Escherichia coli (STEC) serotype O55:H7 occurred in England between 2014 and 2018. We reviewed the epidemiological evidence to identify potential source(s) and transmission routes of the pathogen, and to assess the on-going risk to public health. Over the 5-year period, there were 43 confirmed and three probable cases of STEC O55:H7. The median age of cases was 4 years old (range 6 months to 69 years old) and over half of all cases were female . There were 36/46 (78.3%) symptomatic cases, and over half of all cases developed HUS , including two fatal cases. No common food or environmental exposures were identified, although the majority of cases lived in rural or semi-rural environments and reported contact with both wild and domestic animals. This investigation informed policy on the clinical and public health management of HUS caused by STEC other than serotype O157:H7 (non-O157 STEC) in England, including comprehensive testing of all household contacts and household pets and more widespread use of polymerase chain reaction assays for the rapid diagnosis of STEC-HUS.Recurrent outbreaks of haemolytic uraemic syndrome (HUS) caused by Shiga toxin-producing  Escherichia coli (STEC) are zoonotic, human gastrointestinal pathogens that cause symptoms ranging from mild, self-limiting diarrhoea to bloody diarrhoea [Shiga toxin-producing iarrhoea . Howeveriarrhoea . STEC-reiarrhoea , 4. The iarrhoea . Transmiiarrhoea . In housiarrhoea .(stx1 or stx2 genes) which can be subdivided into subtypes (stx1a-1d and stx2a-2g). There are certain subtypes, specifically, Stx2a and Stx2d, which, in conjunction with the presence of the E. coli attachment and effacing (eae) gene, are associated with an increased risk of STEC-HUS [Whilst there are over 100 reported serogroups of STEC that can cause gastrointestinal disease in humans, in England the most commonly identified serotype is STEC O157:H7 [STEC-HUS . Whilst STEC-HUS –12.Understanding the underlying risk factors is the key to control the spread of STEC infection, and risk factors vary between outbreaks, and between different serotypes. While STEC O55:H7 infections are rare, series of recurrent outbreaks occurred in England between 2014 and 2018. A number of questions remain unanswered regarding the vector, source and routes of transmission of STEC O55:H7. In this paper, we review the epidemiological evidence collected over the 5-year period 2014–2018, to elucidate the plausible source(s) and routes of transmission of the causative agent, and to assess the on-going risk to public health.E. coli O55 from serology, with a sample date in 2014–2018.A confirmed case of STEC O55:H7 was defined as an individual with (i) a microbiologically confirmed isolate of STEC O55:H7 identified by culture from a faecal sample, and/or (ii) antibodies to the lipopolysaccharide (LPS) of stx genes and an epidemiological link to a confirmed STEC O55:H7 case.A probable case was defined as an individual with symptoms including diarrhoea, bloody diarrhoea or HUS, who had a known epidemiological link to a confirmed STEC O55:H7 case or an individual who had a positive polymerase chain reaction (PCR) test for Cases were classed as either symptomatic  or asymptomatic. An outbreak of STEC O55:H7 was defined as a cluster of cases with isolates belonging to the same five single nucleotide polymorphism (5-SNP) single linkage cluster and epidemiologically linked on the basis of person, place and time.Questionnaires were administered to all suspected STEC cases by Public Health England (PHE), in line with standard operating procedures using enhanced surveillance questionnaires (ESQ) . We collAdditional questionnaires were developed to identify shared environmental exposures such as local sites, premises, environments or attractions visited by the cases. We developed these location specific questionnaires using information from cases' initial ESQs. Information provided by cases was added to an outbreak specific questionnaire for use prospectively. Cases who had already been identified and interviewed were re-interviewed and asked about any new areas identified by more recent cases.stx2a/eae outbreak cases. STEC O157:H7 stx2a/eae cases notified to PHE in the same period as the O55:H7 stx2a/eae cases with an ESQ available on NESSS, were included. We performed univariate logistic regression and calculated odds ratios (OR) and 95% confidence intervals (CI) using STATA 15.A univariate case–case study was undertaken to compare clinical outcomes and symptoms of STEC O55:H7 cases to STEC O157:H7 cases with the same virulence profile as the STEC O55:H7 stx by PCR at local hospital laboratories, to the Gastrointestinal Bacteria Reference Unit (GBRU) at PHE . At GBRU, faecal specimens are tested by PCR and cultured on MacConkey, Sorbitol MacConkey (SMAC) and cefixime-tellurite SMAC (CT-SMAC) agar [stx were identified and sequenced. Serum samples were taken from patients with HUS when STEC was not detected in their faecal specimen by culture and were assessed for the presence of antibodies to the LPS of E. coli O26, O55, O103, O111, O145 and O157 [PHE STEC operational guidelines recommend referral of faecal specimens or rectal swabs from patients diagnosed with HUS, and/or bloody diarrhoea where STEC infection is suspected, and faecal specimens that test positive for AC) agar . For allAll isolates of STEC O55:H7 from the human cases and two animal faecal specimens together with nine isolates of STEC O55:H7 from the PHE archive isolated between 2012 and 2014 from patients resident in the Republic of Ireland, were whole genome sequenced on the Illumina HiSeq platform, as previously described .FASTQ reads from all sequences in this study can be found at the PHE Pathogens BioProject at the National Center for Biotechnology Information (accession number: PRJNA315192).Environmental Health Officers (EHOs) from local authorities where cases were resident, inspected venues and sites that cases reported having visited in the 14 days prior to their onset and took appropriate samples. In the initial outbreak, this included food premises, however in subsequent outbreak investigations, the focus of sampling was environmental, following the hypothesis generated during the first outbreak that an environmental source was probable.Campylobacter present in the environment http://enigmaproject.org.uk/the-project/) [During the initial outbreak investigation in Dorset, extensive food, water and environmental sampling was undertaken by EHOs, using standard samples and swabs, faecal pots and boot socks  . HydroloA positive cat faecal sample was identified in July 2014, and subsequently faecal specimens were collected from pet dogs and cats belonging to each case, where possible. Seventeen faecal specimens were taken from animals at a petting farm, and veterinary inspection and risk assessment of the premise. The Food Standards Agency carried out supply chain investigations linked to food consumed by cases, and their pets, focusing on identifying any retail distribution chains local to the Dorset area which differed to the rest of the country.Following the hypothesis generated in the initial outbreak that the likely source of the outbreak was zoonotic, the sampling strategy during the Surrey outbreak focussed on environmental exposure and domestic and wild animal contacts. There were 30 samples taken in total, from livery stables, local recreation grounds and gardens of cases – all of which were identified as sites for environmental sampling through the process of iterative interviewing of cases.Environmental samples were taken from play areas, multiple rural areas and woodlands which had all been visited by the cases in the 14 days prior to onset of illness. Boot sock sampling was undertaken in the areas most frequented by the cases and their contacts, including gardens, play parks and footpaths and surface swab samples were taken from garden bird feeders and poultry feeders. Outdoor toys, shoes and the wheels of a buggy were also swabbed with surface swabs. Food, drinking water, animal feed, soil and vegetation samples were also taken in addition to animal faecal samples which were collected from both wild and domestic animals from the area.https://www.iso.org/standard/53328.html). Water samples (up to 1 l) were filtered and filtrates enriched in 100 ml enrichment broth. Boot socks were immersed in 250 ml enrichment broth and swabs were immersed in 90 ml enrichment broth. The enrichment broths were then tested by PCR targeting stx described above (https://www.iso.org/standard/53328.html).Animal faecal specimens were tested at GBRU using the same protocol as for human faecal specimens. All other samples were analysed by PHE's Food, Water and Environmental (FWE) Microbiology laboratories. At FWE, real-time PCR was used to examine samples for the presence of STEC based on CEN/ISO TS 13136  was 4 years old (range: 8 months to 69 years old) and in Surrey (n = 12), the median age was slightly older at 7 years old (range: 1–34 years old). The median age of the ‘sporadic’ cases (n = 3) was lower than the Dorset median, at 2 years old (range 6 months to 3 years old).In the Dorset outbreak, the median age of cases  were asymptomatic. This included household screening of families for two children with HUS, although STEC could not be isolated from their faecal specimens. The detection of STEC O55:H7 in the faecal specimens of household contacts provided the evidence for linking the two HUS cases to the STEC O55:H7 outbreaks.Of the confirmed STEC O55:H7 cases , 78.3% (36/46) were symptomatic . The probable cases were symptomatic. Symptoms experienced by both confirmed and probable cases were severe – over half were hospitalised (56.5%) and developed HUS (54%), and half reported having bloody diarrhoea (50%). Two children aged 6 months and 2 years old, died as a result of the infection.All symptomatic cases experienced diarrhoea, and of those who reported having diarrhoea, over two-thirds (64%) reported having blood in their diarrhoea.stx2a/eae cases were no more likely to be female than O157:H7 stx2a/eae cases , nor were they more likely to have bloody diarrhoea . However, STEC O55:H7 stx2a/eae cases were found to have over double the odds of being hospitalised compared to STEC O157:H7 stx2a/eae cases  and had over 30 times the odds of developing HUS , P < 0.05) and over 10 times the odds of dying  compared to STEC O157:H7 stx2a/eae cases.In a univariate case–case analysis, STEC O55:H7 Exposure questionnaires were available for 43 of the 46 confirmed or probable cases identified between 2014 and 2018. Questionnaires were unavailable for two confirmed cases notified in 2014 and for one probable case in 2017. Routine and enhanced questionnaires identified no common exposures between all cases. No common food and drink company, premises or supply chain was identified between cases nor was a common area/site or venue visited. The most frequently identified exposures were to rural/semi-rural environments and contact with animals (both domestic and wild). Several cases reported walking in local recreation grounds, woods, paddocks and in gardens .Table 4n = 17) and cats , horses (n = 2), goats (n = 2), poultry (n = 2) and sheep (n = 1). No cases reported contact with cattle or calves.For the 43 cases for whom extended questionnaires were available, over half reported having contact with any animal , including dogs , paddocks (n = 5), woods/fields (n = 5) and beaches (n = 5). Of those who visited a zoo/farm, three reported handling animals, including rabbits, chickens and