CX3CR1, KLRD1, MMP8, PRTN3, RETN and SCD) involved. The model performed moderately in the early identification of patients that develop severe H1N1 disease.H1N1 is the most common subtype of influenza virus circulating worldwide and can cause severe disease in some populations. Early prediction and intervention for patients who develop severe influenza will greatly reduce their mortality. In this study, we conducted a comprehensive analysis of 180 PBMC samples from three published datasets from the GEO DataSets. Differentially expressed gene (DEG) analysis and weighted correlation network analysis (WGCNA) were performed to provide candidate DEGs for model building. Functional enrichment and CIBERSORT analyses were also performed to evaluate the differences in composition and function of PBMCs between patients with severe and mild disease. Finally, a risk score model was built using lasso regression analysis, with six genes ( H1N1 is one of the most widespread influenza A viruses in humans, which first appeared in Mexico and the United States in April 2009, and brought extensive influenza outbreaks . The 200High viral loads and excessive host response are thought to contribute to severe influenza . Previouhttp://www.ncbi.nlm.nih.gov/geo/). The GSE111368 dataset included blood samples of influenza-infected patients at three time points: at enrolment, approximately 48h after enrolment, and >4 weeks after enrolment. We chose H1N1-infected patients and selected their samples at enrolment for further analysis, which were consisted of 67 patients with severe disease and 27 patients with mild disease that served as the discovery cohort. The dataset GSE61821 included samples of 54 mild and 32 severe H1N1-infected patients at an early stage. Another dataset (GSE101702) had 107 patients with influenza A virus infection, with 63 mild cases and 44 severe cases, and the subtype of the virus was unknown. GSE61821 and GSE101702 were used as the validation cohort.The RNA sequence datasets  were obtained from the GEO DataSets , Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed using the ClusterProfiler package . GO was http://cibersort.stanford.edu/) with the default signature matrix at 1000 permutations. The sum of all estimated immune cell type fractions was equal to 1 for each sample.CIBERSORT is a gene expression‐based deconvolution algorithm , that us2 = 0.80) was chosen to construct a scale-free network using pairwise Pearson correlation analysis. Finally, we counted the disparity of module eigengenes and illustrate the cluster tree of modules, selected the cutting line of the module tree diagram, and merged some modules. The correlation between modules and disease severity was shown by heatmap.The gene co-expression network of the primary cohort was obtained with the WGCNA package . A totalvia imposition of a penalty proportional to their size. We used the glmnet R package to perform lasso regression analysis. First, we randomly selected 70% of the primary dataset as training data and the rest as testing data. Then, the training dataset was used to calculate and select the optimal solution of the parameter λ, which gave the minimum cross-validated error and was then applied to predict the testing data. Finally, a six-gene model was built utilizing the regression coefficients derived from the analysis. The model was used to calculate risk score  and predict disease severity.DEGs in modules highly correlated to disease severity were extracted for logistic regression analysis using the lasso method. In this method, a small group of genes associated with disease severity was selected by shrinkage of the regression coefficient Receiver operating characteristic (ROC) analysis was performed to assess the predictive ability of the prediction model using the pROC R package. First, we used the testing data to compare the accuracies of the risk score model and the age model. Then, to test the applicability of our model, we applied it to the validation cohorts.The workflow is shown in To characterize the biological functions of these DEGs, GO analysis was performed. Enriched GO terms in BP were predominately processes related to immune responses, of which the top four were neutrophil degranulation, neutrophil activation involved in immune response, neutrophil activation and neutrophil mediated immunity  +  +  +  +  +  + . We calculated the risk score for each patient in the testing data and compared it to their real disease severity  and PRTN3 (proteinase 3) are related to neutrophil function. MMP8, also called collagenase 2 or neutrophil collagenase, is stored as an inactive enzyme in neutrophil granules, which can be quickly released to the site of inflammation after neutrophil activation. With its unique characteristics, MMP8 has been shown to play an important role in the pathogenesis of respiratory diseases such as acute respiratory distress syndrome or acute lung injury  and stearoyl-CoA desaturase (SCD) are molecules that regulate lipid metabolism, and there are few studies on them in infectious diseases. Limited studies have shown that RETN is positively related to disease severity of sepsis , in whicCX3CR1, KLRD1, MMP8, PRTN3, RETN, and SCD is a reliable prediction model for early identification of patients with severe H1N1 disease. And it has the potential to be used in patients with H3N2 infection.In summary, our analysis of two influenza datasets demonstrate that neutrophil-mediated immune response may play an important role in the progression of severe pneumonia caused by H1N1 infection. The risk score model based on the expression levels of https://www.ncbi.nlm.nih.gov/geo/]. Further inquiries can be directed to the corresponding author.The original contributions presented in the study are included in the article/Supplementary Material. Microarray data that support the findings of this study are available at Gene Expression Omnibus ; .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Lactobacillus species colonizing the honey bee gut. We serially passaged the four species through gnotobiotic bees and in liquid cultures in the presence of either pollen (bee diet) or simple sugars. Although the four species engaged in negative interactions, they were able to stably coexist, both in vivo and in vitro. However, coexistence was only possible in the presence of pollen, and not in simple sugars, independent of the environment. Using metatranscriptomics and metabolomics, we found that the four species utilize different pollen-derived carbohydrate substrates indicating resource partitioning as the basis of coexistence. Our results show that despite longstanding host association, gut bacterial interactions can be recapitulated in vitro providing insights about bacterial coexistence when combined with in vivo experiments.Ecological processes underlying bacterial coexistence in the gut are not well understood. Here, we disentangled the effect of the host and the diet on the coexistence of four closely related  Microbes colonize nearly every environment on Earth, from the ocean and soil to the inner and outer surfaces of animals, such as the gut or skin. They form communities that are usually made up of a diverse range of bacteria, often containing closely related species – a key factor for a successful community.But closely related bacteria can battle for the same resources, so it is unclear how they manage to live alongside each other without competing against one another. While diet is thought to play a key role in enabling closely related bacterial species to co-exist in the gut of an animal, experimental evidence is lacking, due to the difficulty in replicating these systems in the laboratory.One strategy for investigating microbial communities is using honeybees. A major dietary source for honeybees is pollen, which can also be applied in the laboratory to grow diverse types of bacteria found in the honeybee gut. In addition, scientists can generate bees that lack microbial communities in the gut, allowing them to add specific types of bacteria to study their impact.Lactobacillus, alone or together, and fed the bees either sugar water or sugar water and pollen. After five days, the gut bacteria were analysed. This revealed that bees fed on sugar water only had one dominant Lactobacillus species present in their gut, while bees fed with additional pollen harboured all four Lactobacillus species. Further analysis of these four bacterial species revealed that each of them activates distinct genes when grown on pollen, allowing the different species to consume specific nutrients from broken down pollen.Brochet et al. used this approach with Western honeybees to assess whether diet enables closely related bacteria to live alongside one another in the gut. First, they colonized bees that lacked gut microbes with four closely related bacteria of the genus These findings show that closely related bacteria can coexist in the gut by sharing the different nutrients provided in the diet of the host. Consequently, differences in dietary intake in honeybees and other animals may affect the diversity of gut bacteria, and potentially the health of an animal. Gut microbial communities are usually dominated by few bacterial phyla and families, but contain a high extent of species- and strain-level diversity . AccordiThe high concentration of nutrients and the structured environment of the gut may allow functionally redundant species or strains to coexist . The hosFew experimental studies have investigated the coexistence of bacteria in host-associated microbial communities. The high diversity in these ecosystems and the resistance of many host-associated bacteria to experimental manipulations introduce considerable challenges for such approaches . MoreoveApis mellifera) is composed of a few deep-branching phylogenetic lineages (phylotypes) belonging to the Firmicutes, Actinobacteria, and Proteobacteria phyla , Lactobacillus helsingborgensis (Lhel), Lactobacillus melliventris (Lmel), and Lactobacillus kullabergensis (Lkul). The four species are consistently present in the gut of individual honey bees suggesting that they can share the available niches and stably coexist despite their phylogenetic relatedness. Genomic analysis has revealed that these species share <85% pairwise average nucleotide identities (gANI) and exhibit high levels of genomic variation in terms of carbohydrate metabolism  associated with the Western honey bee. We first tested if the four species can establish in the gut of gnotobiotic bees under two different dietary conditions. To this end, we colonized microbiota-depleted bees with each of the four species, alone or together, and fed bees either sterilized sugar water (SW) or sterilized sugar water and pollen (SW+PG). Five days post-colonization, we assessed the bacterial loads in the gut by counting CFUs . After each passage (i.e. after 5 days of colonization), we used amplicon sequencing of a discriminatory housekeeping gene fragment  in combination with CFU counting to determine the absolute abundance of each species in the community. We observed clear differences between the two dietary conditions in the ability of the four species to coexist