon and exposed coral reef. A spectral signature using the 11-band Landsat 8 raster stack was estimated for each land cover class from collected field data using the SCP plugin.
Spatial distribution of oviposition sites Random forest machine learning algorithms were used to predict C. carnifex burrow density on motu Tautau from 11-band Landsat 8 data using the randomForest package for R version 3. 5. 2 [41, 55, 56]. Individual regression trees within a single random forest model use m bootstrapped samples with replacement of the training data, D, and each split uses a subset of the predictor variables  [56]. As such, random forests internally cross-validate and reduce individual variable dominance and over-fitting. Random forests were particularly appropriate for the given study because they do not require prior information about the association between Landsat 8 data and C. carnifex burrow presence or density. Model hyper-parameters and further details of the random forest methodology are given in Supplementary Material S.
Subscript ,  indicates the value of a parameter in cell (, ). All model parameters, their values and sources are shown in Table  1. 𝜎 𝑖,𝑗 = { 1 𝑖𝑓 𝛼 𝑖,𝑗 > 0 0 𝑖𝑓 𝛼 𝑖,𝑗 = 0 (4) where  , is the number of crab burrows in cell (, ). Larval mortality was described as follows: 𝜇 𝐽 𝑖,𝑗 = 𝜅𝐽 𝑖,𝑗 𝛼 𝑖,𝑗 (1 -𝑐) (5) where  is a numeric constant. Further discussion of density-dependent mortality in our model is given in Supplementary Material S. 3. Our model retained males for two reasons. First, the gender ratio in trap data was unequal (see Results) and, as such, our model framework incorporated gender-specific dispersal behaviours and mortality rates to examine whether gender-biased mosquito populations in the field could arise from these mechanisms. Second, males contributed to the total number of mosquitoes in traps and these data were used for model validation.
Many implementations of random forests smooth the estimated response surface by averaging across trees  [87]. When field data recorded ≤5 burrows/100 m 2 , all random forest errors were positive (overestimation). At >5 burrows/100 m 2 , all errors were negative (underestimation). The errors were also larger at the tails of the distribution of field data. The distribution of prediction errors is shown in Supplementary Material S. 7. Larger datasets may fare better at capturing tail ends of the distribution. A discrepancy between the observed and predicted gender distribution should also be noted. 3. 7% of trapped mosquitoes were male. Our model predicted between 30%-36% of mosquitoes to be male. This may have occurred because (i) differences in life history traits between females and males may be larger than suggested by the empirical literature, (ii) the gender emergence ratio of pupae may not be 1:1, or (iii) the trapping protocol may be biased towards trapping female mosquitoes.
Acknowledgements The authors would like to thank the employees of  Institut Louis Malardé's Medical Entomology Laboratory  for outstanding technical support and critical comments throughout this study. In particular, we thank  Manfred Mervin ,  Reva Lannuzel ,  Michel Cheong Sang  and  Hutia Barff  for their support. We would also like to thank the employees of  Le Taha 'a Island Resort and Spa for their generosity in facilitating field research on motu Tautau and providing support and direction throughout the duration of data collection. This work was financially supported by a  Biotechnology and Biological Sciences Research Council (BBSRC) ,  United Kingdom DTP  studentship [BB/M011224/1] to KH.
Interestingly, in accordance with that, analysis of the TCGA-PRAD database showed that PLXND1 overexpression is significantly associated with a poor disease-free survival in PCa, suggesting that EDCs could participate in cancer aggressiveness (and metastasis) through this new signaling pathway. It is well-known that PFOA is also able to bind to the nuclear receptor Peroxisome proliferator activated receptor-alpha (PPARα) and thus alters lipid metabolism and induces liver toxicity in rodents  [38]. Our data demonstrate that in MCF7 and DU145 cell lines, the activity of PPARα is not altered by PFOA treatment (data not shown). Interestingly, Hu et al. demonstrated that PFAS do not alter PPARα expression in prostate stem and progenitor cells  [35]. Therefore, we can hypothesize that PFAS and PFOA can use other signaling pathways than the one of PPAR in PCa pathogenesis.
