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For all participants with microscopically confirmed P. falciparum infections (i.e., isolates), two 5 x 5 mm sections were cut from each dried blood spot and placed in a 1.5‐ml centrifuge tube, with genomic DNA (gDNA) being extracted using the QIAmp DNA mini kit (Qiagen) as previously described (Tiedje et al., 2017). A subset of 200 microscopic P. falciparum isolates from both the pre‐IRS (T1) and post‐IRS (T2) surveys were selected for microsatellite genotyping based on their multiplicity of infection (MOI) (i.e., number of genetically distinct P. falciparum genomes) as determined using var genotyping (see Supporting Information Methods, Figure S1). Briefly, using this approach we estimated the MOI based on the number of var DBLα types identified per isolate, using a cutoff value of 60 var DBLα types per P. falciparum genome. Isolates with ≤60 var DBLα types were classified as single‐clone infections (MOI = 1), while isolates with >60 var DBLα types were classified as multiple‐clone infections (MOI > 1). To facilitate a more accurate assignment of the fluorescent peaks during the analysis (described below), only those isolates with a MOI = 1 or 2, were selected for the microsatellite genotyping (Anderson et al., 1999). The P. falciparum isolates (N = 400) selected from the pre‐ and post‐IRS surveys were genotyped using a verified panel of 12 putatively neutral microsatellite markers located across the 14 chromosomes as described by Anderson et al. (1999): TA1, 2490, TA81, TA87, TA109, TA60, POLYA, TA42, ARA2, PfG377, PfPK2, and TA40, with modified cycling conditions as specified in Ruybal‐Pesántez et al. (2017). Fluorescently‐labelled PCR products were sent to a commercial sequencing facility (Macrogen Inc., South Korea) for capillary electrophoresis and fragment analysis on an Applied Biosystems 3730xl DNA analyser (ThermoFisher Scientific). Raw data files were imported using GeneMarker (SoftGenetics LLC), normalised based on the size standard LIZ500, and scored using customised panels as previously described (Anderson et al., 1999; Ruybal‐Pesántez et al., 2017). All major peaks that were within the expected marker base pair (bp) range and were spaced at intervals corresponding to trinucleotide (3 bp) repeats were considered to be true alleles. Any peak less than 33% of the primary peak (i.e., local max) for a locus was considered a minor allele and not interpreted as a true allele. Background noise was defined as any peak <200 fluorescent units (Anderson et al., 1999). These data were cleaned using R package base v. 3.5.0 (R Core Team, 2018) and then processed using TANDEM v. 1.09 (Matschiner & Salzburger, 2009), which is optimal to assign an allele to each trinucleotide microsatellite locus for each isolate. We combined data from the pre‐ and post‐IRS surveys prior to binning alleles with TANDEM to ensure each survey could be compared accurately to each other. For the 200 isolates investigated in the pre‐ and post‐IRS surveys, the median genotyping success was 89.2% in the pre‐IRS survey and 92.8% in the post‐IRS survey for the 12 microsatellite markers (Table S2) as expected for low‐density asymptomatic P. falciparum infections. Since isolate genotyping success for TA1 and TA42 was <75% pre‐IRS and/or post‐IRS (Table S2), these loci were subsequently removed, with 10 microsatellite loci included for the downstream multilocus microsatellite analyses (Note: All 12 microsatellite loci, including TA1 and TA42, were successfully amplified and genotyped for the 3D7 positive controls, thus the possibility of null alleles could not be excluded). Finally, only those isolates with genotyping data at ≥3 microsatellite loci were included, resulting in 192 (96.0%) and 200 (100%) isolates from the pre‐ and post‐IRS surveys, respectively (Table ​(Table1,1, Figure S1). Here, we report the first population genetic study to examine the impact of indoor residual spraying (IRS) on Plasmodium falciparum in humans. This study was conducted in an area of high seasonal malaria transmission in Bongo District, Ghana. IRS was implemented during the dry season (November-May) in three consecutive years between 2013 and 2015 to reduce transmission and attempt to bottleneck the parasite population in humans towards lower diversity with greater linkage disequilibrium. The study was done against a background of widespread use of long-lasting insecticidal nets, typical for contemporary malaria control in West Africa. Microsatellite genotyping with 10 loci was used to construct 392 P. falciparum multilocus infection haplotypes collected from two age-stratified cross-sectional surveys at the end of the wet seasons pre- and post-IRS. Three-rounds of IRS, under operational conditions, led to a >90% reduction in transmission intensity and a 35.7% reduction in the P. falciparum prevalence (p < .001). Despite these declines, population genetic analysis of the infection haplotypes revealed no dramatic changes with only a slight, but significant increase in genetic diversity (He : pre-IRS = 0.79 vs. post-IRS = 0.81, p = .048). Reduced relatedness of the parasite population (p < .001) was observed post-IRS, probably due to decreased opportunities for outcrossing. Spatiotemporal genetic differentiation between the pre- and post-IRS surveys (D = 0.0329 [95% CI: 0.0209 - 0.0473], p = .034) was identified. These data provide a genetic explanation for the resilience of P. falciparum to short-term IRS programmes in high-transmission settings in sub-Saharan Africa.