across the passages . In the five P5, . Only Lhver time .In summary, these findings show that the four species can stably coexist in vivo when bees are fed pollen, but not when they are only fed sugar water. This is consistent with the idea that pollen facilitates niche partitioning in the honey bee gut by offering competing species different ecological niches facilitating their coexistence.We next tested if the four species can also coexist in vitro, outside of the host environment, under different nutrient conditions. To this end, we cultured the species alone or together in minimal medium supplemented with either glucose (G), pollen extract (PE), or entire pollen grains (PG). All four species were able to grow when cultured alone under the three nutrient conditions . Growth 5 bacteria/ml) after about 11 passages (P11). In contrast, Lapi was stably maintained at high abundance (109 bacteria/ml) until the last passage . The absolute abundance of each strain was determined after every other passage by combining amplicon sequencing with qPCR . As for the in vivo experiment, we observed clear differences in the growth dynamics of the four species, both over time and between the glucose and the pollen culture conditions . In the  passage  and henc passage . None ofTo look at changes in community composition over time, we measured the community stability  in sliding windows of five passages. Little to no change in community stability was observed for the two pollen conditions throughout the experiment, whereas in glucose the community reached a stable state after ~11 transfers . To compLactobacillus species observed in vivo can be recapitulated in vitro in a simple co-culture experiment, suggesting that the partitioning of pollen-derived nutrients is sufficient for enabling coexistence. Similar results were obtained for a second in vitro experiment which included the same nutrient conditions, but was only conducted for ten transfers  or sugar water and pollen grains (SW+PG) (Given the impact of pollen on the coexistence of the four  (SW+PG) . Multidi (SW+PG) , indicat2FC ≥ |2| and p-value ≤ 0.01) between the two dietary treatments  to be differentially expressed  belonged to gene families with homologs in multiple species, differential expression was typically observed for just one of the species . This suBesides these species-specific transcriptional changes, a number of interesting functions were differentially regulated in more than one species. For example, we found evidence for citrate fermentation in Lhel and Lkul. Both species upregulated genes encoding a citrate lyase for the conversion of citrate into oxaloacetate and acetate in the presence of pollen . Lhel, LAltogether, these results suggest that the four species utilize different carbohydrate-related resources from pollen, which supports the niche partitioning hypothesis as the basis for coexistence.Lactobacillus species. We grew the four species in vitro in either co-culture or mono-culture, and with either pollen extract (PE) or glucose as growth substrate (G)  . As for rate (G) .2FC ≥ |2| and p-value≤0.01). As in vivo, Carbohydrate transport and metabolism (COG ‘G’) was the predominant functional category among the upregulated genes in the presence of pollen  were differentially expressed between co-culture and mono-culture treatments , , .Lactobacillus species.We could not find any consistent pattern across the four species in terms of COG category enrichment . MoreoveOur transcriptome analyses suggest that differences in sugar metabolism may enable the four species to coexist in the presence of pollen in vitro and in vivo. To assess species-specific metabolic changes when grown in pollen, we profiled the metabolome of the pollen extract medium before (t = 0 hr) and after bacterial growth (t = 16 hr) using Q-TOF-based untargeted metabolomics . We annoTo corroborate the species-specific utilization of flavonoids, we incubated each of the four species in base culture medium supplemented with rutin. We observed the formation of a yellow insoluble precipitate only in the wells incubated with Lmel . MetabolOther ions with species-specific abundance changes included a plant-derived glycosylated compound belonging to the iridioids class , a component of the outer pollen wall  and cyclic nucleotides  . Lhel wa2FC ≥ |2| and p-value ≤ 0.01, Student’s t-Test, BH correction) . We identified 113 metabolites of which 46 showed a significant change in abundance in at least one strain between timepoint 0 hr and 24 hr (logrection) . All fourection) . Moreoverection) , which crection) .Lmel consumed several simple monosaccharides  at a slower rate than the other species, although having a similar growth profile –5. This In summary, our metabolomics results show that the four species specialize in the utilization of different pollen-derived compounds, and that the observed metabolite changes are to some extent consistent with the transcriptional changes observed in the presence of pollen relative to the presence of simple sugars.Lactobacillus species. We disentangled the effect of the diet and the host on the interactions between the four species by serially passaging them through gnotobiotic bees or in culture tubes, under two nutrient conditions (pollen versus simple sugars). Our results show that the dynamics in the four-species community is governed by negative interactions, because the growth of each member was lower in co-culture than in mono-culture, independent of the environment (host or culture tube) and the nutrient condition (pollen or simple sugars). This is consistent with previous observations that negative interactions predominate in nutrient-rich environments  with a conserved and streamlined core metabolism and similar auxotrophies, suggesting overlapping nutritional requirements . Bacterial cells were then washed with 1xPBS, mixed in equal proportions, and inoculated at an OD600 = 0.05 in triplicates in 96-deep well plates (SIGMA) containing cfMRS medium supplemented with either 2% glucose (w/v), 10% pollen extract (v/v), or 10% pollen grains (v/v) in a final volume of 500 μL per well. Detailed information about pollen extract preparation can be found in the Supporting methods section of 2O to 80 mg of pollen grains crushed with the bottom of a 15 mL falcon tube. The plates were incubated for 24 hr at 34°C under anaerobic conditions without shaking (300 rpm). After 24 hr of incubation, an aliquot of each sample was subjected to CFU plating to enumerate the total bacterial load. Then, 1% of each culture (i.e. 5 μL) was transferred to a plate with fresh medium supplemented with the appropriate carbon sources and incubated again. These transfers were repeated 10, respectively, 20 times for the two independent experiments. After each transfer, cultures were washed once with 1xPBS and stored at −20°C for amplicon sequencing analysis. CFUs were counted after 24 hr and at the final transfer.Each of the four strains was cultured in liquid medium overnight for about 16 hr as described above. The cultures were re-inoculated at an ODThe relative abundance of the four strains across all transfer experiments was obtained using amplicon sequencing of a 199 bp long fragment of a housekeeping gene encoding a DNA formamidopyrimidine-glycosylase which allows to discriminate the four strains from each other .For the in vitro transfer experiments, the PCR fragment was amplified from crude cell lysates. They were generated by mixing 5 μL of culture with 50 μL of lysis solution, containing 45 μL of lysis buffer , 2.5 μL of lysozyme , and 2.5 μL of Proteinase K solution . The samples were incubated for 10 min at 37°C, for 20 min at 55 °C, and for 10 min at 95 °C, followed by a short spin before preparing the PCR . For the in vivo transfer experiment, DNA was isolated from the homogenized gut samples using the hot phenol protocol used in To amplify the gene fragment and to add the Illumina barcodes and adapters, the two-step PCR strategy published in To prepare the sequencing of the amplicons, DNA concentrations were measured using Quant-iT PicoGreen for dsDNA (Invitrogen). Each sample was adjusted to a DNA concentration of 0.5 ng/μL and 5 μL of each sample were pooled together. The pooled sample was loaded on a 0.9% agarose gel and gel-purified using the QIAquick Gel Extraction Kit (Qiagen) following the manufacturer’s instructions. The purified DNA was prepared for sequencing following the Illumina MiniSeq System Guide for ‘denaturate and dilute libraries’ and then loaded on a Illumina MiniSeq Mid Output Reagent Cartridge using the correspondent MiniSeq flow cell. Illumina reads were demultiplexed by retrieving the unique barcodes of the different samples and quality-filtered using Trimmomatic (Trimmomatic-0.35) (LEADING:28 TRAILING: 29 SLIDING WINDOW:4:15 MINLEN:90). Each forward and reverse read pair was assembled using PEAR (-m 290 n 284 j 4 -q 26 v 10 -b 33) , and theLactobacillus-specific primers as described in To obtain absolute abundance data for each strain, we combined the relative abundance data from the amplicon sequencing with CFU counts obtained from plating homogenized bee guts in the case of the in vivo experiments (see above) or by carrying out qPCR with For the in vivo RNA sequencing, microbiota-depleted bees were colonized with the four species community as described above and fed with either sugar water and pollen grains or with sugar water only. After 5 days of colonization, the rectums of five bees per treatment  were dissected and snap-frozen in liquid nitrogen in separate tubes containing glass beads . For RNA extraction, the tissue samples were suspended in 1 mL of TE buffer and homogenized using a bead beater . Then, 200 μL of an ice-cold STOP solution  was added to 1 ml of homogenate and snap-frozen again in liquid nitrogen. Tubes were then thawed on ice and a previously developed hot phenol RNA extraction protocol was followed . For theAfter the precipitation step, samples were treated with DNaseI (NEB) to degrade DNA. RNA samples were purified using Nucleospin RNA clean-up kit (Macherey-Nagel) following the manufacturer’s instructions. RNA was eluted in RNase free-water and stored at −80°C until further use. RNA concentration and quality were assessed using Nanodrop (ThermoFisher Scientific), Qubit  and Bioanalyzer (Agilent). High-quality RNA samples were selected to prepare RNA libraries. For the in vivo RNA sequencing, libraries were prepared using the Zymo-Seq RiboFree Total RNA Library kit (Zymo Research). The libraries were sequenced by the GTF facility of the University of Lausanne using HiSeq 4000 SR150 sequencing (150 bp reads) (Illumina). For the in vitro RNA sequencing, libraries were prepared following the protocol developed by http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). For the in vivo samples, trimmed reads were sorted with sortmerna-4.2.0 to select only the non-rRNA reads for the downstream analysis. Reads were mapped onto the genomes of the selected strains  method was used to estimate the common dispersion and the tag-wise dispersion. Finally, the differential gene expression was determined using the exact test with a false discovery rate (FDR) <5%. COG