goats. None of the reported zoos or farm were visited by multiple cases. Two reported consuming food at such sites and all reported hand washing at these sites.All but one case lived in rural or semi-rural environments. Cases reported day trips, including to, farms/zoos  in the East Midlands was a sorbitol fermenting strain, however, all the other isolates were non-sorbitol fermenting (n = 35) most likely due to a non-sense mutation in srlA, previously described by Schutz et al. [aadA-1b encoding resistance to streptomycin and dfrA-1 encoding resistance to trimethoprim.Of the 43 confirmed cases, STEC O55:H7 was detected in 35 cases, all isolates carried ping HUS . All thez et al. . All theWhole genome sequencing results from 35 human STEC O55:H7 cases and two cats were available. Of these, the sequences of STEC isolated from 33 of the cases and the 2 cats, belonged to the same 10-SNP single linkage cluster . This 10The sequences from the two cases from East Midlands in 2018 fell within 5-SNPs of each other but were phylogenetically distinct from the 10 SNP cluster, being more than 100 SNPs different from the sequences from the previous cases.General advice about hand and food hygiene as well as methods for environmental decontamination was provided to all cases and parents of cases (where cases were children). In concordance with the PHE Interim Operations Guidance for STEC, both verbal and written advice was provided to cases and/or their contacts. Following the Dorset outbreak of 2014–2015, all STEC O55:H7 cases were interviewed about exposures in a 14-day period prior to onset of illness rather than the standard 7-day history. Following the identification of a positive stool sample from a cat, more specific hygiene advice around hand washing after interactions with domestic animals and before preparing food was provided.Extensive contract tracing, including microbiological screening of all close contacts and exclusion from the workplace or school, where necessary, was performed. In all outbreaks and incidents of STEC O55:H7, briefings were issued to primary and secondary care services, including microbiology laboratories, to raise awareness in the health community and increase case detection.stx profile have been detected in the UK since 2018. Outbreaks of STEC O55:H7 have not been previously described in the literature in any other country, although there is evidence to suggest that E. coli O55:H7 was the progenitor from which STEC O157 has evolved [In this study, we reviewed and analysed epidemiological and microbiological data linked to cases of STEC O55:H7 detected in the UK between 2014 and 2018 to look for evidence for the source and transmission routes of this emerging pathogen, and to monitor the on-going risk to public health. The number of cases declined between 2016 and 2018, although the case demographics were similar to those cases in the 2014 to 2015 Dorset cluster with respect to age, sex and seasonality . The maistx2a and eae, known to be significantly associated with the potential to cause HUS [P < 0.05) with the same virulence profile. Other potential factors contributing to poor clinical outcome may include the age of the affected population (children under the age of 5 years old) [Despite the decline in case numbers over the years, clinical outcomes of cases reporting symptoms remained at the severe end of the spectrum, including two fatal cases in 2018. The case–case study highlighted the severity associated with STEC O55:H7 infection compared to infection with STEC O157:H7. As previously discussed, this strain of STEC O55:H7 had a highly pathogenic combination of virulence factors, specifically ause HUS . Howeverstx encoding phage (and therefore the ability to cause severe disease in the human population) in a strain of E. coli already endemic in cattle and sheep [Although we were unable to pin-point the source of the aetiological agent, we analysed the genomic data to better understand the transmission dynamics of this pathogen. STEC O55:H7 had not been observed in England prior to 2014 . Previound sheep , 22. Thend sheep , 24. In nd sheep –29. ExhaThe highly pathogenic nature of this strain of STEC O55:H7 justified the in-depth investigations that followed, and we continue to monitor for signs of re-emergence. The investigations have informed policy on the clinical and public health management of HUS caused by non-O157 STEC in England. We found that sampling the environment was resource intense and the boot sock methodology has not been well validated or evaluated for the detection of STEC in the environment during an outbreak. We recommend caution regarding the use of the extensive resources required to carry out environmental sampling in this scenario. In contrast, comprehensive testing of all household contacts, regardless of whether or not they are symptomatic or in a risk group, is strongly recommended . If food


Rivers and lakes are used for multiple purposes such as for drinking water (DW) production, recreation, and as recipients of wastewater from various sources. The deterioration of surface water quality with wastewater is well-known, but less is known about the bacterial community dynamics in the affected surface waters. Understanding the bacterial community characteristics —from the source of contamination, through the watershed to the DW production process—may help safeguard human health and the environment.The spatial and seasonal dynamics of bacterial communities, their predicted functions, and potential health-related bacterial (PHRB) reads within the Kokemäenjoki River watershed in southwest Finland were analyzed with the 16S rRNA-gene amplicon sequencing method. Water samples were collected from various sampling points of the watershed, from its major pollution sources  and different stages of the DW treatment process  by using the river water as raw water with an artificial groundwater recharge (AGR).p <  0.001, ANOSIM). The species richness and evenness indices were highest in surface water  among sample groups and gradually decreased during the DW treatment process . Although the phylum Proteobacteria was omnipresent, its relative abundance was higher in sewage and industrial effluents (66–80%) than in surface water (55%). Phyla Firmicutes and Fusobacteria were only detected in sewage samples. Actinobacteria was more abundant in the surface water (≥13%) than in other groups (≤3%). Acidobacteria was more abundant in the DW treatment process (≥13%) than in others (≤2%). In total, the share of PHRB reads was higher in sewage and surface water than in the DW treatment samples. The seasonal effect in bacterial communities was observed only on surface water samples, with the lowest diversity during summer.The beta-diversity analysis revealed that bacterial communities were highly varied among sample groups (R = 0.92, The low bacterial diversity and absence of PHRB read in the DW samples indicate AGR can produce biologically stable and microbiologically safe drinking water. Furthermore, the significantly different bacterial communities at the pollution sources compared to surface water and DW samples highlight the importance of effective wastewater treatment for protecting the environment and human health.The online version contains supplementary material available at 10.1186/s40793-021-00379-w. Assuring an adequate supply of high-quality raw water for the production of drinking water (DW) is a challenge worldwide. In most cases, surface waters such as rivers and lakes near cities fulfill the demand for raw water . HoweverBacterial communities play a central role in aquatic ecosystems and can be affected by various ecological factors in water such as temperature and light conditions, UV radiation, pH, the concentrations of available oxygen, nitrogen, phosphorous, and metal ions , the preRecently, many bacterial community studies have used high-throughput 16S ribosomal RNA gene sequencing analysis , includiThe aim of the study was to find out if (a) the bacterial community structure and function—specifically diversity, taxonomy, predicted enzymatic function, and PHRB—change significantly from the sources of contamination through the surface water to drinking water production; (b) the AGR process can produce biologically stable and microbiologically safe drinking water. To reach the study aims, water samples were systematically collected in each season  in two consecutive years from a surface water ecosystem consisting of lakes and rivers, its major point pollutant sources all the way to drinking water production with the AGR process, a path rarely followed before. Then, bacterial diversity, taxonomy, predicted enzymatic functions, and changes in the read abundance of potential health-related bacteria (PHRB) over time and sites were evaluated with the 16S rRNA gene sequencing method.The study sites were in the Kokemäenjoki River watershed in the southwestern part of Finland  in the Turku region (southwest part of Finland) [The Kokemäenjoki River flows from Lake Pyhäjärvi near the city of Nokia, towards the southwest coast of Finland. It drains water from diverse sources such as treated municipal and industrial sewage discharges and runoff from urban areas, mines, agricultural fields, and forest areas. The river water is used for bathing and recreational purposes and serves as raw water for DW production (23 million mFinland) , 29. TheA total of seven municipal wastewater treatment plants (WWTPs) discharging treated wastewater effluents into the Kokemäenjoki watershed were included in the study: Rahola and Viinikanlahti in the city of Tampere; Kullaanvuori and Siuro in the city of Nokia; and Mouhijärvi, Vammala, and Äetsä in the city of Sastamala. Viinikanlahti is the largest WWTP of the study, serving about 200,000 inhabitants, and Mouhijärvi is the smallest, serving about 1300 inhabitants . All theA total of 243 water samples were collected from 30 sampling locations in each season for 2 years from the Kokemäenjoki watershed, its point sources of pollution, and the DW production process Table . SurfaceAll surface water samples were collected with a grab sampler from one meter below the surface except at Site 2 (located on the deepest region of the lake), where the samples were collected from one meter, 10 m, and 40 m below the surface to assess if sampling depth had an effect on lake bacterial communities. Also, groundwater samples of infiltrated water from observation and production wells and wastewater samples from WWTPs were collected as grab samples. The DW production well samples were collected before adding any disinfection, as the current study followed the path from surface water to the DW production well. Water samples were transported in coolers to the laboratory of the Finnish Institute for Health and Welfare  and processed within 24 h.Collected samples were stored by filtering the samples of surface water (75 ml – 400 ml), sewage effluent (50 ml – 100 ml), and groundwater (500 ml – 1000 ml) onto nylon membranes with a pore size of 0.2 μm . Immediately after filtration, the membranes were treated with RNAlater  and kept at 4 °C overnight before freezing at − 75 °C .DNA was extracted from stored filters, which were first transferred to microcentrifuge tubes with acid-washed DNase and RNase free glass beads . Storage tubes containing RNAlater were centrifuged for 3 min at maximum speed, and the pellet was resuspended with 500 μl lysis buffer  containing β-mercaptoethanol  and added to the microcentrifuge tube containing the filter. The tubes were then bead-beated