IncuCyte ® Assay Cells infected with NucLight Red Lentivirus were seeded in a 48-well plate at a concentration of 20. 000 cells/well. The day after, chemicals were added in media containing IncuCyte ® Cytotox Green Reagent (Essen Bioscience ® ) for loss of cell membrane integrity assessment according to the manufacturer's protocol  [44]. Immediately after, the plate was put on the IncuCyte ® (Essen Bioscience ® [Ann Arbor, MI, USA]). Three wells were used per replicate and nine pictures per well were taken every 2 h using a 10× lens for each experiment. IncuCyte ® software is used to analyze images for both red nucleus count and green cell dying count.
Human Phospho-Kinase Array Human Phospho-Kinase Antibody Array (RD Systems ® [Minneapolis, Minnesota]) was used to analyze the relative site-specific phosphorylation of 43 kinases and 2 related total proteins. Following the manufacturer's instructions, we used 1. 107 cells/mL for cell lysis corresponding to 400 µg of protein per array  [43]. Phosphorylated proteins were detected using Syngene PXi by exposing membranes to luminescent signals generated after incubation of the membrane with ECL (WBKLS0500, Millipore ® [Darmstadt, Germany]). Image J ® software (National Institutes of Health and Laboratory for Optical and Computational Instrumentation [University of Wisconsin, Madison, WI, USA]) was used to quantify dot intensity.
Microarray Experiment Pangenomic microarrays were performed on the UCA GENOMIX platform (IPMC, CNRS U7275, Sophia-Antipolis, France) using Agilent-072363 SurePrint G3 Human GE v3 8 × 60 K Microarray. RNA from DU145 cells were treated with EtOH (control), 10 -6 , 10 -9 and 10 -12 M PFOA for 48 h before microarray analysis. Experimental data and associated microarray designs have been deposited in the NBCI Gene Expression Omnibus (GEO under series GSE150655 and platform record GPL 1456). We then focused on coding sequences to perform a differential expression analysis between EtOH (control) and 10 -12 M PFOA conditions using Limma from Phantasus software (https://artyomovlab. wustl. edu/phantasus/ accessed on 20 October 2021), and an overlap analysis with significantly deregulated genes was performed using GSEA (Gene Set Enrichment Analysis) software to investigate canonical pathway and reactome gene sets with FDR q-value less than 0.
DDEEC (Design of Distributed Energy Efficient Clustering) improved the CH selection method by avoiding the penalization of high energy nodes  [20]. It used residual energy for CH selection. After the advancements in different routing strategies, energy heterogeneity at four levels was introduced by the BEENISH (Balanced Energy Efficient Network Integrated Super Heterogeneous) protocol for wireless sensor networks. However, the BEENISH suffered from the drawback of the penalization of higher energy nodes in the process of CH selection  [22]. MRA (Multiple gateway-based Routing Architecture) introduced the novel method of data collection for harsh environment monitoring. In this, the authors utilized four gateways around each periphery of the network  [23]. It aimed for the early detection in harsh environment monitoring.
Performance Analysis and Evaluation The evaluation of performance is implemented in MATLAB, and the comparison is made with existing algorithms such as GA  [9] , ALO  [11]  and PSO  [8]  in terms of energy efficiency, bandwidth, packet delivery ratio and network longevity  [35] [36] [37]. With GA, the structure of the network differs with every round based on the features of the nodes; ALO maximizes the energy utilization and distance, having a minimized response time; PSO calculates the optimal set of cluster heads which maximizes the efficiency of energy and coverage of the network. The simulations carried out during the implementation are shown in Table  1 : Network Longevity: It indicates how long or for how many rounds the network can operate. It refers to the number of rounds after which the nodes in the field will expire whilst completing their jobs.
More recently, proximity labelling techniques, such as APEX-RIP, APEX-Seq, Proximity-CLIP, Cap-seq and proximity-specific ribosome profiling, enabled high-resolution in situ subcellular RNA profiling based on the deposition of covalent tags on RNPs found in immediate vicinity of a genetically engineered enzyme targeted to the organelle of interest. These powerful methods have drawn first nearly comprehensive RNA landscapes of mitochondria, endoplasmic reticulum, nucleus, cytosol and even distinct subnuclear territories  (43) (44) (45) (46) (47) (48) (49) (50). In many cases, they also provided access to the localisation topology of organelle-associated transcripts by distinguishing internal RNAs, present in the luminal space of organelles, and external, surface-attached RNAs. This property is biologically very important as it determines which transcripts and proteins can in principle interact in the cellular milieu, eventually producing physiologically relevant outcomes, and which should never come in touch with each other.