annotations were obtained from IMG/mer, and the enrichment analysis for COG categories tested using the Fisher’s exact test. Transcripts per million (TPM) were visualized using the Integrated Genome Browser software  For the in vivo samples, the reads were already demultiplexed by the sequencing facility. For both experiments, the reads were trimmed with Trimmomatic (Trimmomatic-0.35) (LEADING:30 TRAILING: 3 SLIDING WINDOW:4:15 MINLEN:20). The quality of the reads was checked using FASTQC . Three replicates of a pollen-extract dilution series  as well as undiluted pollen-extracts and water used for performing the dilution series were included in the metabolomics analysis. Because of the insolubility of flavonoid aglycones in a water matrix, metabolites from liquid cultures supplemented with rutin were extracted using a methanol-extraction protocol at the time of inoculation and after 16 hr of growth by adding 200 μL of methanol pre-cooled to −20°C to 100 μL of culture. Tubes were vortexed thoroughly and incubated for 5 min . Samples where then incubated at −20 °C for 1 hr and centrifuged . A total of 200 μL of the supernatant was transferred to a new tube and diluted 10 times in 70% methanol and 25 μL of each diluted sample was sent to Zürich in Eppendorf tubes sealed with parafilm on dry ice. For untargeted metabolomics analysis, each sample was injected twice  into an Agilent 6550 time-of-flight mass spectrometer  as detailed in +). Alternative annotation can be found in Metabolites were extracted from liquid cultures supplemented with 10% (w/v) pollen extract at the inoculation time and after 16 hr of incubation at 34°C. For each liquid culture sample, 300 μL was collected and centrifuged , then 200 μL supernatant was transferred to a new tube and stored at −80°C. After collection of all samples, they were prepared for metabolomics analysis. The samples were thawed on ice and centrifuged again , then diluted 10 times with ddH2FC values between the two time-points were calculated with respect to the mean intensity in the T0 time point. To identify pollen-derived ions, and distinguish them from background originating from culture medium and experimental noise, the ion intensities of the pollen dilution series were plotted for each ion and the R (2) of the obtained linear fit was extracted. In addition, we calculated the log2FC difference between undiluted pollen and water. The R (2) values were then plotted against the log2FC values, and stringent thresholds (R2 > 0.75 and log2FC > 2) were chosen to discriminate ions that are likely pollen-derived  were performed using t-tests with Benjamini-Hochberg (BH) correction for multiple testing. log-derived . All ion13C6 glucose [Cambridge Isotope laboratories]). A volume of 825 μL of cold methanol:water:chloroform (5:2:2) solution was added to the sample and vortexed for 30 s. The tubes were incubated at −20°C for 90 min and vortexed 2x for 30 s during the incubation. Tubes were centrifuged for 5 min at 10,000 g at 4°C. The supernatant was removed and extraction was repeated using 400 μL of ice cold chloroform:methanol (1:1), tubes were vortexed and left on ice for 30 min. Tubes were centrifuged 5 min at 8000 rpm at 4°C and the liquid phase was transferred to the previous extracted aqueous phase. A total of 200 μL of water was added and tubes were centrifuged 5 min at 8000 rpm. The aqueous phase was transferred to a 2 mL microcentrifuge tube. The aqueous extract was dried using a vacuum concentrator at ambient temperature overnight (Univapo 150 ECH vacuum concentrator centrifuge). Once dried, the samples were dissolved in 50 μL of 20 mg/ml methoxyamine hydrochloride in pyridine for 1.5 hr at 33°C followed by derivatization with N-Methyl-N-(trymethylsolyl)trifluoroacetamide  for 2 hr at 35°C.Soluble metabolites were extracted from liquid cultures supplemented with 10% pollen extract (w/v) at the inoculation time and after 8, 16, and 24 hr of incubation. For each liquid culture sample, 300 μL was collected and centrifuged . Then, 200 μL was transferred to a new tube, snap-frozen in liquid nitrogen, and stored at −80°C. Once that all the samples were collected, soluble metabolites were extracted. To extract soluble metabolites, tubes were thawed on ice, and 75 μL of sample was combined with 5 μL of 20 mM internal standard  and U-2FC between time-points were calculated as described above for the untargeted metabolomics analysis.Aliquots (1 μL) were injected on an Agilent 8890/5977B GC-MSD. The samples were injected in split mode (20:1) with an inlet temperature of 250°C. The VF-5ms (30 m x 250 μm x 0.25 μm) column was held initially at 125°C for 2 min, ramped at 5°C / min to 250°C, ramped at 15°C to 300°C, and held for 5 min. The MS was run in full scan mode (50–500 m/z) at a speed of 5 Hz. Peaks from the total ion chromatogram (TIC) were identified by matching retention times and spectra to an in-house library that was built by comparing selected T=0h and T=24h samples against the NIST library, as well as our library of analytical standards. Compounds are noted as either confirmed with our own standards, or the best match and associated matching factor against the NIST library are reported . Peaks whttps://github.com/silviabrochet/Brochet_2021_eLife, copy archived at swh:1:rev:237a27f757296372f0333d298dfb7c765686fe03; The complete custom code for all the analyses is available on GitHub:  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Acceptance summary:Brochet et al., investigate how closely related microbial symbionts coexist in the gut as stable communities. To address this question, the authors use an elegant model, relying on honeybees colonized with a defined bacterial community. They provide compelling empirical evidence that a nutritionally complex diet, together with microbial metabolic diversity, play a key role, enabling species partitioned resource utilization and co-existence of closely related honeybee gut microbiota.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, including Karina Xavier as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Christian Rutz as Senior Editor.Thank you for submitting your article "Niche partitioning facilitates coexistence of closely related gut bacteria" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this decision letter to help you prepare a revised submission.Essential Revisions (for the authors):eLife. Below is a list of essential revisions to help improve the clarity and presentation of the work. Additionally, you can find at the end of this letter, for context, the separate "recommendations for the authors" written by the individual reviewers. We hope you will use this information to improve your manuscript, but you do not need to reply to the comments raised by the reviewers -- you will only need to reply to the following essential points summarized here:In general, all three reviewers found the work exciting and solid, and thus suitable for publication in 1) The experimental design chosen for the in vitro cultures is not clear. It is not clear why a sucrose solution (SW) was used for the in vivo studies and glucose was used for the in vitro studies and why a "simple sugar + pollen" group was not included in the in vitro study. Since we believe it is unlikely to affect the major findings of the paper where comparisons of simple sugars and more complex diets are made, we are not asking for additional experiments. But we think it is important that you explain your rationale, and discuss if and how these choices may impact your results.2) You do not explore much the mechanisms that can explain the lack of persistence observed under the simple diet. We acknowledge that a comprehensive understanding of the mechanisms involved in the simple diet are beyond the scope of the present paper, given that here you provide a clear proposal of the more interesting problem, that is the co-existence and persistence in the complex diet. However, we think that the paper could improve with an added analysis and/or discussion with the current data of possible mechanisms involved .3) Please justify more clearly your choice of study organisms, and in the discussion, comment on how your findings can be extended to the related bumble bee gut microbiome .4) It is important to make available the genome-wide data for the RNA-seq results, not just for selected genes.Reviewer #1 (Recommendations for the authors):1. The current study does not explore what happens in the simplified diet both in vitro and in the Bee gut. The authors propose in the discussion that in the presence of few and simple carbon sources (sugars) there is competition for nutrients and competitive exclusion is driving extinction of most species and domination of a single species. However, there is not much experimental support for this conclusion. Can this be explained by competitive exclusion? Is there transcriptomics (or genomic data) in this study that supports this possibility? If the data cannot provide strong hypothesis for competitive exclusion by nutrition competition in the simplified environment the authors should discuss other possible mechanisms that could explain such phenotypes in the simplified diets.2. One point that it is not address is the number of calories provided by the different diets. Could it be that the simplified diet is also lower in calories (and thus more a limiting) and that differences in calories (as opposed to the different number of nutrient sources) could contribute to the results observed?3. Regarding experimental design it is not clear why a sucrose solution (SW) was used for the in vivo studies and Glucose was used for the in vitro studies. Additionally, in vitro the results with pollen grains were better in promoting persistence than the Pollen extracts, but the follow up studies were performed with pollen extract that enables persistence but not as well. The authors should explain the rationale for these choices and discuss if these choices may or may not have impact on the results obtained.Reviewer #2 (Recommendations for the authors):Overarching comments:1. My ability to make specific comments was hindered by the lack of line numbers and page numbers in the merged document. Perhaps these fell out of the compiled version, but if not, I urge the authors to include page numbers and line numbers in their future submissions.2. I found it confusing that the bee diet conditions contrasted were pollen+sugar water or just sugar water, but the culture conditions contrasted were pollen (extract or granulated) or sugar water. I think that this may impact some statements that the authors are making regarding the comparison between in vitro and in vivo. I've noted specific cases in my comments below but wanted to reiterate this overarching concern here. Could the authors comment on the decision to exclude the conditions pollen only bee diet and/or in vitro pollen+sugar water from this study?Introduction3. You are covering a lot of ground in the introduction- one thing that got lost for me until the second read through was that I think you specifically choose the Firm5 subset of the bee gut microbiota because of close taxonomic relatedness. Consider motivating the