Paleomagnetic results derived from sixteen Black Sea sediment cores. The natural remanent magnetization (NRM) and the anhysteretic remanent magnetization (ARM) were measured with a 2G Enterprises 755 SRM (cryogenic) long-core magnetometer equipped with a sample holder for eight discrete samples at a separation of 20 cm. The magnetometer's in-line tri-axial alternating field (AF) demagnetizer was used to demagnetize the NRM and ARM of the samples. The NRM was measured after application of AF peak amplitudes of 0, 5, 10, 15, 20, 30, 40, 50, 65, 80, and 100 mT. Directions of the characteristic remanent magnetization (ChRM) were determined by principle component analysis (PCA) according to Kirschvink (1980). The error range of the ChRM is given as the maximum angular deviation (MAD). The ARM was imparted along the samples' z-axis with a static field of 0.05 mT and an AF field of 100 mT. Demagnetization then was performed in steps of 0, 10, 20, 30, 40, 50, 65, and 80 mT. The median destructive field of the ARM (MDFARM) was determined to estimate the coercivity of the sediments. The slope of NRM versus ARM of common demagnetization steps was used to determine the relative paleointensity (rPI). In most cases, demagnetization steps from 20 to 65 mT were used to determine the rPI.Note, in all studied Black Sea sediment cores, samples with SIRM/κ~LF~ ratios >10 kAm^-1^ (SIRM: saturated iso-thermal remanent magnetization, κ~LF~: low-field magnetic susceptibility), empirically found to indicate the presence of diagenetically formed greigite, were omitted for paleomagnetic studies.I chrm: characteristic inclinationD chrm: characteristic declinationSlope-rPI: relative paleointensity determined by slope NRM/ARM during alternating field demagnetization; NRM: natural remanent magnetization, ARM: anhysteretic remanent magnetization

Supplement to: Berg, Sonja; White, Duanne A; Jivcov, Sandra; Melles, Martin; Leng, Melanie J; Rethemeyer, Janet; Allen, Claire Susannah; Perren, Bianca; Bennike, Ole; Viehberg, Finn Andreas (2019): Holocene glacier fluctuations and environmental changes in subantarctic South Georgia inferred from a sediment record from a coastal inlet. Quaternary Research, 91(1), 132-148 We investigate a sediment core (Co1305) from a coastal marine inlet on South Georgia using elemental, lipid biomarker, diatom and stable isotope data to infer changes in environmental conditions and to constrain the timing of Late glacial and Holocene glacier fluctuations.

# README \#**For the Ultrasound area:** \#Berrueta et al_Area measured by US (ultrasound) carrageenan inflammation 24-96h. The data is organized as follow: There are 6 columns, column A describe the code assigned to each individual animal. Column B represent treatment: 0=No stretch, 1= stretch. Column C refers to the time for the treatment: from 24h to 96h. Column D refers to the batch or group of samples. Column E correspond to the weight of the tissue at the time of euthanasia in mg. Column F represent the ultrasound area of the inflammatory lesion per each sample. \#**For the flow cytometry data** **Neutrophils and macrophage subpopulation**. Analysis was performed using Flow cytometry. The data set is organized as follow: \#**Berrueta et al_neutrophils dynamics**: There are 9 columns, column A indicates the code assigned to each individual animal. Column B represent treatment: 0=No stretch, 1= stretch. Column C refers to the time for the treatment: from 24h to 96h. Column D refers to the batch or group of samples. Column E indicates the type of cell. Column F is the total cell count per each sample. Column G represent the percentage of live cells per sample. Column H describes the relative number of CD45+ cells (leukocytes). Column I describe the relative number of neutrophils. \#**For the macrophages**: There are 8 columns, column A indicates the code assigned to each individual animal. Column B represent treatment: 0=No stretch, 1= stretch. Column C refers to the time for the treatment: from 48h to 96h. Column D refers to the batch or group of samples. Column E indicates the relative number (percentage-%) of CD45+ cells (leukocytes). Column F describe the relative number (percentage-%) of F4/80+ cells (macrophages) . Column G describe the relative number (percentage-%) of M1 macrophages. Column H describes the relative number (percentage-%) of M2 macrophages. \#**Single cell and bulk library preparation and sequencing**. Data set organization is as follow: For each of the cell populations: \#All.integrated.minus9.minus16.minus17.cDC1_stretch.markers.cDC1_non-stretch.markers. classic dendritic cells 1 (cDC1): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17.cDC2_stretch.markers. cDC2_non-stretch.markers. classic dendritic