Antibodies used in this study. mouse monoclonal ␣-CYCS (BD Pharmingen, Franklin Lakes, USA, Cat # 556432), goat polyclonal ␣-ENO (Santa Cruz Biotechnology, Inc, Santa Cruz, USA, Cat # sc-7455), mouse monoclonal ␣-His (Sigma-Aldrich, Saint Louis, USA, Cat # SAB1305538), mouse monoclonal ␣-LRPPRC (Santa Cruz Biotechnology, Inc, Santa Cruz, USA, Cat # sc-166178), rabbit polyclonal ␣-OPA1 (Proteintech Group, Rosemont, USA, Cat # 27733-1-AP), rabbit polyclonal ␣-PNPase (Abcam, Cambridge, UK, Cat # ab96176), mouse monoclonal ␣-Ro60 (Santa Cruz Biotechnology, Inc, Santa Cruz, USA, Cat # sc-100844), rabbit polyclonal ␣-Ro60 (Sigma-Aldrich, Saint Louis, USA, Cat # HPA002835), rabbit polyclonal ␣-TOMM20 (Santa Cruz Biotechnology, Inc, Santa Cruz, USA, Cat # sc-11415), rabbit polyclonal ␣-uL4m (Proteintech Group, Rosemont, USA, Cat # 27484-1-AP), polyclonal sheep HRP-conjugated ␣-mouse (GE Healthcare, Chicago, USA, Cat # NXA931), polyclonal donkey HRPconjugated ␣-rabbit (GE Healthcare, Chicago, USA, Cat # NA934V).
CoLoC-seq data processing Sequencing read libraries were pre-processed with cutadapt version 2. 8  (65)  to trim adapter sequences. Thereafter, read alignment and gene feature quantification was done with READemption version 0. 4. 3 (66) (https://doi. org/10. 5281/ zenodo. 250598). All libraries were aligned to the Human genome (Genome Reference Consortium Human Build 38 patch release 13) retrieved from RefSeq  (67). READemption uses segemehl version 0. 2. 0-418  (68)  as the read aligner. The options used for alignment, coverage calculation, and feature quantification can be found in the scripts deposited at Zenodo (https://doi. org/10. 5281/zenodo. 6389451). Entries for repetitive genes were compounded, and their reads were summed up. Genes for which the majority of reads came from cross-mapping to embedded tRNA-, snRNA-, 7SL-, 5S rRNA-or mtDNA-like (NUMTs) sequences were excluded from the analysis.
Remark that when an RNA is not localised inside the organelle, i. e. its P 0 = 0, the equation resolves back into the canonical pseudo-first-order decay: f (A) = e -k i A (2) By contrast, transcripts fully localised inside the organelle and inaccessible for RNase A (P 0 = 1) are expected to show a constant level independent of RNase A concentration: f (A) = P 0 = 1 ( 3 ) Nonlinear curve fitting of Model 1 into CoLoC-seq and Mock-CoLoC-seq data (for either individual or combined replicates) was performed in Origin 2021b (v9. 8. 5. 212, OriginLab Corporation) with the following settings. Constraints on parameters: 0 ≤ P 0 ≤ 1, k ≥ 0; Leverberg Marquardt regression method; initial P 0 estimate fixed to the lowest observed f(A) value; 500 iterations; tolerance 1E-09; 'Model comparison method' for CI computation for the parameters.
Statistical analyses Violin plots were created in Microsoft Power BI Desktop (v. 2. 71. 5523. 821) with the use of the Epanechnikov kernel density method. Spearman's correlation was calculated with Free Statistics and Forecasting Software, v1. 2. 1 (http://www. wessa. net/rwasp spearman. wasp). Mann-Whitney test was performed in Statistics Kingdom (https:// www. statskingdom. com/170median mann whitney. html). One-sample Wilcoxon test and the calculation of confidence intervals (95% CI) for colocalised proportions of transcripts detected by smFISH were performed in GraphPad Prism 9 (v9. 3. 1). The P 0 -k simulation in Supplementary Figure  S7B  was performed in MS Excel 2016 based on the Model 1 and visualised with Heatmapper (http://heatmapper. ca/). Ensemble RNA folding free energy was predicted with RNAfold  (75).