paragraph beginning "One of the most prevalent phylotypes…" so that this is clearer.Lactobacilli chosen specifically, over other major phylotypes? What fraction of the total bee gut diversity does this phylotype represent? Is there generally only one strain of a species present in the bee gut? How were strains chosen as representatives?4. I think that you have very well thought out reasons for choosing the species and strains for this study, and I think that it would strengthen the manuscript to spend a little more time on this in the introduction. I'm not very familiar with the bee gut system, and my first impression was to wonder why so few strains were tested, and why the strains chosen were representative/good choices. Some of this you mentioned in the discussion- I think it would strengthen the manuscript to present these details sooner. Specific questions that I think need to be addressed in the introduction are: Why were Lactobacillus species in the honey bee gut depends on the host dietSection: The coexistence of four related 5. I think it would help the reader to unpack the idea in your summary paragraph. This is the first place where niche partitioning is mentioned (partitioning is referred to in the abstract), and you don't define the term. I think the link between niche partitioning and your results is that pollen is a complex resource, and sugar water is not. From consumer-resource models, one would expect that there would be only one dominant competitor on one resource. If multiple resources are available, then one would expect that the community would be as diverse as the # of resources. Since pollen has >4 resources, coexistence is possible if strains partition the resources they consume and do not compete directly.not suggesting more experiments here, but have you considered seeing whether you could support the 4-member community on sugars found in pollen and detected as consumed by the metabolomics? Showing that this could work in a defined environment with these strains would be a nice  extension of the resource partitioning idea.6. I am Lactobacillus speciesSection: in vitro co-culture experiments recapitulate the diet-dependent coexistence of the four 7. In culture, you observe higher yield for all four strains on glucose than on pollen or pollen extract, but you see the opposite in the bee gut. Have you followed up on why this might be? I ask because you are using yield as a proxy for 'niche space', and I think a fundamental assumption there is that resources that define the niches are limiting. If in culture a different resource is limiting (let's say resource B), then all of your strains might reach the same yield and stop growing due to resource B limitation before they have consumed all of the glucose or pollen. One way to determine whether glucose/ pollen limits yield would be to grow your strains on different concentrations of glucose/ pollen- if yield is dependent on the resource you are adding, you should see a linear relationship between the yield and the amount of glucose/pollen. I think that establishing that pollen or glucose is truly growth limiting in these experiments is critical if you want to make the statement that "portioning of pollen-derived nutrients is sufficient for enabling coexistence".Lactobacillus species observed in vivo can be recapitulated in vitro in a simple co- culture experiment, suggesting that the partitioning of pollen-derived nutrients is sufficient for enabling coexistence." I'd encourage the authors to re-phrase the summary and header for this section: I think that what is shown is that strains coexist on pollen but not on glucose, not that the diet-dependent coexistence has been recapitulated in vivo.8. Your experiments in bees use either sugar water or sugar water+pollen grains. But the experiments in this section use either pollen grains, pollen extract, or sugar. It would be a clearer comparison with the in vivo experiments if you showed coexsistence on pollen+sugar water. Without comparing the same conditions, I don't think that you can conclude from this section that "In summary, these findings show that the dietary-dependent coexistence of the four Lactobacillus species upregulate divergent carbohydrate transport and metabolism functions during gut colonization in the presence of pollenSection: The four 9. I find the down-regulation of amino-acid metabolism/transport genes in the SW+pollen condition to be interesting but counter-intuitive. Pollen is supplying dietary amino acids, whereas SW alone does not. Sometimes transcription becomes elevated when substrates are scarce, is there evidence in the literature that this could be the case?10. Are the sugars transported/catabolized by the encoded COG G genes present in pollen?11. I'm surprised that you didn't encounter problems with reads mapping to multiple reference genomes with HTSeq. The taxa share 85% gANI. I'd expect there to be some frequency of 100-mers sharing 1 SNP, which I understand from the results was the threshold for mapping reads to the genomes. I don't see any discussion of this in the text. Could you comment on what controls were done to explore the frequency of cross-mapping of reads?12. Figure 4. My understanding of GH families is that they can be quite broad. For example, the GH16 family encompasses 15 different types of enzyme, not just the one cited in the figure. Could the authors provide more detailed information about why specific activities were attributed the GH families highlighted in the figure panels?Section: Transcriptional responses to pollen are concordant in vivo and in vitro13. Echoing my comments in the section about in vitro co-culturing, I'm wondering why the comparison was not glucose vs. glucose+pollen. In the host, when there is pollen available glucose is also available. Because of this, some of the differences between the in vitro pollen condition and the in vivo pollen+glucose condition might be transcripts that differentially expressed in response to glucose AND pollen, and not transcripts that are only expressed in the host. Because of this, I don't agree with the interpretation of the data that the transcriptional responses are concordant, and I think that the interpretation of differential expression to suggest that "genes are specifically expressed in vivo" is overly strong.14. I think that these data can be used to make the overall point that the authors are presenting: the four strains do express different sugar-catabolism related genes when grown on pollen. Rather than comparing directly to the RNA-Seq from the host, I'd encourage the authors to focus instead on what their data show about the regulation of COG-G category genes in response to nutrient source. Specifically:a. How does gene expression change between mono- and co-culture on the same carbon source?b. If the strains differentially expressing genes when they are co-cultured, is a more diverse profile of COG-genes expressed, or do the strains tailor their expression?c. I think there is only one direct comparison: co-culture communities on glucose. Are the COG genes differentially expressed the co-culture glucose community in vitro also the ones that are differentially expressed in the context of the in vivo community grown on glucose?15. Supplementary Figure 3 indicates that some host samples profiled by RNASeq did not cluster by mds because of low read mapping. Please clarify if these samples were included in other analyses involving RNASeq data or if they were removed. If they were included, please comment on the rational for inclusion given low reads and lack of clustering.Lactobacillus speciesSection: Metabolomics analysis reveals differences in flavonoid and sugar metabolism across the four 16. In studies of non-host associated microbial communities, an observation is that there is strain-level heterogeneity with respect to the profile of carbon sources consumed. From the metabolomics data, it looks as though some strains don't consume some carbon sources. Do you know whether all 4 strains can grow on the different carbon sources that you find by metabolomics in pollen, in particular the carbon sources that are most abundant in pollen?Discussion:17. I don't think that the comparison to hydra is fair- hydra microbiota assemble on the host epithelium, not a gut lumen.Lactobacillus in the bee gut (the rectum) is known, and that other microbial taxa of the bee gut microbiota display different tissue tropism. I think that this is a really interesting point. Would we expect the dynamics of bee gut taxa with a closer host association to be different? Was the choice of strains motivated by the tissue tropism?18. You note that the primary site for colonization of 19. I'm surprised that you don't see evidence of cross-feeding on the complex resource. Do you think that this is due to the taxonomic similarity of the isolates (do you think crossfeeding is restricted to other phylotypes), or the small # used in this study, or that the community doesn't become limited for the resources in pollen? Can you differentiate among these possibilities with your data? 1) The experimental design chosen for the in vitro cultures is not clear. It is not clear why a sucrose solution (SW) was used for the in vivo studies and glucose was used for the in vitro studies and why a "simple sugar + pollen" group was not included in the in vitro study. Since we believe it is unlikely to affect the major findings of the paper where comparisons of simple sugars and more complex diets are made, we are not asking for additional experiments. But we think it is important that you explain your rationale, and discuss if and how these choices may impact your results.It is true that the in vitro and in vivo nutrient conditions were not identical, and retrospectively it would have been more consistent to compare pollen versus glucose both in vitro and in vivo. However, we need to keep in mind that whatever we would have fed to the bees, would have been pre-digested and partially absorbed by the host tissue in the midgut before it reaches the bacteria in the hindgut. For example, sucrose is cleaved by the bee into glucose and fructose and absorbed in the midgut. Consequently, the in vivo and in vitro conditions will never be the same even if we would have used identical treatments. Despite these inconsistencies between the in vivo and in vitro treatments, we agree with the reviewers that the experimental design is still valid to test the main hypothesis of our study, i.e. a complex, pollen-based diet facilitates coexistence, while the provisioning of simple sugars leads to the dominance of a single strain independent of the environment (culture tube versus host).We have revised the discussion to acknowledge that the different sugar conditions may explain the differences in the relative abundance of community members between the in vitro and in vivo experiments. This can be found in the revised manuscript on lines 402-422.2) You do not explore much the mechanisms that can explain the lack of persistence observed under the simple diet. We acknowledge that a comprehensive understanding of the mechanisms involved in the simple diet are beyond the scope of the present