High-risk human papillomavirus HPV (HR-HPV) modifies cervical cancer risk in people living with HIV, yet African populations are under-represented. We aimed to compare the frequency, multiplicity and consanguinity of HR-HPVs in HIV-negative and HIV-positive Zimbabwean women.This was a cross-sectional study consisting of women with histologically confirmed cervical cancer attending Parirenyatwa Group of Hospitals in Harare, Zimbabwe. Information on HIV status was also collected for comparative analysis. Genomic DNA was extracted from 258 formalin fixed paraffin embedded tumour tissue samples, and analysed for 14 HR-HPV genotypes. Data was analysed using Graphpad Prism and STATA.Forty-five percent of the cohort was HIV-positive, with a median age of 51 (IQR = 42–62) years. HR-HPV positivity was detected in 96% of biospecimens analysed. HPV16 (48%), was the most prevalent genotype, followed by HPV35 (26%), HPV18 (25%), HPV58 (11%) and HPV33 (10%), irrespective of HIV status. One third of the cohort harboured a single HPV infection, and HPV16 (41%), HPV18 (21%) and HPV35 (21%) were the most prevalent. HIV status did not influence the prevalence and rate of multiple HPV infections (p>0.05). We reported significant (p<0.05) consanguinity of HPV16/18 , HPV16/33 , HPV16/35 , HPV35/51 ; HPV39/51 , HPV31/52 , HPV39/56 (OR = 11 95% CI = 8–12), HPV59/68 , HPV66/68 , independent of age and HIV status.We found that HIV does not influence the frequency, multiplicity and consanguinity of HR-HPV in cervical cancer. For the first time, we report high prevalence of HPV35 among women with confirmed cervical cancer in Zimbabwe, providing additional evidence of HPV diversity in sub-Saharan Africa. The data obtained here probes the need for larger prospective studies to further elucidate HPV diversity and possibility of selective pressure on genotypes. Human papillomavirus (HPV) is a ubiquitous virus, which, under normal physiological conditions can be cleared within 24 months ,2. In thEvidences from diverse global HPV ethnogeography data patterns, for example, shows that HPV51 is predominantly observed in Caucasians, while HPV58 is skewed towards East Asia and Latin America –17. An aDespite the strides made to understand the relationship between HPV, HIV and cervical cancer, there is still a gap in SSA. Particularly, in Zimbabwe, most studies have examined HPV in normal cervical cytology and precancerous lesions, leaving only a few studies that focus on cervical tumour tissue ,17,40–43This was a cross-sectional study consisting of 258 women with histologically confirmed invasive cervical cancer diagnosis. This study recruited participants between July 2016 and January 2019 from an outpatient oncology facility, Parirenyatwa Group of Hospitals Radiotherapy and Chemotherapy Centre (RTC), in Harare, Zimbabwe. The biospecimens for analysis were collected at the pathology laboratory of diagnosis, namely Lancet Pathology and Parirenyatwa Group of Hospitals Pathology Department. Collected samples were processed as Formalin fixed paraffin embedded (FFPE) blocks.All human health research was in accordance with the Helsinki declaration. Ethical approval was obtained from University of Zimbabwe College of Health Sciences and Parirenyatwa Group of Hospitals Joint Research Ethics Committee (412/16), Medical Research Council of Zimbabwe (A/2153), University of Cape Town Research Ethics Committee and Research Council of Zimbabwe (No: 03351).The participants from the current study were nested in a pharmacogenomics of cervical cancer study, aiming to recruit women who were newly diagnosed with cervical cancer, and were eligible to receive radical therapy. Potential study participants were identified through the RTC hospital registry. To be considered eligible for this study, women had to be ≥18 years, with histologically confirmed diagnoses of invasive cervical cancer staged between 1b – 4A, and were earmarked for curative anti-cancer therapy. Individuals who were diagnosed with resectable (stage 1A) disease that did not require chemo/radio- therapies or advanced disease that required palliative care (stage 4B) were excluded from the study, because these individuals were