for 40 s at maximum speed  and centrifuged 3 min at maximum speed. The DNA fraction was extracted using AllPrep DNA/RNA Mini Kit  following the manufacturer’s protocol.The DNA extracts were shipped on dry ice to the laboratory of the United States Environmental Protection Agency  for community sequencing, as previously described , 27. SpeThe fastq files with forward and reverse reads of bacterial 16S rRNA gene obtained from 300 bp paired-end Illumina MiSeq sequencing were merged with Flash software version 1.1 . Trimminp <  0.002, ANOSIM; Table p <  0.196, ANOSIM; Table The bacterial communities were further analyzed with MicrobiomeAnalyst . A totalHerein, the core microbiome refers to the set of taxa that have higher relative abundance above a given abundance threshold. The core bacterial communities were calculated with MicrobiomeAnalyst in a way that an OTU with a relative read abundance of more than 0.01% of total reads in a sample was defined as the core community of that sample. The core OTUs detected in more than 20% of samples  in a studied group was reported as the core community of that sample group. The core bacterial OTUs were classified into the deepest possible taxonomic level with the Greengenes OTU annotation library.www.genome.jp/kegg). The KO analysis for 230 water samples was performed as follows: (a) a total KO list was obtained from MicrobiomeAnalyst; (b) for each ortholog, the KO with the highest number was identified; and (c) all orthologs were re-arranged to select only the orthologs that had the highest value of 500 or more in Step b for the further identification of KEGG pathways. The total number of each functional category was calculated with a simple sum [The predicted enzymatic function of bacterial communities was calculated with 16S rRNA gene sequence library-based OTUs with PICRUSt  with Micmple sum  and compn = 230 retained after removing 13 low read samples) and in addition for each sample group in different seasons of the year. The detection frequency of tentative PHRB genus Arcobacter spp. [The list of screened potential health-related bacteria (PHRB) is shown in Supplemental Table Ster spp.  was highn = 1) to doubleton reads (n = 2) and assigns more weight to the rare species [Alpha-diversity was calculated by MicrobiomeAnalyst using OTU counts, observed species index, Chao1 index, and abundance-based coverage estimator (ACE) as taxonomic richness indices . The obs species . ShannonAnalysis of similarity (ANOSIM) method from MicrobiomeAnalyst was used for non-metric multi-dimensional scaling (NMDS) of beta-diversity with the Bray-Curtis index distance method. The comparison was made following the criteria reported by Ramette : (a) 0.7p-values < 0.05 were considered statistically significant. The normality of data was tested by the Shapiro-Wilk test. Because many PHRB reads had zero read counts in samples, the bacterial read counts were log-transformed by (Log10 N + 1) before statistical analysis. Since the data were highly skewed even after the logarithmic transformation, the 95% confidence limit of median reads of PHRB was calculated with the following equations [The significance of the difference between the sample groups and seasons was studied with the Kruskal-Wallis test followed by the Dunn-Bonferroni post-hoc test in IBM SPSS Statistics for Windows version 25. The quations  from PHRwhere n is the total number of observations in the studied sample group.p < 0.001, Kruskal-Wallis). No significant changes were observed in the average read counts between the seasons of the year .A total of 6,052,510 high-quality reads were obtained after sequencing, aligning, and chimera removal. The average read count per sample was 26,320 , ranging from 4860 to 65,480 reads. The rarefaction curves of all sample groups are presented in the Supplemental Material , industrial effluents (n = 15), mine runoff (n = 4), and drinking water (n = 37), respectively. The geometric mean (± standard error) of diversity indices in each sample group is shown in Table A total of 3823 OTUs were observed in the samples included in the analysis (n = 230). Specifically, 3388, 2554, 962, 1269, and 1711 OTUs were recorded for surface water (n = 115), municipal sewage  and Chao 1 Index  .Among the surface water sample subgroups , river water had a significantly higher Shannon diversity index —and within industrial effluent—between Industry I and Industry II . Following the water flow in the AGR process, the sample collected from the GW Observation Well I was closer to the pretreated samples, while the sample collected from GW Observation Well II overlapped with the production well samples  and the beta-diversity of bacterial taxa, five major clusters were noted—municipal sewage, industrial effluent, mine runoff, surface water, and the samples from the AGR production process Fig. . FurtherA total of 38 bacterial phyla from the Greengenes database were identified via high-throughput 16S rRNA gene sequence analysis. All these phyla were present in surface water samples, but only 25, 30, 32, 20, and 22 phyla were recorded from industrial effluent (both I and II), mine runoff, sewage (both effluent and influent), pretreated water, and AGR samples, respectively. About 40% of phyla from each sample group had a read contribution of more than 0.1% out of all reads in that sample group. Hereafter, we report bacterial community composition at the phylum, class, and family levels.Proteobacteria was omnipresent, but the relative abundance of this phylum was significantly higher in sewage samples (influent and effluent: 79 and 80%), industrial effluent (I and II: 66 and 71%), mine runoff (78%), GW observation well (63%), and GW production well (73%) compared to surface water (55%) samples . The read proportion of Proteobacteria gradually increased through the DW production process. As demonstrated in Table Proteobacteria phylum was highest in the spring samples (64%) and lowest in the summer samples (45%). The difference was most visible in the relative abundance of the Betaproteobacteria class—the highest share in spring (49%) and the lowest in summer (28%).The bacterial phylum Proteobacteria phylum, there was a large variation in bacterial classes in different sample groups  and effluent  samples than in surface water and DW production process . The abundance of Epsilonproteobacteria was less than 1% in the rest of the sample groups, except for mine runoff (7%). Furthermore, in most sample groups, the abundance of Gammaproteobacteria was less than 2%, except in the effluent from Industry I, mine runoff, and surface water, where their abundance was 12, 8, and 4%, respectively. Betaproteobacteria was detected in all sample groups, with the highest abundance in Industrial Effluent II (59%). The read proportion of class Alphaproteobacteria increased gradually as the drinking water treatment process proceeded  than in the other groups  .Overall, within the ups Fig. . EpsilonCampylobacteraceae  and Moracellaceae  were mostly detected in the sewage samples. Among the Betaproteobacteria, the Comamonadaceae family was detected in all sample groups  was 6% in surface water, 15% in pretreated water, and 3% in GW observation well samples. The relative abundance of members of the Rhodospirillaceae family (Alphaproteobacteria) was high in the production well (26%) and the GW observation well (11%) samples.The taxonomic classification was not possible for all reads at the family level, although the reads were assigned to the phyla and class levels Fig. . The famups Fig. , althougups Fig. . FurtherBacteroidetes was the second-most abundant phylum , while Bacteroidia was common in sewage ; Saprospirae was detected in surface water (6%) and pretreated water (16%). At the family level  was dominant in the industrial effluent (I: 8% and II: 34%).Among the studied samples, lum Fig. . Their rActinobacteria was the third-most dominant phylum . The relative abundance of members of this phylum was higher in surface water (13%) and pretreated water (14%) than in the rest of the samples (≤5%). Of the other phyla, Acidobacteria was significantly more abundant in the GW observation well (16%) and production well (13%) samples than in other samples where the relative abundance of Acidobacteria was ≤2% , Verrucomicrobia (3%), and Cyanobacteria (2%) in surface water and Fusobacteria and Firmicutes, which were solely detected in municipal sewage samples with a relative abundance of ≤4%.lum Fig. , and mosCore bacterial communities are reported here as the number of core OTUs at the class level Fig. , while tBetaproteobacteria was identified as a core member in all sample groups , Comamonadaceae , and Moraxellaceae  were the most prevalent , Pelagibacteraceae (96%), and Comamonadaceae (96%) had the highest detection frequency in surface water samples (Table SBetaproteobacteria (21), Actinobacteria (9), and Saprospirae (6) had the highest number of core OTUs  and Alphaproteobacteria (8) and phyla Acidobacteria (6) and Bacteroidetes (4) had the highest core OTU numbers out of a total of 38 core OTUs , Rhodospirillaceae (71%), and an unidentified family OTU from the Betaproteobacteria class (76%) had the highest detection frequency (Table SBetaproteobacteria (19) and Alphaproteobacteria (10) and phyla Acidobacteria (6) and Bacteroidetes (4) had the highest core OTU numbers out of a total of 40 core OTUs , KO hits for most of the predicted functions were significantly lower in the spring samples than in the other seasons, while the KO hits were not significantly different among these three seasons  had KO hits of 500 or more in at least one sample out of the 230 studied samples. The orthologs were identified and grouped into three subgroups based on different KEGG functional gene ontology affiliations  and further divided into 21 different functional categories in each sample group , a total of 145,116 bacterial reads  were determined to belong to PHRB. Pseudomonas spp., Mycobacterium spp., and Acinetobacter spp. were the most frequently detected PHRB genera and were detected in 210, 200, and 193 samples, respectively. When the read counts were considered, Acinetobacter spp., Bacteroides spp., and Pseudomonas spp. had the three highest read counts at 470370, 37765, and 25,540, respectively. Acinetobacter, Bacteroides, and Pseudomonas also had the top three in OTU numbers with 47, 45, and 57 OTUs, respectively, out of a total of 221 OTUs belonging to PHRB detected in this study. Arcobacter spp. was among the most prevalent groups in sewage influent and sewage effluent samples. Most of the Campylobacteraceae family reads from these groups of samples belonged to Arcobacter spp. . All detected PHRB genera except Legionella spp. and Mycobacterium spp. had higher read counts in the sewage influent samples compared to the other sample groups . These findings are in accordance with earlier studies , 30, 55,Comamonadaceae and Rhodocyclaceae families (Betaproteobacteria class) detected in the industrial effluent samples  were detected in all sample groups but were most abundant in industrial effluents, municipal effluent, and mine runoff , which are photo-heterotrophs, plant-associated, nitrogen-fixing aerobes capable of utilizing varying sources of organic carbon and energy [Weeksellaceae family (Bacteroidetes phylum), known to be aerobic and free-living environmental bacteria [Flavobacteriia class in aquatic systems.Members of the off Fig. . Many ard energy . Membersbacteria , were prbacteria , 42. Wanbacteria  suspecteProteobacteria, Actinobacteria, and Bacteroidetes