paper, given that here you provide a clear proposal of the more interesting problem, that is the co-existence and persistence in the complex diet. However, we think that the paper could improve with an added analysis and/or discussion with the current data of possible mechanisms involved .We have addressed this point by adding a section in the discussion where we explain in more detail why we hypothesize that the lack of persistence of the four strains in the presence of the simple nutrients is due to competitive exclusion. The key point here is that each strain individually colonizes the bee gut, respectively grows in the culture tubes, in presence of the simple nutrients. However, when co-cultured only one strain persists in the simple nutrient condition, while in the presence of pollen all strains persist. The difference between these two conditions is the presence of pollen, i.e. a nutrient-rich substrate. The competitive exclusion principle states that two individuals can’t coexist if they compete for the same resource. The strain with the slightest growth advantage over the others will win, be it because it can utilize the available nutrient faster or actively inhibit the others in their growth. This is what our data suggests. We cannot see any other possible mechanism explaining this data.The section in the discussion is found on line 423-435.3) Please justify more clearly your choice of study organisms, and in the discussion, comment on how your findings can be extended to the related bumble bee gut microbiome .Lactobacillus Firm5 on line 75-89. In brief, we chose this phylotype because (i) it is one of the most abundant phylotype of the honey bee gut microbiota, (ii) it is a very diverse phylotype that diverged into four different species which are different in their carbohydrate-utilization related gene content, (iii) the four species were shown to consistently coexist in vivo in conventional bees.We have addressed this point by restructuring the part of the introduction dedicated to Lactobacillus Firm5 coexist in other bees. Two different lineages of Firm5 have been found in bumble bees, but it is unclear if they coexist, neither is it known if these are the only two lineages associated with bumble bees. We feel that there is currently too little data to discuss how our results would relate to diversity found in other bee species. However, we added a sentence in the discussion which states that given the fact that the coexistence is based on pollen nutrients, it follows that dietary differences are likely to influence the diversity of Lactobacillus Firm5 in divergent host species. We give as an example Apis cerana, which harbors only one species of Lactobacillus Firm5. See line 479-488.As for the bumble bee, or other social bee microbiota, we don’t know yet how many different lineages of 4) It is important to make available the genome-wide data for the RNA-seq results, not just for selected genes.We have addressed this point by adding all differentially regulated genes in the supplementary dataset 9, in addition to the subset of significant ones. Please, note that in the GEO submission we included all the raw data used for the analysis and the scripts can be found in the GitHub link indicated in the text.


The aims of this study were to examine the prevalence of hospital contact in the year prior to suicide in Victoria, Australia, and to compare characteristics among those who did and did not have contact in the year prior to suicide.The study was a data linkage cohort study of 4348 Victorians who died by suicide over the period 2011–2017. Data from the Victorian Suicide Register (VSR) was linked with hospital separations and Emergency Department (ED) presentations datasets by the Centre for Victorian Data Linkages (CVDL). The main outcomes were: (1) hospital contact for any reason, (2) hospital contact for mental-health-related reasons, and (3) hospital contact for intentional self-harm. Unadjusted and adjusted odds ratios were calculated as the measures of association.In the year prior to suicide, half of the decedents (50.0%) had hospital contact for any reason (n = 2172), 28.6% had mental-health-related hospital contact (n = 1244) and 9.9% had hospital contact for intentional self-harm (n = 432).males aged 75+ years and females of all ages were significantly more likely to have hospital contact for any reasonfemales aged 10–24 years and 25–49 years were significantly more likely to have mental-health-related hospital contactfemales aged 10–24 years and 25–49 years had 3.5 times and 2.4 times the odds of having hospital contact for intentional self-harm.In the year prior to suicide, when compared with males aged 25–49 years (the reference group):The comparatively high proportion of female decedents with mental-health related hospital contact in the year prior to suicide suggests improving the quality of care for those seeking help is an essential prevention initiative; this could be explored through programs such as the assertive outreach trials currently being implemented in Victoria and elsewhere in Australia. However, the sizeable proportion of males who do not have contact in the year prior to suicide was a consistent finding and represents a challenge for suicide prevention. Programs to identify males at risk in the community and engage them in the health care system are essential. In addition, promising universal and selective interventions to reduce suicide in the cohort who do not have hospital contact, include restricting access to lethal means and other public health interventions are also needed. Many people who die by suicide have had contact with hospitals  in the weeks or months leading up to their death –5. TheseConsequently, across Australia and in many other jurisdictions, suicide prevention initiatives have been designed to target the population that access hospitals. For example, the Australian federal government has funded 12 National Suicide Prevention Trials involving primary health network (PHN) regions . One of does not have contact with hospitals prior to suicide, other interventions can be developed specifically targeting those people who would not be helped by hospital outreach initiatives.By better understanding characteristics of people who have hospital contact prior to suicide, current strategies such as those mentioned here, can be further developed to improve access to services, encourage service engagement, and improve the quality of the services currently being provided. Conversely, by understanding who Three previous reviews were identified on the prevalence of contact with health care prior to suicide. The first review published in 1998 concluded that up to 41% of people who died by suicide had contact with inpatient psychiatric care in the year prior to their death . Both thThis study is a retrospective cohort study of 4348 Victorians who died by suicide over the period 2011–2017 and examines hospital contacts that occurred in the cohort in the 12-months prior to their death.Suicide data from the Victorian Suicide Register (VSR) was linked with hospital separations from the Victorian Admitted Episodes Dataset (VAED) and Emergency Department (ED) presentations from the Victorian Emergency Minimum Dataset (VEMD). The Victorian Department of Health and Human Services (DHHS) was the source of the data from the VAED and VEMD. The VSR is an ongoing register established by the Coroners Court of Victoria (CCOV) to collate detailed information on suspected suicides to assist Coroners with their investigations and support their prevention mandate. All suspected suicides in Victoria are legally required to be reported to the Coroner for investigation . The VSRVSR data on all suspected and Coroner-determined suicides was supplied for the period 1 January 2011 to 31 December 2017. In hospital datasets, contacts were limited to those that occurred in the year prior to suicide using the date of death recorded in the VSR and dates of contact provided in admissions and ED presentations datasets. Hospital admissions with an admission source indicating a transfer from another hospital or statistical separation  in consecutive records were all considered to be part of the same episode and recoded to be the one hospital admission. Any hospital episode  that ended in “death” was excluded as it was considered to be a result of the fatal (suicide) incident. The only exception to this were episodes where the intentional self-harm was coded as occurring in a health service area–these episodes were retained as they were most likely inpatient suicides.A hospital contact was considered to be mental health (MH) related if it was for reasons related to mental disorder, intentional self-harm and/or suicidal ideation. Hospital admissions were flagged as MH related if any of the 40 diagnosis codes were mental disorder (F00-F99), intentional self-harm (ISH) (X60-X84) or suicidal ideation (R45.81). ED presentations were flagged as MH related if any diagnosis code was mental disorder (F00-F99) or suicidal ideation (R45.81) or if the “human intent” variable indicated ISH. Records with a primary diagnosis code for a general psychiatric examination (Z046) or a history of ISH (Z915) were also included in the definition of MH related ED presentations.The area-level measure of socioeconomic circumstances was taken from the Socio Economic Index For Areas (SEIFA) . The IndDescriptive analysis was carried out to identify sociodemographic characteristics of those who had hospital contact and those who did not. Chi square tests were conducted on categorical data to establish differences between the groups. The outcome variables were hospital contact yes/no, MH related hospital contact yes/no and ISH hospital contact yes/no).Statistical modelling was carried out to determine sociodemographic factors associated with hospital contact. Univariate and multivariate logistic regression analysis including main effects of all variables was conducted using IBM SPSS version 26. Unadjusted and adjusted odds ratios were calculated as the measures of association. Due to a significant interaction between age and sex, these variables were combined and categorised into m10-25, m25-49, m50-74, m75+, f10-25, f25-49, f50-74, f75+.This study was approved by the Monash University Human Research Ethics Committee; application number 14647. Consent was waived by the ethics committee as data was either collected for administrative purposes  or participants were deceased (VSR data). All datasets were fully anonymised before being supplied to university researchers.hospital contact (HC) for any reason during the year prior to suicide (n = 2172). Female decedents were significantly more likely than male decedents to have HC. This pattern was observed overall and in every age group with the exception of 75+ years   had = .004]) .MH HC during the year prior to suicide (n = 1244). Female decedents were significantly more likely than male decedents to have MH HC in the year prior to suicide. This pattern was observed overall and and in every age group  (More than one-quarter of the suicide cohort (28.6%) had = .016]) .HC for ISH during the year prior to suicide and female