referred for care outside of the RTC. Eligible participants were informed of the study verbally, and the women willing to enroll provided written consent. To maintain participant confidentiality, all participant data was de-identified and assigned study numbers. The clinical and demographic information for all recruited participants were abstracted onto a case report form. Demographic information such as age, residency, histopathological tumour characteristics, were collected from the patient folder in the RTC. In addition, behavioral, lifestyle factors and sexual history were collected by interviewing the study participants, namely history of alcohol, smoking, parity, age at sexual debut, number of sexual partners, history of circumcised partner, sexually transmitted infections were also collected. Data on HIV status was also collected for comparative analysis.™ Spectrophotometer 2000/2000c  and DNA integrity was evaluated by running a 1% agarose gel electrophoresis. The extracted DNA was stored at -80°C for further analysis.A total of 5mg of the FFPE blocks were sectioned off in preparation for genetic analysis. Genomic DNA was isolated from the FFPE scrolls following a slightly modified manufacturers’ protocol from Zymo genomic DNA extraction FFPE kit . Paraffin was removed by adding a Xylene-based deparaffinization solution and incubating at 55°C for 30 minutes. The wax layer was then aspirated and the remaining biopsy tissue was digested overnight using 10mg/ml Proteinase K . The quantity and quality of DNA was assessed using a Nanodrop™ II HPV HR detection kit . The Anyplex™ II HPV HR detection kit  targets the HPV L1 gene (encoding the capsid) using primers provided in the Anyplex kit. This assay uses Dual Priming Oligonucleotides (DPO™) and tagging oligonucleotide cleavage and extension (TOCE™) technology which enables the distinguishing of 14 HR-HPVs in a single reaction, namely, HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68. A total reaction of 20μl was used during genotyping, following the manufacturers’ protocol. Each reaction contained a human β-globin internal control, L1 primers, oligonucleotides and enzymes. A negative control, as well as 3 positive controls (DNA mixture of pathogen clones) were run concurrently with each test plate to confirm the validity of the amplification. The CFX96™ real-time thermocycler  was used under thermocycling conditions recommended in the Seegene protocol. Fluorescence was detected using the end point cyclic melting temperature analysis. Seegene Viewer v2.0 program  was used to analyse and interpret fluorescence data. Each result was considered valid when the internal control was detected in the sample (IC+). Positive result (+) indicated HPV DNA presence, while negative result (-) indicated the absence of the viral DNA. All HPV negative results with a positive endogen internal control (IC+) were not re-analysed.Genotyping for high-risk HPV subtypes was undertaken using clinically validated AnyplexData on the HPV genotypes of African populations was extracted from the HPV Information Centre database . The HPVSTATA v12.0  and Graphpad v8  statistical software packages were used for data analysis. Demographic and clinical data were allocated into either continuous data or categorical data. Continuous data were expressed as mean ± standard deviation or median (inter-quartile range), while the categorical data was expressed as absolute or relative frequencies. For the comparison of the sociodemographic factors between HIV negative and HIV positive study participants, the two-sample Wilcoxon rank-sum test and Chi-squared test of independence was used. Correlation between the various HPV genotypes and HIV status was performed using Poisson’s and Logistic regression. The Chi-squared test of homogeneity was used to compare the frequency of HPV genotypes among African women using Fisher’s exact and Chi-squared values as test statistics. Univariate regression was used to determine an association between the established HPV risk factors with the HR-HPV genotype infections. Multivariate logistic regression was used to determine an association between the pairings of the HPV genotypes using