in surface water  Proteobacteria reads of river water samples than in our study. The relatively lower proportion of Proteobacteria phylum in surface water and pretreated samples than in other sample groups can be due to the presence of environmental bacteria like Actinobacteria. The Actinobacteria phylum was the most abundant among the Gram-positive bacteria in surface water. The identified reads were mostly heterotrophic or symbiotic with plants such as nitrogen-fixing bacteria belonging to the family ACKM1 [Chloroplast sequences are closely related to Cyanobacteria; they can originate from cyanos and are therefore difficult to remove from the data completely. There could be classification problems in the taxonomic databases related to these 16S rRNA sequences. Chloroplast reads have also been reported in earlier 16S rRNA gene amplicon sequencing studies [The detection of the bacterial phyla ter Fig.  was consly ACKM1 . Althoug studies .Oxalobacteraceae and Methylophilaceae (Betaproteobacteria class). These are free-living environmental bacteria groups known for their wide range of phenotypic properties and include aerobic or micro-aerobic, facultative anaerobic, heterotrophic, and mesophilic members [Methylophilaceae can utilize methanol or methylamine as a source of carbon and energy and have been reported in numerous environments including activated sludge [Other major groups found in surface water were the families  members , 14. Thed sludge .Proteobacteria phylum (73%) in the AGR production well samples was higher than the 47% reported in Ma et al. [Proteobacteria reads from surface water, pretreated water, GW observation well, and  the production well  that were not identified at deeper taxonomic levels than class and order  was one of the most abundant families in groundwater. This group is anaerobic chemoheterotrophic under dark conditions and heterotrophic under aerobic conditions [Acidobacteria-5 and Acidobacteria-6 were also found in groundwater samples  and their detection frequencies. Table S4. Correlation between various alpha diversity indices. Table S5. Seasonal variation of sequencing reads and alpha diversity indices in surface water. Table S6. Top 3 highest sample prevalence OTUs in each sample group. Table S7. Seasonal variation of KO hits. Table S8. Seasonal variation of PHRB read counts in surface water. Figure S1. Rarefaction curve of 16S rRNA gene amplicon sequences in sample groups. Figure S2. Variation in read counts, Chao1, Shannon index, and Simpson index in sample groups. Figure S3. Variation in OTUs number, Observed index, and ACE index in sample groups. Figure S4. Seasonal variation of read counts, Chao1, Shannon index, and Simpson index in surface water. Figure S5. Seasonal variation of OTUs number, Observed index, and ACE index in surface water. Figure S6. Seasonal variation of bacterial taxa in surface water. Figure S7. Bacterial diversities in different surface water types.Additional file 2: Supplemental Data Sheet: Core bacterial communities. Supplemental Data Sheet S1. Municipal influent. Supplemental Data Sheet S2. Municipal effluent. Supplemental Data Sheet S3. Industrial effluent I. Supplemental Data Sheet S4. Industrial effluent II. Supplemental Data Sheet S5. Mine runoff. Supplemental Data Sheet S6. Surface water. Supplemental Data Sheet S7. Pretreated water. Supplemental Data Sheet S8. Groundwater (combine groundwater observation and production wells). Supplemental Data Sheet S9. Groundwater observation wells. Supplemental Data Sheet S10. Groundwater production wells.


We found that different capture techniques induce distinct changes in amino acid serum profiles, the urea cycle, and glycolysis, and attribute the differences in metabolic patterns to differences in physical activity and stress caused by the different capture methods. These results suggest that when designing experiments involving the capture of wild animals, it may be prudent to employ a single capture technique to reduce confounding factors. Our results also supports administration of tranquilizers as soon as animals are restrained to mitigate short-term physiological and metabolic responses when using pursuit and physical restraint capture techniques.Environmental metabolomics has the potential to facilitate the establishment of a new suite of tools for assessing the physiological status of important wildlife species. A first step in developing such tools is to evaluate the impacts of various capture techniques on metabolic profiles as capture is necessary to obtain the biological samples required for assays. This study employed  Globally, wild ruminants have experienced significant declines in numbers and distributions due to overharvest, anthropogenic alterations of landscapes, competition with domestic livestock, and exotic diseases introduced via comingling with livestock3. Prior to settlement of the temperate regions of western North America by Euro-Americans, a diverse suite of wild ruminants were found throughout the mountain and prairie environments, but by the early 1900s, populations were severely depleted or eliminated from much of the landscape4. The establishment of protective laws, development of wildlife science, and substantial investment of resources by society over the past century has restored populations of native wild ungulates to landscapes where suitable habitat exists. Substantial resources are invested into intensive management and research of restored wild ungulate populations to understand drivers of population dynamics and mitigate factors that limit demographic vigor. Despite intensive efforts by government agencies, however, restoration success for Rocky Mountain bighorn sheep (Ovis canadensis) has been modest. Historically bighorn sheep were broadly distributed throughout western North America with pre-settlement populations estimated at approximately 2 million animals5. Following a century of concerted restoration efforts, however, current bighorn sheep abundance is estimated at < 10% of historic levels6. Two major factors affecting the health and demographic vigor of bighorn sheep are nutritional limitations due to poor quality habitats, coupled with inter/intra-specific competition for forage7 and respiratory disease caused by bacterial pathogens8.Wild ruminants are important to human societies and have major impacts on the structure and function of ecosystems9. A first step in developing metabolomics-based tools for assessing the health, nutritional, and physiological status of wild ruminants is to evaluate the impacts of various capture techniques on metabolic profiles as capture is a pre-requisite for obtaining the biological samples required for assays. Previous work has used a limited suite of serum biochemical and hematological assays to establish baseline values and to contrast the effects of various capture techniques, primarily to help inform wildlife managers on selection and execution of capture techniques that minimize serious physiological effects such as metabolic acidosis and capture myopathy, that can compromise the welfare of animals post-capture11.Wildlife scientists possess limited tools for assessing the nutritional health and disease status of bighorn sheep and other wild ruminants, inhibiting the understanding of wildlife-habitat relationships and the etiology of diseases. The rapid development of environmental metabolomics, i.e. the global analysis of small molecule metabolites present in organisms, cells, tissues, or biofluids, is expanding our abilities to investigate the interactions of organisms with their environment, and has the potential to facilitate the establishment of a new suite of tools for assessing the physiological status of important wildlife species, helping advance ecological understanding and enhance conservation1H nuclear magnetic resonance (NMR)-based metabolite profiling to identify characteristic molecular makers resulting from the three primary techniques  used to capture wild ruminants for research, conservation, and management. This work characterized the serum profiles of polar metabolites of wild bighorn sheep captured with different techniques to inform future sampling protocols for metabolomics studies of wild ruminants, and to provide insights into the physiological impacts of capture. We have found that in addition to stress indicators, different capture techniques induce very distinct and broad-spectrum serum metabolic changes in these wild animals.The present study has explored the value of Capture and handling of animals reported herein complied with scientific guidelines and permits acquired from the State of Montana, the State of Wyoming, and the National Park Service. All animal capture and handling protocols were approved by Institutional Animal Care and Use Committees at Montana State University , Montana Department of Fish, Wildlife, and Parks (Permit # 2016-005), National Park Service (Permit # NPS 2014.A3), or Wyoming Game and Fish Department (Permit # 854).10. Animal captures occurred from December through March during the winters of 2014–15, 2015–16, and 2016–17, when all animals were on senescent native forages resulting in sub-maintenance diets. The majority of these animals, 385, were captured using net guns fired from a helicopter. This method required close pursuit by the helicopter, normally for 2–5 min, until a small net was deployed from a shoulder mounted gun that entangled the animal. A handler was place on the ground to physically restrain the captured animal via a blindfold and hobbles. Animals captured in remote wilderness were processed and released at the capture site, generally within 15–35 min of capture. In other situations with good ground access, the blindfolded and hobbled animals were placed in transport bags and slung under the helicopter on a long cable to a central processing site where they were processed, with blood samples normally collected between 20 and 60 min after capture. Large nets suspended over baited sites (dropnet) were dropped on 104 animals. Once the net was dropped, a large crew of handlers physically restrained the animals with blindfolds and hobbles, extracted each animal from the net, and carried it to a central processing area within 100 m of the dropnet site. Because the large nets captured 10–30 animals each, captured animals were queued for processing with blood samples generally drawn from 20 to 90 min following deployment of the nets. Ground-based delivery of immobilizing drugs, i.e. a cocktail of butorphanol, azaperone, and medetomidine; via dart rifles was used to capture 73 animals12. Animals were approached to within 5–15 m for effective dart delivery. Once stuck by the dart, the animals normally ran 5–20 m, and resumed pre-darting behaviors with their social group until the drugs began to take effect, causing the darted animal to bed down until sedated, normally 20–35 min following drug delivery. Sedated animals were blindfolded and hobbled, sampled, and drug antagonists administered, with processing time normally requiring 10–20 min.A total of 562 serum samples were obtained from wild bighorn sheep in 14 populations distributed across Montana and Wyoming  in 90% H2O/10% D2O, pH 7.0.Serum samples were prepared for small molecule polar metabolite extraction and 1H NMR spectra were recorded at 298 K (25 °C) using Montana State University’s Bruker 600 MHz (1H Larmor frequency) AVANCE III solution NMR spectrometer equipped with a SampleJet automatic sample loading system, a 5 mm triple resonance liquid-helium-cooled  TCI cryoprobe, and the Topspin software (Bruker version 3.2). 