decedents were significantly more likely than male decedents to have ISH HC. This pattern was observed overall and among those aged 10–24 years and 25–49 years   had  .0001]) .Compared with males aged 25–49 years, males aged 75+ years had 1.6 times the odds of having HC, and females of all ages were significantly more likely to have HC  .For MH HC prior to suicide, when compared with males aged 25–49 years, older males aged 50–74 years and 75+ had reduced odds of MH HC (0.7x and 0.4x respectively), whereas females aged 10–24 years and 25–49 years were more likely to have MH HC (1.6x and 1.5x respectively). Rural/regional residents had lower odds of having MH HC when compared to metropolitan residents (0.7x) .For ISH HC, females aged 10–24 years and 25–49 years had 3.5 times and 2.4 times the odds of having ISH HC when compared to males aged 25–49 years. The oldest males (75+ years) had reduced odds of ISH HC (0.4x). Rural/regional residents had lower odds of having ISH HC when compared to metropolitan residents (0.7x) and persons in SEIFA quintiles 2 & 3 were more likely than persons in quintile 1 to have ISH HC (1.6x) .There were many groups that had very low proportions of decedents who had HC in the year prior to suicide . With reThis is the first study that has linked Victorian Suicide Register (VSR) data with hospital administrative datasets to gain insight into the incidence of hospital contact in the year prior to suicide. Previous work using the VSR has examined service contacts and/or history of previous mental-health-related issues but used VSR coded data in isolation potentially raising issues of accuracy and completeness ,12–14.Half of the 4348 decedents had hospital contact for any reason in the year prior to suicide, 29% had contact for mental health related reasons and 10% had contact for intentional self-harm. These results regarding mental health contacts are consistent with a 2018 review that found 26% of suicide decedents had contact with inpatient or outpatient mental health services in the year prior to suicide .Although almost three-quarters of the suicide cohort were male (74%), females were significantly over-represented in hospital-treated contacts for most age groups. This may simply reflect difference in the prevalence of help seeking by males and females in the general population but may also provide valuable suicide intervention and prevention guidance. The comparatively high proportion of female decedents  with mental-health related hospital contact in the year prior to suicide suggests improving the quality of care for women who are seeking help is an essential suicide prevention initiative; this could be explored through the assertive outreach trials currently being implemented in Victoria and elsewhere in Australia. The sizeable proportion of men who do not have hospital contact in the year prior to suicide was a consistent finding across age groups, regions and SEIFA quintiles, and represents a particular challenge. Programs to identify males at risk in the community and engage them in the health care system are essential. In addition, promising universal and selective interventions to reduce suicide in the cohort who do not have hospital contact, include restricting access to lethal means –18 and oOnly 13.9% of men and 25.6% of women decedents aged 75 years and older had mental-health-related contact recorded in the year prior to suicide; these numbers reduce to 3.7% and 8.9%, respectively, when contact for intentional self-harm is examined. These findings could suggest that suicide among older people in the Victorian population may be related to physical illness and are consistent with previous international  and VictAs is common to all data linkage studies using administrative data, the information captured is limited to what was recorded in the administrative data. The quality and coding issues inherent in the different hospital data sources can influence the utility of the data for informing suicide prevention initiatives. Information about emergency presentations at health services without a 24-hour ED are not available in the VEMD, so the study covers the majority, but not necessarily all, of the emergency presentations among the cohort. Another data issue relates to known data quality issues with the VEMD regarding the coding of the “human intent” variable . Of partThe findings of this research suggest that interventions at the universal, selective and indicated levels are all needed to prevent suicide and provide support for researchers that have argued for public health responses, interventions and upstream prevention to decrease suicide –27. A co


In recent decades, global oil palm production has shown an abrupt increase, with almost 90% produced in Southeast Asia alone. To understand trends in oil palm plantation expansion and for landscape-level planning, accurate maps are needed. Although different oil palm maps have been produced using remote sensing in the past, here we use Sentinel 1 imagery to generate an oil palm plantation map for Indonesia, Malaysia and Thailand for the year 2017. In addition to location, the age of the oil palm plantation is critical for calculating yields. Here we have used a Landsat time series approach to determine the year in which the oil palm plantations are first detected, at which point they are 2 to 3 years of age. From this, the approximate age of the oil palm plantation in 2017 can be derived. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13899713 This rise has deeply impacted natural forest ecosystems5 and biodiversity7, while contributing significantly to climate change by releasing carbon from converted forests and peatlands into the atmosphere9.Driven by food and industrial demand, the production of global oil palm has more than doubled over the last two decades. Between 1997 and 2018, oil palm plantations expanded from 10 to 21 Million hectares (Mha), while crude palm oil production increased from 100 to 300 million tons (Mt)10. Therefore, knowing the exact extent and age of plantations across a landscape is crucial for landscape-level planning to allow for both sustainable oil palm production and forest conservation. Based on the land sparing theory11, the improvement of oil yields has been put forward as a means of reconciling oil production and forest conservation13.Oil palm is known to be the most efficient oil-producing plant globally. Oil palm yields, however, vary dynamically with plantation stand age and location. They increase during the youth phase of the first seven years, reach a plateau during the prime age of 7–15 years, and then slowly start to decline before palms are replaced at the age of 25–30 years14 or semi-automatic approaches15, blended with extensive field information17. These approaches are, however, very labour intensive.The combination of high-resolution satellite records and cloud-computing infrastructures to handle big data can now be deployed to map oil palm extent and age, independent of official statistics. The latter are usually provided annually at a rather coarse spatial scale , they may be incomplete or not updated regularly, and the quality is uncertain. Hence, they do not provide detailed spatial extent or age. Several recent studies have applied remote sensing techniques to estimate the extent of oil palm plantations in Southeast Asia. Studies aimed at quantifying plantation extent on a national scale or larger have deployed visual, expert-based interpretation methods20. Recent experimental studies have demonstrated the usability of the European Union’s free and open Copernicus Sentinel data to detect oil palm plantations23. Sentinel data have proven particularly useful in detecting smallholder and industrial plantations24 and plantation age25, which, in turn, is a good predictor of oil palm yields25.The availability of radar imagery has given rise to a new generation of remote sensing studies benefiting from penetration of clouds, improved resolution and high revisiting frequencyproductive oil palm plantations in Indonesia, Malaysia and Thailand, the three countries that in 2017 contributed to almost 90% of global oil palm production1, at a 30 m resolution using remote sensing. Productive oil palm plantations comprise both industrial and smallholder plantations that have an age of at least three years. We have excluded roads, mill infrastructure and other land uses occurring in oil palm-dominated landscapes. The methodology for determining the extent of plantations builds on Sentinel-1 microwave data whereas the Landsat archive has allowed us to build a time series reaching back to the 1980s to determine when the plantation was established. The methodology identifies oil palm plantations of all types independent of weather and daylight conditions or ground-sourced information amidst other tree crops in the landscape. We determined the accuracy of the resulting oil palm map against an independently collected and interpreted sample of very high-resolution satellite images.Here we have mapped the extent and the age of 26.The main input data used in the detection of oil palm include: (1) the Copernicus Sentinel-1 microwave backscattering time series; (2) the Landsat multispectral time series; and (3) additional auxiliary data sets. All data acquisition and processing were performed using the Google Earth Engine (GEE)Oil palm area detection requires preparation of Sentinel-1 data and the application of an unsupervised oil palm detection routine based on the Normalized Difference Vegetation Index (NDVI) and texture features. The combination of NDVI and texture is important to reliably distinguish oil palm from other woody vegetation. To this end, an annual mosaic of backscatter coefficients for 2017 was produced by processing all available Sentinel-1 data (1A and 1B) with single polarizations . The main objectives in producing this annual mosaic were: (i) to build a baseline C-band backscattering coefficient mosaic of Southeast Asia; and (ii) to assess radar backscatter changes between different years in land cover change studies.The value of the output pixels corresponds to the ‘mean’ value of the Sentinel-1 collection after: (1) outlier removal ; and (2) normalization for incidence angle to remove some of the ascending/descending variation. In addition to VV and VH yearly means, we added the VH/VV difference and the summed average (SAVG) texture metrics from the Grey Level Co-occurrence Matrix around each pixel of every band (radius of three pixels). To calculate the SAVG, all input data were scaled to bytes.We deployed a stratified unsupervised classification algorithm to detect oil palm without training data; a conceptual overview of the workflow is presented in Fig. 29, and removing areas falsely classified as oil palm from within the mangrove area.Post-processing was used to apply rule-based corrections to the intermediate oil palm map resulting from the unsupervised classification. These corrections were necessary in settlements, mangroves and sloping areas, as well as for other objects like roads running through the plantations. From a reflectance and backscattering viewpoint, settlements are very heterogeneous, such