age, and HIV status as predictors for the model. Further sensitivity analyses were performed for the co-segregating HPV genotypes, using multivariate regression models, and controlling for known HPV risk factors, namely, age, history of STI, parity, HIV status, age of sexual debut and tumour histology. We considered p<0.05 as statistically significant. For analysis, all individual HPV genotypes regardless of whether they occur as single or multiple infections were analysed and reported as standalone genotypes. None of the statistical analysis took into account the species from which the HPV genotypes are from.Sociodemographic and clinical characteristics of the study group are summarized in The 258 cervical cancer patients were screened for HR-HPV DNA, and 96% (n = 248) were positive, representing 14 HPV types   There waAbout one-third (n = 85) of the study cohort were harbouring a single HR-HPV infection. The prevalence and distribution of HR-HPV mono infections is illustrated in Majority (67%) of the cohort exhibited multiple HPV infections. Of the 14 HR-HPV genotypes analysed in this study, 63% (n = 163) harboured multiple (>2) HPV genotypes . Fifty nFurthermore, the incidence of multiple-type HPV infections was assessed to determine if specific HPV genotypes tend to co-segregate, or if the multiple-type infections occur as a result of chance. HIV status was not a significant contributor to the co-segregation patterns. We used multivariate logistic regression, using the HPV genotypes as predictors, while controlling for age and HIV status, as illustrated below . We deteThe prevalence of the various HPV genotypes in the Zimbabwean study cohort was compared to the most common HPV genotypes on other African women across the continent, using data obtained from HPV Information Centre database  and summet al. used PGMY09/11 PCR and dot plot hybridization. Although both methods yield highly concordant findings, the PGMY09/11 HPV detection method has been associated with a higher rate of false negatives compared multiplex PCR Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: NoReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: NoReviewer #2: No**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This cross-sectional study on HPV types in 258 cases of cervical cancer in Zimbabwe, categorized by HIV status, supports earlier results, which have shown that differences in HPV type distribution diminishes with the severity of the cervical lesions. This study also brings more data to the growing body of knowledge regarding the increased proportion of HPV35 in women from sub-Saharan Africa/ancestry.1. Throughout the text “despite” is often used when it should be “irrespective of” or “regardless of” for example third line in result-section of abstract.2. Methods: I would suggest using regression analyses for Table 2, 3 and 4 rather than Chi2. Either Poisson regression for prevalence ratios or Logistic regression for odds ratios.3. Results: I suggest categorizing by HIV status in Figure 2.4. Results: For better understanding of Figure 3A and Figure 3B it might be better if the absolute numbers of certain genotype combinations were presented. I lack an explanation as to how to assess the odds ratios (eg OR increased at 3.3 for the most common combination 16/35 but decreased at OR 0.3 for the second most common combination 16/18. Is 16 reference?).5. Results: It says 58% having 2 HR-HPVs in the text but 59% in Table 3.6. Supplementary Table 1. Suggest adding HIV as risk factorReviewer #2: In this hospital-based cross-sectional study, the authors examine the distribution of high-risk HPV in HIV+ and HIV- women in Zimbabwe. The study reported high prevalent of HPV 35 among women confirmed with cervical cancer, and HIV status was not associated with frequency, multiplicity and consanguinity of HR-HPV in cervical cancer. The study is well-designed and performed, and also provides important information on high-risk HPV distribution in African women.Several aspects could be further clarified:1. It would be informative to provide a brief description of the HPV vaccination program and uptake in Zimbabwe early in the introduction.2. Could the authors