1D 1H NMR experiments were performed using the Bruker ‘zgesgp’ pulse sequence with the following experimental parameters: 256 scans; a 1H spectral window of 9600 Hz; 32 K data points and a dwell time interval of 52 μs, amounting to an acquisition time of 1.7 s; and an additional 1 s relaxation recovery delay between acquisitions14.Samples in 2.0 mL centrifuge tubes were spun at 13,000 rpm for 2 min to remove any potential remaining debris, and 500 μL of each sample transferred into 5 mm Bruker NMR tubes. One dimensional (1D) 1H–1H total correlation spectroscopy (TOCSY) spectra were acquired for representative samples using the Bruker-supplied ‘mlevphpr.2/mlevgpph190 pulse sequences and the following experimental parameters: 256 (t1) and 2048 (t2) data points, 2 s (D1) relaxation delay, 32 transients per FID, 1H spectral window of 6602.11 Hz, and a 80 ms TOCSY spin lock mixing period. 2D 1H–1H TOCSY spectra were subsequently processed using the Topspin software (Bruker version 3.2).For the verification of select metabolite identification , 2D 17. Spectral baselines were adjusted using the Chenomx spline automatic adjustment (Whittaker function), followed by inserting manually baseline breakpoints to achieve flat and well-defined spectral baselines. Line broadening, phase correction, and shim correction were employed following Chenomx protocol recommendations and previously reported data processing approaches19. 1H Chemical shifts were referenced to DSS whose NMR signal was set at 0.0 ppm, and the NMR signal from imidazole was used to correct for small chemical shift changes due to slight pH variations. NMR signals were quantified from relative signal intensity with DSS as reference, normalized to sample volumes, and annotated by matching chemical shift and spectral splitting patterns to those of reference spectra accessible through the Chenomx 600 MHz (1H Larmor frequency) spectral database of small molecule metabolites16 , following Chenomx protocols and published NMR metabolomics data analysis approaches20. This step was applied to 0.2% of the samples and no discernable pattern of missing values was observed within any specific capture group. Logarithmic transformation (log base 2) was applied to the data to adjust for potential skewing of data distributions21. Normalization procedures are necessary prior to statistical analysis of metabolomics data to reduce systematic variations, and to correct for changes originating from intrinsic biological sample variations within groups22. To this effect, sample-wise normalization to a constant sum (centering) was applied, as well as auto-scaling (i.e. centering each variable around the mean and dividing by the standard deviation)23. Of the 562 samples originally included in this analysis, a subset of suspected extreme observations was observed (n = 21). The metabolite concentration data corresponding to these samples were examined and found to be missing metabolite information from 18% of the time points collected, which led us to omit these 21 samples from further analysis.Pre-processing parameters for statistical analysis were implemented using Metaboanalyst 4.0, and included replacing missing values for metabolite concentrations that were not observed in some samples but observed in others with 1/5 of the minimum positive value to their corresponding variables20. HCA was conducted using a Euclidean distance measure and Ward clustering algorithm. All 49 metabolites identified and quantified in almost all samples were used to assess whether differences in serum polar metabolite patterns (i.e. differences in metabolite levels) could discriminate and separate the different animal capture groups. To further discriminate between groups, Partial Least Squares Discriminant Analysis (PLS-DA) was performed, as this method is widely used to assess the maximum covariance between a dataset and class labels. Our team used R, along with the caret package, MixOmics and MetaboanalystR packages for PLS-DA modeling, PCA analysis, one-way ANOVA (Tukey’s post-hoc test), and volcano plot analysis26. A potential issue with PLS-DA is that the approach can be susceptible to model overfitting, leading to separations by class that may not be real or are exaggerated27. To assess model validation, the diagnostic ability of the PLS-DA classifier system was assessed using classification error rate (CER) analysis, area under the receiver operating characteristic curve (AUROC), permutation tests (n = 2000), and evaluating Q2 and R2 values associated with the PLS-DA models  and hierarchical clustering analysis (HCA)els Fig. 28. ValidTwo-dimensional principal component analysis (2D PCA) using Metaboanalyst and R programs was employed to evaluate whether polar serum metabolite level differences could separate animals from the different capture groups, and provided encouraging information about separate group clusters, leading to supervised multivariate statistical approaches to further analyze the significance of the metabolite profiling data.2 parameter, classification error rates and permutation tests  scores plots enabled the visualization of metabolic profile differences within groups of animals that were captured by the three different capture techniques Fig. A, as welsts Fig. A–D.2D-PLS-DA scores plots Fig. . This isANOVA analysis assessed differences in fold change (FC) and identified statistically significant metabolites that differentiate samples obtained from the three different capture techniques Fig. . MetabolAdditional PLS-DA analyses were undertaken to assess the extent of serum metabolite changes in pairwise comparisons of capture techniques. The first set included dart versus helicopter, as these two capture techniques yielded the most distinct serum metabolomes Fig. C. This aO-acetylcarnitine in the dart-captured group  to the separation of the three capture groups revealed that amino acid levels of tryptophan, valine, isoleucine, phenylalanine, and proline were highest in animals captured by dart, with intermediate levels in animals capture using dropnets, and lowest in animals captured using the helicopter method Fig. A. One-wa29. While the trend of an overall decrease in urea cycle intermediates parallels a similar trend in amino acid concentrations, the extent to which amino acid metabolism is linked to changes in urea cycle activity is difficult to evaluate due to the nature of nitrogen recycling in the rumen of these ruminants.Of these amino acids, aspartate, glycine, and glutamate function as precursors for neurotransmitter synthesis, and may therefore be valuable indicators of the capture techniques’ impacts on animal health and changes to their physiological state. Glutamate is a fundamental component of nitrogen excretion in the urea cycle, and its lower serum levels in animals captured by helicopter support the idea of altered metabolite flow through the urea cycle. In addition to these patterns, decreasing levels of aspartate were observed in samples of dropnet and helicopter captured animals compared to the levels found in the dart-captured animals. The change regarding urea cycle alterations also manifested itself in differential serum urea levels, with fold changes (FC) between the groups decreasing significantly with capture techniques, with a mean FC difference of 1.4 for the dart-captured group, 0.26 for the dropnet-captured group, and − 0.3 for the helicopter-captured animals , including glycerol, inosine, lactate and pyruvate  were marginal, suggesting that the PLS-DA model may be slightly overfit for this pairwise capture group comparison (Supplementary Fig. The polar metabolite profiles obtained from serum samples of animals captured by dropnet and helicopter were more similar to each other than those of dart-captured animals. Nevertheless, PLS-DA analysis indicated 15 metabolites that contributed to the separation of dropnet versus helicopter capture groups Fig. D, which In conclusion, we have found that different animal capture techniques result in distinct and broad serum metabolic changes in wild bighorn sheep. Serum metabolite profile differences were most significant when the dart-captured animals were compared to the other animal groups captured by dropnet or helicopter methods. Metabolite level changes were less pronounced when the serum metabolite profiles of dropnet-captured animals were compared to those of the helicopter-captured group.The differences in metabolic profiles documented in this study were attributed primarily to differences in physical activity and stress caused by the different capture methods. Both dropnet and helicopter capture rely on nets with consequential physical struggles as the animals attempt to escape entanglement. Both also involve significant time between capture and animal restraint with blindfolds and hobbles aimed at reducing physical activity, but no doubt causing stress, continued muscle exertion, and elevated heart, respiration, and metabolic rates. While there existed considerably variability in the time animals were manipulated when subjected to each capture technique, in general, animals captured with dropnets and helicopters were physically retrained for long periods of time. In contrast, cautiously approaching animals on the ground and delivering immobilization drugs via a dart rifle appear to result in minimal physical exertion and, while darted animals were also blindfolded and hobbled for all handling, processing and sampling, physical exertion and stress appear to be minimal due to sedation. This study has thus demonstrated that the three animal capture techniques examined here, which are the primary techniques employed to capture most wild ruminants, have wide ranging impacts on the metabolism of bighorn sheep, as reflected in significant and broad ranging changes in serum polar metabolite profiles. Most notable appears to be a significant shift in central carbon energy metabolism due to the nature of the type of capture technique employed.The field of metabolomics has considerable potential to enhance the assessment of the health and physiological state of wild animals, and to guide efforts aimed at improving their conservation and management. Of particular interest for wild ruminants is the development of quantitative analytical tools to accurately characterize their body reserves, nutritional status, and disease state, which are the primary limiting factors influencing wild animal populations.37.Controlled experimental studies with captive animals will provide the most rigorous approach to developing metabolomics-based tools, but ethical constraints limit experimental protocols involving disease processes, and preclude experimental protocols that mimic the type of severe and prolonged nutritional deprivation routinely experienced by wild ruminants inhabiting seasonal environments. Thus, complementary observational studies of wild animals will be needed to realize the full potential of metabolomics for wildlife conservation and management. Our findings suggest that when designing such studies that require the capture of wild animals, it may be prudent to employ a single capture technique, if possible, to reduce confounding factors that may alter serum metabolome profiles. The more dramatic changes that were observed in the polar serum metabolite profiles of animals captured using the dropnet and helicopter techniques suggest that administration of tranquilizers as soon as animals are restrained may be warranted to mitigate short-term physiological impactsSupplementary Information.