that the backscattered values in some parts of settlements are similar to those observed from oil palm fields. Setting a minimum NDVI value of 0.5 removed settlements from the area classified as oil palm. Similar confusion exists with mangroves. This was resolved by deploying a mangrove mask, derived from three regional and global mangrove mapsde facto minimum mapping unit of 900 m2 for the oil palm map. For a refinement of the final product, we removed pixels with NDVI less than 0.5, which at times have been erroneously introduced by the majority filtering in small non-palm areas such as roads running through plantations. We used digital elevation data from the Shuttle Radar Topography Mission (SRTM) at a resolution of one arc-second (approximately 30 m) to define terrain slope. This was required to correct the Sentinel 1 signal with respect to hillshades30.On Kalimantan, coverage by Sentinel satellites is in descending orbit only, i.e., imagery is always taken from the same perspective. This results in gaps, notably in hilly areas. To account for these gaps on south sloping areas in Kalimantan, we applied a post-processing majority filter within a 3 × 3 (30 × 30m) moving window to fill gaps in detected oil palm plantations. This process introduced a 32. We then generated a time series of BSI development starting from 1984. To smooth the time series and to remove noisy observations, we calculated the median BSI from a moving window of 12 months of the time series and omitted any 12-month time period in which we had less than three observations.As an oil palm canopy develops over time, the area fraction of bare soil decreases, which can be detected with optical remote sensing through the bare soil index (BSI). To estimate plantation stand age, we moved backwards in time from oil palm plantations detected in 2017 until bare soil exceeded a certain value in young stands. For this we used surface reflectance values from the Landsat 5 and Landsat 7 image collections. We masked out clouds for every scene using the pixel quality attributes generated from the CFMASK algorithmth percentile as a threshold value. The 95th percentile coincides with the presence of very young plantations (two to three years of age with an open canopy and a high BSI), as confirmed by the visual interpretation of high-resolution imagery. For all pixels above the threshold in 2017, we analysed the BSI time series backwards in time where canopy closure is defined as the point when the BSI index drops below the cut-off value. This means that the resulting oil palm age map will register the first observation of oil palm plantations at an age of two to three years.To determine a reference BSI for oil palm, we calculated a histogram of BSI values from established plantations in 2017 and used the 95http://dare.iiasa.ac.at/85/)33. It consists of a 16-bit GeoTIFF at a resolution of 30 m with a single attribute value, i.e., the year in which the oil palm plantation was first detected. At this point, the plantation is 2 to 3 years of age. The data values range from 0 to 37 where 0 is the No Data value. Values 1 to 3 are not present and a value of 4 corresponds to the year 1984, the first year oil palm was detected, and each consecutive number represents the next year, i.e., 5 is 1985, while the maximum value of 37 corresponds to 2017. Table The data set is publicly accessible for download from the permanent DARE repository housed by the International Institute for Applied Systems Analysis (IIASA) , where volunteers evaluated whether oil palm was present or not at each sample location : the choice of the right variety (dura vs. tenera); inadequate water management, notably on peat soils; inadequate fertiliser doses, in particular potassium; diseases; and labour shortages. Furthermore, interlinkages exist between overaged plantations and these causes: high-yielding tenera varieties have been becoming more widely used over time, meaning that old plantations still contain higher fractions of dura. Many diseases tend to strike harder on old plantations and harvesting of older and hence higher palms requires more labour.Woittiez 42.The remote sensing product presented here can be used to estimate productivity and assist high-level planning of the oil palm sector to adapt its strategies regarding plantation management, e.g., with respect to the need for renewal of aging plantations as highlighted by Wahid and SimehFinally, the generation of such a data set in near real-time could provide timely, independent, transparent and consistent monitoring of palm oil production across large geographical areas, bridging the gap between technology and land policy.Supplementary Information


In 2012, there were 2,454 cases and 1,676 deaths from cervical cancer in Kenya. Human Papilloma Virus (HPV) is responsible for 99% of all cervical cancers. National cervical cancer prevention guidelines recommend HPV vaccination among HPV-naïve pre-adolescent girls' prior to onset of sexual activity preferably through school-based interventions. Similarly, Voluntary Male Medical Circumcision (VMMC) programs also reduce sexually transmitted infections like HIV, and ideally should also be conducted prior to the onset of sexual activity. The Families Matter! Program (FMP) is a school-based evidence-based HIV prevention intervention for parents and guardians of pre-adolescents aimed to enhance protective parenting practices in order to reduced sexual risk-taking among adolescents. In this paper we describe how we will recruit a cohort of 55 parent-child dyads in a primary school in Kisumu County then implement and evaluate an adapted FMP program that incorporates VMMC promotion and HPV vaccination in conjunction. It is anticipated that the intervention will enhance parental-child communication about sexual matters, promote safe sexual practices and uptake of biomedical prevention interventions and overall reproductive health among the pre-adolescents Cervical cancer contributed to 21% of the cancer cases among women.3 In Kenya in 2012, there were 2,454 cases of cervical cancer cases, 1,676 cancer deaths and a cervical cancer screening coverage rate of 3.2% among women aged 18-69 years.5 Human Papilloma Virus (HPV) is responsible for 99% of all cervical cancers. HPV subtypes 16 and 18 cause up to 70% of all cancers.1 Human Immunodeficiency Virus (HIV) is postulated to facilitate the progression of HPV infection to cervical cancer.6 The prevalence of cervical cancer is therefore unsurprisingly highest in developing countries which also bear the burden of HIV.7Cervical cancer is the second most common cancer in women living in resource limited settings. There were approximately 445 000 new cases in 2012 in resource limited settings contributing to 84% of the new cases worldwide. In 2012, approximately 270 000 women died from cervical cancer; more than 85% of these deaths occurring in low- and middle-income countries.8 Whilst awareness of cervical cancer has been shown to promote uptake of HPV vaccine, 9 barriers exist, and studies suggest that some Kenyan parents of adolescent children fear that that administration of an HPV vaccine could encourage their daughters to have sex. Published literature documents HPV vaccine uptake of 31% and completion of 70%.9 Furthermore, the HPV vaccine is not yet available in routine clinical care in Kenya.Cervical cancer can be prevented through HPV vaccination among pre-pubescent girls. The Kenya cervical cancer prevention guidelines recommend the administration of HPV vaccine to pre/early adolescent prior to onset of sexual activity. This should preferably be done through a school-based program targeting girls in primary school from classes 4-8, corresponding to an age range of 9 to 13 years. The rationale for this age group is that they are HPV-naïve and will have the highest antibody response to the vaccine.11 The Kenya Voluntary Male Medical Circumcision (VMMC) strategy (2014/5-2018), aims to reduce new HIV infections by increasing the proportion of men aged 15-49 that are circumcised to 80%. For the greatest effect in preventing HIV acquisition, MC should be performed prior to the onset of sexual activity.12 Published literature documents a VMMC up-take of 71% through school-based programs.13 In Kenya sexual activity has been documented to begin early, prior to 15 years of age.14 As such, school based interventions have been widely implemented to reach large numbers of adolescents with HIV and SRH messages.15Male Circumcision (MC) has also been shown to have a protective effect against Sexually Transmitted Infections (STIs) like HPV and HIV. Male circumcision (MC) reduces the risk of STI and HIV acquisition in men by 35% and 60% respectively.18 FMP retention rates of 90% have been documented.17 During implementation, Kenyan parents reported having felt ill-equipped to discuss sexual matters with their children.16Families Matter! Program (hereafter referred to as FMP) is an evidence-based HIV prevention intervention for parents and guardians of adolescents aged 9-12 years. The exclamation is placed to emphasise the importance of the families in helping adolescents avoid risky behaviour such as early sexual activity and drug abuse. FMP aims to enhance protective parenting practices that are associated with reduced sexual risk-taking among adolescents and promote parent-child communication about sexuality and risk reduction. FMP provides education on HIV prevention, STI risk reduction and contraception. Families Matter! Program was modified to the Kenyan context from the US-based Parents Matter! Program in 2003-2004. Following its implementation, the programme was evaluated and shown to be acceptable and also retained its effectiveness in promoting parental skills, parent-child communication and adolescent sexual risk reduction.the social learning theory, to encourage parents to provide supportive environments, reinforce competencies, supervise their children and reduce contact with poor role models. Secondly the problem behaviour theory to bolster children's competencies, promote positive behaviour and reduce opportunities to engage in risk behaviour. Thirdly the theory of reasoned action equips parents with skills to communicate their own attitudes and norms to their adolescents to modify the adolescent's behaviour. Finally, the social cognitive theory enhances parents' efficacy in their ability to communicate with their children about sexual topics. 