CoLoC-seq analysis in human mitochondria: proof of principle We set out to evaluate the feasibility of CoLoC-seq on a relatively well-studied human mitochondrial transcriptome. Mitochondria possess their own genome, providing a straightforward way of benchmarking for bona fide resident transcripts. Indeed, human mitochondria (especially from the HEK293 cell line used here) are a popular test organelle for subcellular transcriptomics techniques  (45) (46) (47) 49) , which permits us to directly compare the performance of CoLoC-seq with that of other existing tools. Of no less interest, a number of reports provided evidence for the presence of select nuclear DNA (nDNA)-encoded transcripts inside these organelles, although this topic remains contentious and, therefore, deserves more rigorous assessment  (51, 76).
We then subjected the same samples to Illumina sequencing. Since the quantity of interest is the amount of remaining intact RNA, we developed a custom protocol that excludes RNase A-cleaved transcripts (Supplementary Figure  S2  and Materials and Methods). To this end, we leveraged the specific chemistry of RNase A cleavage: the enzyme generates 5'-hydroxyl and 3'-phosphate groups, which are incompatible with standard adaptor ligation  (63). Thereby only 5'-phosphorylated and 3'-hydroxylated RNAs get amplified, sequenced, and quantified. Importantly, a single RNase A cleavage is sufficient to invalidate the transcript and exclude it from the library. Upon mapping the reads to the human genome and their cross-library normalisation with the help of an exogenously added spike-in RNA, we could reconstitute the genomewide digestion dynamics of mitochondria-associated transcripts (Supplementary Table  S2 ).
Those include the highly conserved Y1 and Y3 RNAs  (91, 92) , the primate-specific SNAR-A RNAs  (93) , and a set of tRNAs. Manual inspection of their profiles confirmed that they had a significant RNase A-resistant pool in CoLoC-seq, when mitochondria were intact, but not in Mock CoLoC-seq, when they were lysed (Figure  4B , C, Supplementary Table  S2 ). As far as the read length (75 nt) permits to see, all these RNAs appear to be full-size, untruncated transcripts (Supplementary Figure  S8A-C ). The extensive association of cytosolic tRNAs with human mitochondria has been previously reported  (36) , while the functional significance of this phenomenon remains enigmatic. The composition of the detected tRNAs is strongly biased: Glu, Asp, Lys and Gly isoacceptors overwhelmingly dominate the landscape (Supplementary Figure  S8D ).
Thereby, in comparison with the mtDNA-encoded MT-CO1 mRNA, this effectively sets the higher limit of their abundance at <1 per 300 mtDNA-encoded transcripts (taking the hyper-conservative approach assigning one mtDNA-encoded RNA to one observed mitoplast; a more realistic estimate, accounting for its higher real abundance and the fact that the MT-CO1 mRNA is but one of many mtDNA-encoded transcripts, is likely 3-4 orders of magnitude lower). Altogether, our data suggest that, while some nDNA-encoded RNAs seem to be present in human mitochondria, their abundance in the matrix is extremely low.
Due to the specific sequencing protocol, CoLoC-seq is naturally blind to regular 5'-OH and 2'/3'-phosphorylated RNA species (e. g. tRNA fragments) and to circRNAs. However, those can still be visualised and quantified by northern blotting. Finally, one may expect that CoLoC-seq might have missed some low-abundance RNAs that could still be localised in the mitochondria. However, the copy number estimates for mitochondria-localised Y RNAs enabled by smFISH in isolated mitoplasts (Figure  5C ) suggest that we have reached the limit of biologically relevant detection, and the existence of further such transcripts is unlikely.
ACKNOWLEDGEMENTS The authors wish to thank  Dr Fritz Th ümmler  ( Vertis Biotechnologie AG ) for the customisation of the CoLoCseq library pipeline,  Timothée Vincent  for help with pilot CoLoC-seq data analyses,  Lauriane Kuhn  ( Proteomics Platform Esplanade, Strasbourg ) for mass spectrometry analyses, and all members of the MITO team,  UMR7156 -GMGM , and the  LabEx MitoCross  for stimulating and fruitful discussions.
FUNDING University of Strasbourg  through the  IdEx Unistra  [ ANR-10-IDEX-0002 ]; SFRI-STRAT  US  project [ ANR-20-SFRI-0012 ];  EUR IMCBio in the frame of the Research Cluster MitoCross  [ ANR-17-EURE-0023 ];  IdEx -Attractivité  [ ANR-10-IDEX-0002-02  to Al. S. ];  Centre National de la Recherche Scientifique (CNRS). Funding for open access charge:  IdEx Unistra  [ ANR-10-IDEX-0002 ].