clarify the reasons of only including cervical cancer diagnosed at stage 1b-4.3. It seems the histopathological characteristics are available for all cases included in this study. Such information should be included in the descriptive analysis.4. Maybe only relevant covariables to the current study are needed to be described. Some of the information collected through the project but not used for the study might not be necessary to mention, such as weight, height, comorbidities and treatment etc.5. I figure no significant association was detected in the univariate regression between HPV-related risk factors  and any HR-HPV genotypes might be due to limited power, when the HR-HPV types were stratified by each type. However, those are known factors that associated with HPV infection. Could the authors provide estimates (figure 3b) additionally adjusting for those factors and histology at least as sensitivity analysis when examine the co-occurrence of HPV types. Besides, please clarify what is the reference group for the ORs.6. A legend explaining how the HR-HPV types were classified  especially for those cases with multiple types of HPV infection in table 1 could be informative.7. In table 4, could the authors clarify the data source for the HPV types in each region of Africa if they are from external studies?8. Could the authors provide a detailed descriptive table as supplement the distribution of HR-HPV types by HIV status for all included women (including the specific types for multiple infections)?**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  10 May 2021Response to reviewer comments: PONE-D-21-0407128 April 2021Dear Professor, Joakim Dilner,Thank you for reviewing our manuscript PONE-D-21-04071- entitled “High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women” and providing us with constructive criticism on how to improve the statistical analysis and the scientific soundness of the work. Below are our responses to each of the reviewers’ and editor’s comments. Reviewer #1:1. Throughout the text “despite” is often used when it should be “irrespective of” or “regardless of” for example third line in result-section of abstractThank you for pointing this out. The authors have edited the document accordingly, and replaced the word “despite” with other terminologies2. Methods: I would suggest using regression analyses for Table 2, 3 and 4 rather than Chi2. Either Poisson regression for prevalence ratios or Logistic regression for odds ratios.https://hpvcentre.net) and so the best analyses to compare these frequencies is using the Chi2 statistical methods.Thank you for this suggestion. The re-analysis for Tables 2 and 3 was performed, as per the reviewer’s recommendations. Data generated for Table 2 was tested using Poisson’s regression, while Table 3 was analysed using logistic regression. However, Table 4 compares absolute frequencies for the HPV genotypes as recorded by an established database, the HPV Information Centre (Table 2: HR-HPV genotypes by HIV status (n=258) and the HPV phylogenetic classification.  Frequency (Proportion) HPV genotype All HIV negative HIV positive IRR (95% CI) p valueNegative 10 (0.04) 6 (0.02) 4 (0.02) 0.40 (0.22 – 1.00) 0.44316a 123 (0.48) 66 (0.26) 57 (0.22) 0.97 (0.69 - 1.37) 0.88118b 65 (0.25) 34 (0.13) 31 (0.12) 1.11 (0.78 – 1.57) 0.57331a 17 (0.07) 8 (0.03) 9 (0.04) 1.06 (0.57 – 1.96) 0.85433a 25 (0.10) 18 (0.07) 7 (0.03) 0.81 (0.49 – 1.34) 0.41635a 68 (0.26) 40 (0.16) 28 (0.10) 0.90 (0.63 – 1.29) 0.57439b 10 (0.04) 3 (0.01) 7 (0.03) 1.42 (0.67 – 3.05) 0.36145b 14 (0.05) 8 (0.03) 6 (0.02) 1.05 (0.53 – 2.06) 0.89251c 11 (0.04) 5 (0.02) 6 (0.02) 1.35 (0.75 – 2.45) 0.31652a 9 (0.04) 5 (0.02) 4 (0.02) 0.88 (0.33 – 2.38) 0.79956d 6 (0.02) 2 (0.0) 4 (0.02) 1.11 (0.41 – 2.99) 0.84358a 23 (0.11) 9 (0.05) 14 (0.05) 1.28 (0.78 – 2.09) 0.33459b 3 (0.01) 0 (0.00) 3 (0.01) 1.67 (0.53 – 5.24) 0.38066d 1 (0.0) 0 (0.00) 1 (0.01) 1.1 (0.15 – 7.89) 0.92268b 7 (0.03) 2 (0.01) 5 (0.02) 1.23 (0.51 – 3.01) 0.645IRR= incidence rate ratio computed from the Poisson regression analysis a= Human papillomavirus alpha 9 species; b= Human papillomavirus alpha 7 species; c= Human papillomavirus alpha 5 species; d= Human