cells 2 (cDC2): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17.EC_stretch.markers. EC_non-stretch.markers. Endothelial cells (EC):The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. FB \_stretch.markers. FB \_non-stretch.markers. Fibroblasts (FB): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. iDC \_stretch.markers. iDC \_non-stretch.markers. inflammatory dendritic cells (iDC): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. M1 \_stretch.markers. M1 \_non-stretch.markers. M1 macrophages (M1): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. M2c \_stretch.markers. M2c \_non-stretch.markers. M2c macrophages (M2c): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. MC \_stretch.markers. MC \_non-stretch.markers. Mast cells (MC): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. Mo \_stretch.markers. Mo \_non-stretch.markers. Monocytes (Mo): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. Th1 \_stretch.markers. Th1 \_non-stretch.markers. T helper cells 1 (Th1): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. Th2 \_stretch.markers. Th2 \_non-stretch.markers. T helper cells 2 (Th2): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. \#All.integrated.minus9.minus16.minus17. M2a \_stretch.markers. M2a \_non-stretch.markers. M2a macrophages (M2a): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. All.integrated.minus9.minus16.minus17. M2b \_stretch.markers. M2b \_non-stretch.markers. M2b macrophages (M2b): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. All.integrated.minus9.minus16.minus17. Nph \_stretch.markers. Nph \_non-stretch.markers. Neutophils (Nph): The data include 7 columns; column A include the total list of analyzed genes. Column B is the statistical significance p value, Column C represent the logarithmic of average expression or log 2FC per each gene. Column D is the actual Fold change expression per each gene. Column E is the gene expression for the Stretch sample (pct.1). Column F is the gene expression for the No stretch sample (pct.2). Column G is the adjusted p value. For the Lipidomic data (LC-MS/MS: Liquid chromatography–mass spectrometry) Berrueta et al Lipidomics data sharing 111723. Data set was organized as follow: There are 5 columns describing the data: Column A describe the total list of lipid mediators analyzed. Column B describe the average of each lipid mediator in No Stretch samples at 48h. Column C describe the average of each lipid mediator in Stretch samples at 48h. Column D describe the average of each lipid mediator in No Stretch samples at 96h. Column E describe the average of each lipid mediator in Stretch samples at 96h. For the Lipidomic data (LC-MS/MS: Liquid chromatography–mass spectrometry) Berrueta_et_al_Table_1_Lipidomics_111723. Data set was organized as follow: There are 5 columns describing the data: Column A describe the total list of lipid mediators analyzed. Column B describe the average of each lipid mediator in No Stretch samples at 48h. Column C describe the average of each lipid mediator in Stretch samples at 48h. Column D describe the average of each lipid mediator in No Stretch samples at 96h. Column E describe the average of each lipid mediator in Stretch samples at 96h. All ultrasound data acquisition and measurements were performed by investigators blinded to intervention condition. Ultrasound images of the back were acquired under isoflurane anesthesia. A high-frequency ultrasound scanner (Vevo 2100, Fujifilm VisualSonics, Toronto, Canada) in B mode with a 21 MHz transducer (MS 250) was used for optimal spatial resolution. A conductive gel was centrifuged for 5 minutes to remove air bubbles and spread over the skin. The transducer was stabilized with a clamp and mounted into an articulated arm to control the distance and the angle between the transducer and the skin surface. the transducer was oriented transversal or sagittal perpendicular to the skin of the back and centered on the lesion area. Total lesion area was calculated by averaging the lesion area measured at transversal and sagittal positions. Flow cytometry Inflammatory lesions were excised and minced in 5% FBS-DMEM, using a scalpel, then the suspension was filtered through a 70mm filter. Isolated cells were counted using an automated cell counter TC20 (Bio-Rad, CA). For surface receptors, cells at 1 × 106/mL were stained with a mix of mouse monoclonal antibodies: To detect neutrophils (N=92) we used the antibodies: APC anti-mouse Ly-6/Ly-6c(Gr1) and FITC anti-mouse CD45; for macrophage populations (N=32): we used the following combination of antibodies: APC cy7 anti-mouse CD45, APC anti-mouse F4/80, FITC anti-mouse Nos2 (iNOS), PE anti-mouse CD206. Stained cells were examined using a FACSCanto II Flow