The purpose of this study was to explore the situations and factors influencing casual sexual behavior among male college students, in order to provide scientific evidences and measures of the prevention and control for HIV/AIDS.2 test was performed on the composition ratios between different groups. With the occurrence of casual sexual behavior as the dependent variable, logistic regression was used to analyze the factors influencing casual male sexual behavior.Using the stratified cluster sampling method, male college students who self-reported sexual behavior were selected as survey subjects in 13 colleges and universities in 11 cities of Zhejiang Province from October to November 2018. We used a custom online questionnaire to collect information on the demographic characteristics, sexual attitudes, sexual behaviors, and HIV interventions of the respondents. The χAjusted OR = 1.45, 95% CI = 1.14–1.84), knowing that HIV self-test kits were available at school , accepting one-night stands , accepting commercial sex , being a man who has sex with men , being a senior , having knowledge of HIV/AIDS prevention and treatment , and knowing that the CDC provides HIV testing services  were factors influencing male college students’ casual sexual behavior.A total of 2734 male college students were surveyed, aged 20.20±1.41 years, of which 595 had casual sex, accounting for 21.7%. The rate of HIV prevention awareness among the participants was 85.1%. Multivariate analysis showed that receiving a self-assessment of HIV risk conducted by the school (Male college students who have causal sexual behaviors have a high degree of openness in sexual attitudes, insufficient knowledge of AIDS prevention, and knowledge of HIV testing-related information but low testing rates. For male college students’ HIV prevention education intervention, it is necessary to emphasize the establishment of correct sexual attitudes and concepts and promote safe sexual behaviors to prevent the spread of HIV. As a major global infectious disease, AIDS is currently a serious social problem in most countries –3. In reMany studies had reported that nearly half of college students had casual sexual behaviors during the university study –11, casuHowever, there are few studies on the situations and factors influencing casual sexual behavior among male college students, and this study will fill this gap. This research was a cross-sectional survey of casual sexual behaviors among male college students from October to November 2018. It explored the current situation and influencing factors of casual sexual behavior among male college students. The intent is to provide a basis for colleges and universities to develop targeted prevention and control procedures for HIV and other sexually transmitted diseases (STDs).Zhejiang Province is located in the Yangtze River Delta region on the southeast coast of China. It is between 118°01’~123°10’ east longitude and 27°02’~31°11’ north latitude. In 2019, the Zhejiang Provincial Department of Education and the Zhejiang Provincial Health Commission jointly issued a document requiring schools to include AIDS education in their health education plans, organize the implementation of preventive education, and encourage college students to take the initiative to receive voluntary AIDS counseling and testing services. A survey was conducted among students attending 13 colleges in 11 districts and cities in Zhejiang Province from October to November 2018, including 3 colleges/universities in Hangzhou and 1 college/university each in another 10 cities. The types of universities were chosen as university or vocational technical college, and the selection of universities was as recommended by the local CDC. A total of 31,674 students were surveyed, of which 14,320 were male students. The inclusion criterion for the study subjects resulting in the selection of 2734 male students was that they self-reported having sex, accounting for 19.1% of the total number of male students. This study complies with the Declaration of Helsinki, which was reviewed and approved by the Ethics Committee of the Zhejiang Provincial Center for Disease Control and Prevention as well, and for which the batch number is 2018–036. All survey participants provided signed informed consent.The questionnaire was formulated after reading the domestic and foreign literature, discussions with the research team, and conducting preliminary surveys of students. The main content of the questionnaire included general population sociological characteristics, knowledge of HIV and STDs, the occurrence of sexual behaviors, and the acceptance of interventions. There are 7 items about HIV/AIDS prevention and treatment knowledge, where answering 5 or more items correctly was defined as having HIV/AIDS prevention and treatment knowledge. These 7 items were as follows: Whether knowing AIDS is an incurable and serious infectious disease? (Item 1); Whether the main mode of HIV transmission among young Chinese students is male homosexual sex?(Item 2); Whether it can be judged by appearance that a person is infected with HIV?(Item 3); Whether daily life and contact spread HIV?(Item 4); Whether the correct use of condoms reduces the risk of infection and transmission of HIV?(Item 5); Whether there is a need to proactively seek HIV counseling and testing after high-risk sex?(Item 6); Whether knowing that the local government provides free antiretroviral treatment for people living with HIV/AIDS? (Item 7). Casual sexual behaviors means whether the participants ever had temporary sex  in the past year.Using the stratified cluster sampling method, a random number table was used to select 3 departments in each university, and then classes were selected from each department by grade. An electronic questionnaire was generated with the web link or QR code, and was distributed to each participant by WeChat. The investigators were composed of tutors who were responsible for tutoring these classes, and were trained by the research team before the investigation.Using the cross-sectional survey method, the students were organized by the teachers to fill in the online electronic questionnaires. Students who were currently not at school had the questionnaire network link sent to them, and they filled out the survey after being given the instructions. The measurement of condom use effectiveness included three questions as follows: Do you have confidence in discussing condom use with your partner before having sex? When the partner refuses to use a condom or does not carry a condom, is there any confidence not to have sex? Do you have the confidence to prepare condoms in advance before having sex? There were 5 options for answering these three questions: very confident, somewhat confident, confident, unconfident, and very unconfident, assigned scores of 3, 2, 1, 0, and −1, respectively. The measurement scores were divided into three groups, namely, unconfident, confident, and very confident, according to 1 point or less, 2–5 points and 6–9 points, respectively. The measured Cronbach’s alpha coefficient in this study was 0.852.The investigators consisted of professionals from the local Center for Disease Control and Prevention and counselors from the middle classes selected by the universities. After unified training, a unified questionnaire was used to conduct an anonymous survey. The investigator explained the purpose, significance, investigation method, and privacy protection policy of the research before the survey and placed these descriptions in the opening remarks of the questionnaire. The research subjects were told that the purpose of the survey was to formulate strategies for protecting students from HIV and other STDs. The survey analyzed only data from this group, excluding personal data.χ2 test was used to compare the demographic characteristics of the male college students with different sexual behaviors. The measurement data are expressed by the mean ± standard deviation. Whether the participants engaged in casual sexual behavior was the dependent variable. Independent variables included demographic characteristics, sexual attitudes, acceptance of interventions, and self-efficacy of safe sexual behaviors. The single-factor logistic regression method was used to analyze the factors influencing casual sexual behavior among male college students. Variables with P<0.1 in the univariate analysis and demographic characteristics such as age, grade and hometown were included as independent variables in the multivariate logistic regression analysis model. P<0.05 was considered statistically significant.SPSS version 21.0 software  were used for data analysis. Some variables such as age, grade, hometown, household registration, family relationship, and sexual behavior characteristics are expressed by the composition rate. The P>0.05), but significant differences were found between the two groups for grade and hometown (P<0.05). The statistical analysis results are shown in Among the 2734 subjects, 595 engaged in casual sexual behavior, accounting for 21.8%; 2139 engaged in no casual sexual behavior, accounting for 78.2%. Their ages were between 16 and 28 years old, with that of the casual sexual behavior group being 20.10±1.42 and that of the without casual sexual behavior group being 20.23±1.40. There were no significant differences in age, household registration, or family relationship between the two groups (P<0.05). The rate of correctly answering the other 6 questions in the two groups was between 63.19% and 97.34%. The rate of correctly answering "Item 3", "Item 4", "Item 5", and "Item 6" in the noncasual sexual behavior group was higher than that in the casual sexual behavior group, and the difference between the two groups was significant (P<0.05). The statistical analysis results are shown in Among the 2734 male college students, 2328 answered 5 questions correctly, accounting for 85.1%. Among them, the rate of correctly answering "Item 2" was higher in the casual sex group (49.41%) than in the noncasual sex group (43.43%), and there was a significant difference (Crude OR = 1.48), those who had received the self-assessment of AIDS risk conducted by the school (Crude OR = 1.98), those who were aware that the school had HIV self-test kits available (Crude OR = 3.04), those who had accepted a one-night stand (Crude OR = 4.29), those who had accepted commercial sex (Crude OR = 3.82), those who were MSM (Crude OR = 3.30), those who had heard of voluntary counseling and testing for AIDS (Crude OR = 1.49), and those who had accepted HIV voluntary counseling and testing services in the past year (Crude OR = 2.26) were more likely to engage in casual sexual behaviors. The participants who knew that the CDC provides HIV testing services (Crude OR = 0.63) and knew about HIV/AIDS prevention and treatment (Crude OR = 0.63) were more likely not to have casual sex. The statistical analysis results are shown in In the univariate analysis, the participants who had received the school’s HIV test promotion in the last year (P>0.05). The participants who had received a self-assessment of HIV risk conducted by the school , those who were aware HIV self-test kits were available at school , those who had accepted a one-night stand , those who had accepted commercial sex , and those who were MSM  were more inclined to have casual sex. The participants who were seniors , those who had awareness of HIV/AIDS prevention and treatment , and those who knew that the CDC provides HIV testing services  were not inclined to engage in casual sexual behaviors.In the multivariate analysis, there were no significant differences in the two demographic characteristics of age and hometown Click here for additional data file.S2 Questionnaire(DOCX)Click here for additional data file.S1 File(RAR)Click here for additional data file.