19FMP's theoretical framework is guided by four theories. Firstly 17 We propose to provide an adapted FMP program that incorporates VMMC promotion as a component of HIV prevention and HPV vaccination as a component of STI risk reduction among parents/guardians at one public primary school within Kisumu County to enhance HPV prevention. In this paper we describe our methods that we propose to undertake and specific aims we intend to achieve in order to assess the feasibility of implementing an adapted FMP in the Kenyan setting.Nationally interventions to provide HPV vaccination, VMMC and family education are currently provided in isolation.20 Kisumu County was chosen based on its high HIV prevalence (16% vs. 5% country wide),21 the highest proportion of young persons whose age of sexual debut was before 15 years (21% vs. 12%) and the highest prevalence of STIs (3% vs. 2%) among sexually active persons in the reproductive age group.21 Day primary schools provide a suitable target population of children in classes' 4-8 from which to draw children who are in daily contact with their parents to enhance parent-child communication.18We will conduct a cohort study among parent-child dyads within one public primary day school in Kisumu County.18 HPV vaccination 9-13 years, 8 VMMC 10-14 years,15 all of which are best delivered before the onset of sexual activities.14 The parents must give informed consent, commit to attending all six FMP sessions, bring their child to the 5th session and participate in ‘home assignments’.18 Only one eligible child per household will be included in the study. Pre/early adolescent boys that have already undergone VMMC (and subsequently their parents)will be ineligible for inclusion. Similarly, pre/early-adolescent girls that have already received HPV vaccination will also be excluded . Where either parental consent or assent from the pre/early-adolescent cannot be obtained, the parent-child dyad will be ineligible for inclusion.The study population will be comprised of parents of at least one pre/early-adolescent male or female child (aged 9-12 years) in day schools. The age range chosen is ideal for each of the three programs i.e. FMP 9-12 years,To design the evaluation study, we developed a series of specific aims and corresponding hypotheses:To determine the uptake and completion rate of HPV vaccine and associated parent and child characteristics among pre/early-adolescent girls who participated in an adapted school-based FMPWe hypothesize that promotion of HPV vaccine uptake through FMP will double vaccine uptake from 31% to 62% while maintaining the same completion rates of 71%To determine the uptake of VMMC and associated parent and child characteristics among pre-adolescent boys who participated in a school-based FMPWe hypothesize that FMP will attain a VMMC uptake of 80% from the inception of the program to the 6 months booster sessionTo document lessons learnt during the promotion of HPV vaccination for pre/early-adolescent girls and VMMC for pre/early-adolescent boys through a school-based FMP.We hypothesize that the program will provide valuable lessons as to the feasibility and outcome of promotion of HPV vaccine uptake and VMMC through a school-based FMP program9 and VMMC uptake of 71% through school based programs 13 as well as and FMP retention rates of 90% 17 documented in the literature. Using HPV vaccine uptake (in order to obtain the largest sample size22), and sample size formula for calculation of difference in proportions, to double HPV vaccine uptake (from 31% to 62%) with a significance level of 5% and power of 90% would require a sample size of 50.23We based our subsequent sample size computation on HPV vaccine uptake of 31% and completion of 70% 20 Given the FMP recommends groups of 12-18 participants, we will recruit four groups of approximately 15 parent-child dyads. To improve retention rates, we will provide transport reimbursement and refreshments during sessions. Although absenteeism will be discouraged, makeup sessions will be given for parents who miss a session and are willing to continue with other sessions. We will attempt to make-up for missed sessions by having parents attend make-up sessions in alternate groups as this can affect group dynamics.18To adjust for an attrition rate of 10%, a total of 55 dyads will be recruited to participate.18 will be adapted to include information on VMMC15 and HPV vaccination8 that is currently found in different HIV program guidelines by a team of health program managers and certified FMP providers.The current FMP curriculum15 The County Health Promotion Officers  who will be involved in the study will notify the County Education Officers who will in turn get in touch with the School heads of the potentially participating schools. Once a school is finally chosen, the study team will make an inception visit to plan study implementation with the school head. We will also involve the head of the participating health facility to ensure that the facility is equipped to implement study proceduresThe County Health Management Team (CHMT) will be contacted to obtain approval to conduct the study. We will adapt the methods used by a school-based VMMC program in the former Nyanza province when conducting school-based VMMC projects.During a Parents-Teachers meeting, a study team member will make a short presentation about the study. Interested parents/guardians will be asked to provide their names and contact details. Interested parents will be contacted at a later date and informed of the study procedures. Those that are willing to participate will be invited to the study's first formal meeting at the school grounds  where study procedures will be presented in detail and written consent obtained from those willing to consent to participate in study procedures.18This will be delivered by a certified FMP trainer through six 2-hours' sessions; five sessions will be administered over a 5-week period and the 6th will be conducted six months after the 5th session. The first 5 sessions will involve (i) getting to know you and understand your child, (ii) effective parenting, (iii) parents' role in sexuality education; the information in this session will also include HPV vaccination and MC for HPV prevention, (iv) how to increase your comfort and skills in discussing sexuality issues and (v) discussing sexuality and handling peer pressure. In the 6th session, (vi) a booster session will be done to review the FMP. This will be done with parents who participated in the school health program whether or not they accepted to vaccinate their daughters or allow their sons to undergo VMMC. Minutes of each of the meetings will be documented for analysis.Vaccine administration and VMMC services will be availed for free at a selected public health facility for a 7 months' period from the beginning of FMP implementation. The pre/early -adolescents that undergo any procedures at the selected facility will be linked to the study by the use of study-specific referral forms and unique study-specific identifiers.Parental-child dyads will be assigned dyad-specific interlinked participant identifiers. E.g. if a parent is FMP-1-0; his or her child will be identified as FMP-1-1. Our study will therefore be able to link parent and child information to uptake. The same identifiers will be noted on the vouchers which participants will be requested to present to a selected health facility should they take up the VMMC/HPV vaccination interventions. Data will be collected from parents and their pre/early adolescent children separately using, paper or electronic data collection tools.24Data from parents/guardians will include demographic information i.e. age, sex, relationship to the child, education level, marital status, occupation, number and ages of other children, communication with their children about sexual matters, knowledge about HIV and cervical cancer, participation in cervical cancer screening programs for females and VMMC programs from males, whether they are from a traditionally-circumcising or non-circumcising community, etc. Data from the adolescents will include demographic information, e.g. age, sex, education level, knowledge on HIV prevention and source of that knowledge, communication with their parent about sexual matters, etc. Data will be collected at baseline (prior to the 1st session) and after completion of the 5th session. During each session, anonymised deliberations will be documented and analysed to document experiences of the participants. Data will be entered into an MS Access Database and uploaded into SAS 9.2 for analysis.20 All assumptions regarding type, independence, normality and variance of the data will be tested.25 Thematic content analysis will be used to identify emerging themes and document barriers and facilitators to promoting HPV vaccination and VMMC through FMP from project documents, deliberations of the sessions and study team members' experiences.26Measures of central tendency and proportions will be used to summarise characteristics of parent child-dyads. One parent-child dyad will be considered as one unit. HPV vaccine up-take will be described as the proportion of eligible adolescent girls who received the HPV vaccination during the study period. Among those who received the initial dose of HPV vaccine, HPV completion will be described as having received all the 3 doses of HPV. Logistic regression will be used to describe parental and child characteristics associated with HPV vaccine uptake and completion. VMMC uptake will be described as the proportion of eligible pre/early-adolescent boys who underwent VMMC during the study period. Logistic regression will be used to describe parental and child characteristics associated with VMMC uptake.18 HPV vaccination is also given over a 6 months' period.8 Moreover, HPV and VMMC services will be available for free at selected public health facilities for a 7 months period from the beginning of the FMP sessions. The study timeline is shown in This will take into consideration the length of the FMP program, i.e., 5 weeks for implementing the intervention and a booster session 6 months later.Written informed consent will be obtained from parents/guardians of the participants and assent from the minors. The informed consent process will be administered in the language the participants and guardians feels most comfortable, in English, Kiswahili or Dholuo. They will also be informed of the referral procedures as described in the intervention. A detailed description of all procedures including period of study will be provided.No personal identifiers will be collected and all collected information shall remain private. All data collected will be kept secured, in locked storage spaces or in password-protected files and computers for the digital files. Furthermore, all staff will be trained in ethical procedures and measures to protect confidentiality; they will also be required to sign confidentiality agreements. Ethical approval for the conduct of this study will be sought and obtained from the Jaramogi Oginga Odinga Teaching and Referral Hospital (JOOTRH) Ethics Review Committee that cater for research projects in Kisumu County. Study approval will only commence following liaison and approval with the County government of Kisumu, officials from the ministry of education and the heads of the participating school and health facility.Study findings will be disseminated internally to participants in a debrief session, staff of the participating schools, county government officials and externally in national stakeholder forums, international conferences and in peer-reviewed publications.27It is anticipated that the intervention will enhance parental-child communication about sexual matters, promote safe sexual practices and overall reproductive health among the pre/early-adolescents and the uptake of biomedical HIV and HPV prevention interventions.