Cytometer (BD Biosciences, San Jose, CA) with FlowJo single cell analysis software. Single cell and bulk library preparation and sequencing Single cell library preparation was performed according to the manufacturer’s instructions for the 10× Chromium single cell kit (10x Genomics). The libraries were then pooled and sequenced on a NextSeq 2000 sequencer (Illumina). Single cell RNA seq data processing and quality control Read processing was performed using the 10x Genomics workflow (Zheng et al. 2017). Briefly, the Cell Ranger Single Cell Software Suite (v3.0.1) was used for demultiplexing, barcode assignment, and unique molecular identifier (UMI) quantification (http://software.10xgenomics.com/single cell/overview/welcome). The reads were aligned to a custom mm10 reference genome (Genome Reference Consortium Mouse Build 38) extended with additional annotation for several frequently used mouse transgenes. Both lanes per sample were merged using the ‘cellranger mkfastq’ function and processed using the ‘cellranger count’ function. In total, the Cell Ranger software detected 7,921 cells per sample, sequenced at 23,326 reads and identifying 1,611 genes derived from 4,375 UMIs per cell on average across all samples. The following metrics were used to flag poor quality cells: number of genes detected, total number of UMIs, and percentage of molecules mapped to mitochondrial genes. Within the Seurat workflow, low quality and artifact cells were excluded by removing any cells that expressed fewer than 200 genes, and removed genes expressed in less than 3 cells. Gene expression matrices were transformed for better interpretability using the Seurat function ‘NormalizeData’. A total of 51,943 cells were included in the subsequent clustering and pseudo time analyses. For cross-condition data integration and batch correction, ‘FindIntegrationAnchors’ and ‘IntegrateData’ were applied to data in Seurat, following the data integration vignette (https://satijalab.org/seurat/archive/v3.2/immune_alignment. html) Targeted LC-MS/MS Supernatants from the inflammatory lesions were placed in ice cold methanol containing deuterated internal standards (d8-5S-hydroxyeicosatetraenoic acid (5-HETE), d4-leukotriene B4 (LTB4), d4-prostaglandin E2 (PGE2) and d5-lipoxin A4 (LXA4); 500pg each) and homogenized using a PTFE Dounce (Kimble Chase). Proteins were allowed to precipitate (4oC), and lipid mediators were extracted using C18 solid-phase cartridges as described before (Dalli et al., 2018) Measurement of lipid mediators was carried out by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a QTrap 5500 (ABSciex, Framingham, MA) equipped with a Shimadzu LC-20AD HPLC and a Shimadzu SIL-20AC autoinjector (Shimadzu, Kyoto, Japan). An Agilent Eclipse Plus C18 column (100mm x 4.6 mm x 1.8 mm) maintained at 50oC was used with a gradient of methanol/water/acetic acid of 55:45:0.01 (v/v/v) to 100:0:0.01 at 0.4 ml/min flow rate. Multiple reaction monitoring (MRM) transitions were used to identify and quantify lipid mediators in samples, as compared with retention times of authentic standards run in parallel. Quantification was achieved using calibration curves constructed with synthetic standards for each mediator, after normalization to extraction recovery based on internal standards and followed by normalization to tissue weight.  Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE2 and PGD2) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (Nos2) and pro-resolution (Arg1) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis.

The dataset contains: Validation_TH.xlsx, Validation_EL.xlsx: The results of the validation procedure where experimentally obtained measurements are compared to simulation estimates. Sensitivity_CHTC.xlsx: Sensitivity analysis of overall heat transfer coefficient from the converter housing to the exterior. Case Study.xlsx: The results of the case study, i.e. loss distribution among the components.

The present data were collected from two cruises that took place in the Canary Current from September 10 to October 1, 2002 (COCA I) and from May 21 to June 7, 2003 (COCA II). The study was conducted along two zonal sections (21ºN and 26ºN) extending from the coastal upwelling to the open ocean at 26ºW, and focused on the epipelagic (0-200 m) and mesopelagic (200-1000 m) zones. The cruises consisted of a total of 31 hydrographic stations and 10 biogeochemical stations, half of them along each section, which were roughly equidistant. At each station seawater samples were collected at fixed depths in the biogeochemical stations every 50 m for suspended POC, DOC and ETS activity by means of a Seabird 911+ CTD, mounted on a General Oceanics rosette sampler equipped with 24 10L-Niskin bottles.