Here in the present study, titanium oxide nanoparticles were newly synthesized using propolis extract followed by antimicrobial activity, cytotoxicity assay using human cancer cell lines, and acute toxicity study. The physicochemical characterization of the newly synthesized TiO2 NPs had average size = 57.5 nm, PdI = 0.308, and zeta potential = −32.4 mV. Antimicrobial activity assessment proved the superior activity against Gram-positive compared to Gram-negative bacteria and yeast . The newly synthesized TiO2 NPs showed a potent activity against the following human cancer cell lines: liver (HepG-2) (IC50 8.5 µg/mL), colon (Caco-2), and breast (MDA-MB 231) (IC50 11.0 and 18.7 µg/mL). In vivo acute toxicity study was conducted using low (10 mg/kg) and high (1000 mg/kg) doses of the synthesized TiO2 NPs in albino male rats. Biochemistry and histopathology of the liver, kidney, and brain proved the safety of the synthesized TiO2 NPs at low dose while at high dose, there was TiO2 NPs deposit in different vital organs except the cerebral tissue.Titanium oxide nanoparticles (TiO It was found that the newly synthesized titanium oxide nanoparticles have a cubic to rectangular shape with average size of 57.3 ± 4 nm  while S. epidermidis was the most susceptible microorganism .Data in 2 NPs exhibited potent growth inhibitory effect against the tested human cancer cell lines. At 24 h of incubation with serial dilutions of TiO2 NPs for 72 h, the percentages of growth inhibition for each human cancer cell lines were estimated. It was found that TiO2 NPs inhibited the growth of three cancer cell lines as their concentrations increased. Also, the maximum inhibition of cancer cell proliferation reached at 72 h. Accordingly, cytotoxicity effect of TiO2 NPs on Caco-2, HepG-2, and MDA-MB 231 cells was dose- and time-dependent as it is shown in 2 NPs had low IC50  for inhibiting the proliferation of colon, liver, and breast cancer cells, respectively. The morphological collapse of TiO2 NPs-treated human cancer cells (As it was shown in er cells  supporte2 NPs (Group 2), and 1000 mg/kg TiO2 NPs (Group 3) is presented in 2 NPs toxicity is a dose-dependent effect.The biochemical study of different rat groups receiving normal saline (control group), 10 mg/kg of TiO(1) Kidney. Light microscopic examination of renal sections of all control rats demonstrated well preserved and kept intact normal histological appearance of renal  corpuscles which were formed of glomerular capillaries and Bowman's capsules with subcapsular space. Numerous proximal convoluted tubules had narrow lumina and were lined with simple truncated cubical  cells with basal spherical nuclei. Distal convoluted tubules had wide lumina. They were less numerous and were lined with simple cubical cells with central or apical spherical nuclei (2 NPs-treated rats) were similar to those of the control group, showing most of the renal glomeruli and tubules more or less normal, but few tubules revealed cytoplasmic vacuolations in their lining cells  Liver. H & E-stained sections of liver of the control group revealed normal characteristic of hepatic architecture; the normal hepatic lobules appeared to be made up of intact hepatocytes arranged in cords radiating from the central vein. They were polyhedral in shape with granular acidophilic and slightly vacuolated cytoplasm and rounded vesicular, centrally located nuclei. In between the hepatic cords, the hepatic sinusoids appeared as narrow spaces lined with flattened endothelial cells and scattered irregular Kupffer cells with ovoid nuclei  Brain. Pathological changes in the cerebrum and hippocampus of experimental rats were examined under light microscopy. H & E staining results showed no obvious damage or inflammation in brain cerebral cortex. Cerebral examination of all experimental rats revealed normal neurons structure with central large vesicular nuclei, containing one or more nucleoli, and peripheral distribution of Nissl granules which was illustrated by histological pictures . This treated group showed thickness decrease of pyramidal cell layer, with increased hippocampal neuronal necrosis with dilated blood vessel ; hence in the present manuscript, Egyptian propolis extract was used in the novel synthesis of TiO2 NPs and then the antimicrobial activity and growth inhibitory effect against human cancer cell lines were evaluated and an acute toxicity study was performed to indicate the nanoparticles safety and the potential dose for biomedical applications. It was revealed that the synthesized nanoparticles had a significant antimicrobial activity against various microbes with superior activity against Gram-positive bacteria . TiO2 NPs is gaining enormous attention due to its chemical stability and toxicity and above all the wide spectrum activity (effective against Gram-positive and Gram-negative bacteria) [2 NPs may possibly be explained by the ROS generation which leads to microorganisms' degradation through several oxidation processes [2 NPs was accomplished through a mechanical damage of the bacterial cell by damaging the bacterial outer membrane, while Foster et al. [2 NPs had a wide range of antimicrobial activity including Gram-positive and Gram-negative bacteria, yeast, fungi (filamentous and unicellular), protozoa, algae, and viruses.It is well known that TiOacteria) . The mairocesses . Pigeot-rocesses  revealedr et al.  stated t2 NPs was investigated through different cancer cell lines, namely, colon, liver, and breast cancer cell line. Significantly low IC50 was recorded against liver and colon cancer cell lines . Gandamalla et al. [The cytotoxic effect of the newly synthesized TiOa et al.  tested ta et al.  illustraa et al. .2 NPs in rats was more than 12.000 mg/kg body weight. Hence, in the present study, we explored the toxicity of TiO2 NPs at different concentrations. Rats have been widely utilized in this study of TiO2 NPs toxicity evaluation. This was mainly due to their availability and low cost [2 NPs can enter the body. This can include the intake of foods or drugs containing nanoparticles. TiO2 NPs that enter the body via the gastrointestinal tract are absorbed by the lymphoid tissue of the small and large intestines and subsequently enter the blood circulation, are transferred to the mesentery, and reach the body organs, especially those with abundant reticuloendothelial systems, such as the liver. Finally, TiO2 NPs are excreted by urine [2 NPs can be transported into various organs in the body through the blood and lymphatic circulatory systems. Moreover, TiO2 NPs can cross the blood-brain barrier, causing cavitation in hippocampal inflammation [2 NPs, we studied the biochemical in addition to the histopathological changes in liver, kidney, and brain tissues as scan for other biological effects of high (1000 mg/kg) and low dose (10 mg/kg) of TiO2 NPs.In another avenue, the World Health Organization  stated tlow cost . The gasby urine  so it haammation . In the 2 NPs (20–30 nm diameter) and revealed that the increasing level of AST and ALT indicated the organs injury. The intraperitoneal injection induced increased level of AST and ALT and caused some histopathological changes of liver such as prominent vasodilatation, vacuolization, and congestion due to the titanium nanoparticles accumulation in liver. Liu et al. [2 NPs compared to those of low dose and control. Similar results were reported by Wang et al. [2 NPs with various sizes (25 and 80 nm), and an elevated ALT/AST ratio (regardless of the nanoparticles size) was revealed. In the same study, brain lesions and vacuoles in the hippocampus neurons were detected. On the other hand, in the kidney, proteinic liquids filled the renal tubule and renal glomerulus swellings were observed while in the liver, hydropic degeneration around the central vein plus spotty necrosis of hepatocyte was detected. It was explained by the long-time retention of the nanoparticles due to their small size which resulted in liver and kidney damage. The highly cytoplasmic acidophilia of these cells is demonstrated in this study such as the rupture of lysosomes which leads to amorphous eosinophilic cytoplasm as it is an initial sign of hepatocytes necrosis before shrinking and dissolution of nuclei [2 NPs (1000 mg/kg), an inflammatory mononuclear cellular infiltration around the portal area was demonstrated. These results were in accordance with the results of Giray et al. [2 NPs could interact with proteins and enzymes of the hepatic interstitial tissue interfering with the antioxidant defense mechanism and leading to reactive oxygen species (ROS) generation which in turn may imitate an inflammatory response.Younes et al.  tested tu et al.  observedg et al.  who subjf nuclei . In the y et al.  who sugg2 NPs administration to rats produced histopathological variations and abnormalities in the renal tissue in the form of congested dilated glomerular capillaries, flattening of the epithelial lining of some tubules, and exfoliation and pyknosis of some tubular cells with apparent luminal dilatation and intratubular cell debris. In the present study, occasional moderate glomerular congestion was demonstrated by the renal tissues of rats exposed to high dose TiO2 NPs (2 NPs (group II). It was reported that the renal glomerular basement membrane is fragile and sensitive to toxic effects of NPs [2 NPs exposure. Karyorrhexis and karyolysis are destructive fragmentation and complete dissolution of the chromatin matter of a necrotic or dying cell as confirmed by the previous studies done by Al-Doaiss et al. [2 NPs revealed no pathological changes of neurons in cerebral cortex and cerebellum, but the pyramidal cell soma was enlarged and elongated. Guo et al. [2 NPs doses  and a significant increase in ALT, AST/ALT, and urea nitrogen while using 500 mg/kg treatment which indicate that the liver and kidney injury were dose dependent. In the same year, Chen et al. [2 NPs deposition in liver, kidney, and lung in adult male mice injected with 324, 648, 972, 1296, 1944, or 2592 mg/kg of the tested nanoparticles. In the high dose group (2592 mg/kg body weight) various signs of liver and kidney damage were observed, namely, proteinic liquids in renal tubules, swelling of renal glomerulus, hydropic degeneration, hepatic fibrosis, hepatocellular necrosis, and apoptosis. Liu et al. [2 NPs of 5, 10, 50, 100, and 150 mg/kg body weight and revealed that a serious damage to the kidney, liver, and myocardium along with altered blood sugar and lipid levels was detected. Shakeel et al. [2 NPs in mice was 150 mg/kg body weight.Al-Rasheed et al.  found thTiO2 NPs . This als of NPs  that cans et al. . Howevero et al.  investign et al.  tracked u et al.  intraperl et al.  conclude2 NPs could be a promising candidate as antimicrobial and anticancer agent. However, the results of fluorescence confocal microscopy at two representative concentrations confirmed that NPs were readily internalized and accumulated inside the cells. As expected, the level of internalization varied from one cell type to another, likely due to the intrinsic endocytic properties of each cell type. Independently of the differences in the NPs uptake between various cell types, the influence of surface coating on the internalization can be clearly observed in different organs. Low dose TiO2 NPs (10 mg/kg) were only poorly internalized by almost all of the cells types, whereas high dose TiO2 NPs (1000 mg/kg) highly increased the level of internalization in all examined cells except cerebral tissue. In another avenue the acute and subacute toxicity of various TiO2 NPs concentrations should be evaluated before applying the synthesized nanoparticles in biomedical applications.TiO


Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine  The field of metabolomics aims to study the complex mixture of metabolites in any tissue or organism and is widely used in several research fields for biomarker discovery, in nutritional studies or to personalized medicine-related scientific questions ,2,3,4. TVarious tools have been published, which circumvent these drawbacks and facilitate data analyses, utilizing defined metabolite libraries and fitting peaks, according to their pre-defined multiplicities and characteristics within defined matrices ,15,16. Amean (x)). First, a peak picking approach is performed on the full dataset for every single spectrum by finding all local maxima. This is followed by an optional noise reduction step, where all peaks with a net intensity between the local maximum and the neighboring minimum are discarded. Therefore, an adjustment for the noise level, especially in regions with a baseline above zero, as well as in overlapping peak regions is achieved. In the next step, the non-linear peak fitting algorithm constructs Voigt line-shaped approximated peaks to the experimental data by optimizing amplitudes, peak maxima, the ratio between Gaussian and Lorentzian and peak width. Peak fitting is based on the lsqcurvefit function inbuilt in MATLAB, employing a trust-region-reflective algorithm. In the following step, the AUC of fitted peaks are calculated over a defined integration range . The chemical shift  of these peak integrals vary slightly, even in aligned datasets. Therefore, peak shifts are adjusted to the reference spectrum by an alignment step that iterates through every processed spectrum to find peaks within a user defined peak shift window. The generated dataset of integrated peaks can now be further reduced by applying a frequency filter to exclude peaks that are present in less than a set percentage in the dataset. Finally, the workflow gives as output: a list of peak integrals, a plot of each fitted spectrum and quality metrics, such as the residual sum of squares and the standard error of fit for the fitting parameters. These metrics, as well as the graphical results  and binned data analysis , is shown using a standard addition of three metabolites  with three spiked concentrations in four different urine samples, which results in 12 data points. The data are used to calculate a standard curve for every method. These equations were used to re-calculate the concentration for all 12 individual values. Boxplots see  are emplrors see  is somewThe publicly available dataset MTBLS1  from theIn summary, these results show that using Voigt fitted peak integrals instead of the whole spectrum (as is or binned) allows a crucial reduction of noise, and thus, facilitate the unsupervised data analysis. 2 and Q2 values. Although all three methods yield a valid model to distinguish diabetic and non-diabetic individuals, both R2 and Q2 are higher using the Voigt fitted dataset. Furthermore, the loadings plots of the predictive component  , aims toomponent D,E stillOverall, these results indicate that the noise reduction achieved by applying the introduced peak fitting using a Voigt approximation enables a more convenient analysis of NMR metabolomics datasets. Through the reduction of the dataset, a yet inevitable visual inspection of results becomes more simple and false positive results caused by baseline differences are reduced. Furthermore, the impact of different data analysts is largely reduced. The field of untargeted NMR metabolomics became increasingly important over the past few years. However, effective and reliable data processing remains a bottleneck. The majority of studies published in the field of untargeted metabolomics rely on, either full spectra analysis or on different binning methods. Although NMR is generally highly reproducible, minor changes in baseline intensities may occur due to accumulation of underlying signals, as well as line broadening due to inhomogeneity of the magnetic field. Both conventionally used methods are limited in their ability to compensate for this non-informative variance. Nevertheless, a reduction of this noise is a crucial aspect in uncovering relevant variance and allow identification of biomarkers. The introduced approach aims to improve the efficiency of untargeted NMR metabolomics data analysis by using a peak fitting approach, based on a Voigt line shape model approximation in a least square sense, along with alignment of peak integrals to a reference spectrum. Peak fitting reduces the noise driven bias by reduction of the data. Regions containing mere baseline or very small peaks below a defined S/N ratio are excluded from further analysis, and thus, reduce the influence of the measurement error, which is usually relatively large for small values, and the irrelevant variation within the data. A comparison of all three data processing methods  demonstrates the reduced extend of noise influence of analysis performed using Voigt fitted data compared to conventionally used processing methods for both unsupervised and supervised analysis methods. A significant influence of noise within the first principal components is a well-known feature and generally accepted as fact within the NMR metabolomics community. An orthogonal PLS is typically the method of choice to segregate this noise from the biological variation of interest. Although an orthogonal filter is applied, a significant influence of non-informative variance is demonstrated using conventional data processing. Several research articles have been published, optimizing both integration and data reduction in various biofluids. From these, several approaches need input data, such as a predefined target list or spectral libraries and deliver a targeted metabolomics output, as reviewed by Bingol et al. , while oVoigt-fitting decreases the chance of detecting false positive markers by general data reduction and simplifies the interpretation, and analysis of loadings, respectively weights. Nevertheless, thresholds for S/N and frequency filter must be adjusted carefully to avoid rejection of relevant signals. The Voigt fitting approach was developed and tested on human urine samples as representative biofluid for complex mixtures. However, this method can also be adapted and optimized for other biological matrices. The relevance of improved data processing methods is clearly supported by the comparison of performance of data processing methods in this work. Peak fitting using a Voigt line shape model has been demonstrated to enhance the power of statistical analysis in contrast to conventionally used methods. The used script is written in MATLAB R2020a and can be obtained for implementation by contacting the corresponding author. For illustration of performance improvement the fitting approach in comparison with full spectra and binning approach the MTBLS1 dataset (raw spectra) from the MetaboLight repository . The MTBl-alanine, Caffeine and Nicotinamide in three concentrations by comparing the results to peak height in full spectra analysis and AUC in binned spectra analysis. l-alanine was used because its resonance appears in a non-crowded region and shows a distinct doublet as easy-to-integrate standard. Caffeine has resonances in a crowded region where baseline effects do occur (3–4 ppm) and Nicotinamide causes resonances in the downfield area to comprehensively cover the whole spectrum. A stock solution of 1 mg mL−1 H2O was prepared. A total of 135 µL urine was combined with either 5, 10 or 15 µL stock solution resulting in an addition of 5, 10 and 15 µg standard. The samples were then filled up to a total volume of 150 µL, 50 µL 1.5 M K2PO4-buffer (pH 7.4) containing 0.1% Trimethylsilylpropionic acid (TSP) in 100% D2O was added, samples were thoroughly vortexed and centrifuged at 4 °C for 10 min at 12,700× g. A volume of 180 µL of supernatant was transferred into 3-mm NMR tubes. Samples were measured immediately after preparation. The error estimation of the three tested methods was calculated using four different urine samples each spiked with The samples were analyzed on a Bruker 800 MHz spectrometer operating at 800.35 MHz equipped with a quadrupole inverse cryogenic (QCI) probe probe . A total of 256 scans were recorded into 64 K datapoints with a spectral with of 16 ppm and a 90° pulse of 13 µs. All spectra were acquired at 300 K using a standard 1D-pulse sequence with water suppression (noesygppr1d) during an recycle delay of 4 s, an acquisition time of 3 s, and a mixing time (tm) of 200 ms. Spectra were manually phased and baseline corrected in TopSpin 3.6.1 . −4 ppm, resulting in 44,001 data points per spectrum (−1 to 10 ppm). The water region was removed (δ 4.70−4.85 ppm). Spectra were aligned using a recursive segment-wise peak alignment (RSPA) algorithm [Spectra were imported into Matlab software  for data processing with a resolution of 2.5 × 10lgorithm , probabilgorithm . To complgorithm . For fulPrincipal component analysis (PCA) was performed in Matlab software  using unit variance (UV) scaling prior to analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis was performed according to the method described in Cloarec et al. (2005) .