papillomavirus alpha 6 species.Table 3: Multiple HR-HPV infections, stratified by HIV status (n=163).Number of HPVs found together Frequency (Proportion)  All HIV negative HIV positive OR (95% CI) p-value2 94 (0.59) 45 (0.28) 50 (0.31) 1.65 (0.46 – 5.98) 0.4433 31 (0.19) 16 (0.10) 15 (0.09) 2.10 (0.55 –8.01) 0.2774 19 (0.12) 11 (0.07) 8 (0.05) 0.80 (0.16 – 4.02) 0.7825 12 (0.08) 6 (0.04) 6 (0.04) 1.75 (0.08- 36.28) 0.7186 6 (0.04) 4 (0.03) 2 (0.01) 1.2 (0.34 – 4.41) 0.7563. Results: I suggest categorizing by HIV status in Figure 2.The authors appreciate this recommendation and have presented the data as recommended, please see below.Figure 2: The HPV mono infection distribution in women with confirmed cervical cancer (n=85).4. Results: For better understanding of Figure 3A and Figure 3B it might be better if the absolute numbers of certain genotype combinations were presented. I lack an explanation as to how to assess the odds ratios (eg OR increased at 3.3 for the most common combination 16/35 but decreased at OR 0.3 for the second most common combination 16/18. Is 16 reference?). Thank you for indicating this, a supplementary table was added to this effect (Supplementary Table 2). Logistic regression analysis was performed to estimate the co-occurrence by adding the two specific HPV genotypes as the predictor and co-variate. For example, where HPV16/18 are analysed, it indicates that HPV16 is the predictor, and HPV18 is the covariate. In the comparative analysis, the reference is the whole dataset, less the HPV genotypes being analysed for co-occurrence. Supplementary Table 2: HPV co-occurrence regression analysis, without HIV status, and with HIV status as a co-variate.Composite Genotypes N (proportion) OR (95% CI)a pa OR (95% CI)b pb16/18 44 (0.17) 0.3 (0.2- 0.5) <0.001 0.3 (0.2- 0.5) <0.00116/31 14 (0.05) 0.9 (0.4-2.2) 0.824 1 (0.4- 2.1) 0.77316/33 18 (0.07) 0.3 (0.2 – 0.6) 0.001 0.3 (0.2-0.7) 0.00116/35 83 (0.32) 3.3 (1.9 – 5.7) <0.001 3.7 (2.1-6.4) <0.00116/39 6 (0.02) 0.7 (0.2 – 2.3) 0.555 0.6 (0.2 – 2.9) 0.35816/45 14 (0.05) 1.7 (0.6 – 4.8) 0.325 1.7 (0.6 – 5.0) 0.33116/51 15 (0.06) 1.8 (0.6 – 5.2) 0.257 1.6 (0.6 – 4.6) 0.38616/52 5 (0.02) 0.6 (0.2 – 2.0) 0.391 0.6 (0.2 – 2.2) 0.42616/56 7 (0.03) 4.2 (0.5 – 34.8) 0.181 4.3 (0.5 – 36.1) 0.18216/58 23 (0.09) 1.4 (0.6 – 3.2) 0.410 1.4 (0.6 – 3.2) 0.41016/59 1 (0.00) 0.2 (0.0 – 1.9) 0.154 0.1 (0.0 – 1.3) 0.08318/31 5 (0.02) 0.6 (0.21 – 1.6) 0.285 0.5 (0.2 – 1.5) 0.25018/33 12 (0.05) 0.7 (0.4 – 1.5) 0.422 0.8 (0.4 – 1.7) 0.56718/35 26 (0.10) 0.6 (0.4 – 1.0) 0.068 0.6 (0.4 – 1.1) 0.08918/39 1 (0.00) 0.2 (0.0 – 1.6) 0.135 0.2 (0.0- 1.3) 0.08818/45 3 (0.01) 0.4 (0.1 – 1.3) 0.134 0.4 (0.1 – 1.3) 0.11618/51 7 (0.03) 1.2 (0.4 – 3.0) 0.753 1.0 (0.4 – 2.7) 0.99818/52 3 (0.01) 0.9 (0.2 – 3.6) 0.899 1.0 (0.2 – 4.0) 0.96118/56 4 (0.02) 2.2 (0.5 – 8.9) 0.275 2.2 (0.5 – 9.3) 0.29118/58 11 (0.04) 1.1 (0.5 – 2.5) 0.745 1.0 (0.5 – 2.2) 0.97918/59 2 (0.01) 2.2 (0.3 – 15.6) 0.443 1.7 (0.2 – 12.5) 0.62118/68 1 (0.00) 0.3 (0.0 – 2.1) 0.207 0.2 (0.0 – 1.8) 0.16431/33 1 (0.00) 0.3 (0.0 – 2.1) 0.210 0.3 (0.0 – 1.9) 0.18331/35 7 (0.03) 0.9 (0.3 – 2.2) 0.751 0.9 (0.4 – 2.3) 0.82331/45 3 (0.01) 0.3 (0.0 - 1.9 ) 0.183 2.5 (0.7 -9.7) 0.16931/51 2 (0.01) 1.4 (0.3 – 6.6) 0.647 1.2 (0.3 – 5.8) 0.78431/52 3 (0.01) 6.0 (1.4 – 25.1) 0.014 6.8 (1.6 – 29.2) 0.01031/56 1 (0.00) 1.8 (0.2 – 15.5) 0.582 1.8 (0.2 – 15.4) 0.60831/58 1 (0.00) 0.4 (0.0 – 2.9) 0.348 0.3 (0.0 – 2.6) 0.29131/68 1 (0.00) 1.6 (0.2 – 13.3) 0.668 1.5 (0.2 – 12.4) 0.73433/35 8 (0.03) 0.4 (1.2 - 0.9) 0.019 0.4 (0.2 -0.9) 0.02433/39 1(0.00) 0.6 (0.1 – 4.7) 0.610 0.5 (0.1 – 3.9) 0.49433/45 4 (0.02) 1.6 (0.5 – 5.2) 0.404 1.6 (0.5 – 5.3) 0.41833/51 2 (0.01) 0.6 (0.1 – 2.9) 0.560 0.5 (0.1 – 2.5) 0.43233/52 1 (0.00) 0.6 (0.1 – 5.3) 0.685 0.7 (0.0 – 5.6) 0.72033/58 5 (0.02) 1.1 (0.4 – 3.0) 0.845 1.0 (0.4 – 2.7) 0.97733/68 3 (0.01) 2.9 (0.7 – 12.6) 0.150 3.1 (0.7 – 12.9) 0.12035/39 2 (0.01) 0.4 (0.1 – 2.0) 0.288 0.3 (0.1 -1.7) 0.18535/45 5 (0.02) 0.4 (0.1 – 2.0) 0.288 0.7 (0.2 -2.0) 0.46235/51 13 (0.05) 4.1 (1.6 – 10.6) 0.004 4.0 (1.4 – 10.0) 0.00835/52 2 (0.01) 0.5 (0.1 – 2.3) 0.369 0.5 (0.1 – 2.5) 0.39435/56 3 (0.01) 1.2 (0.3 – 5.1) 0.805 1.1 (0.3 – 5.1) 0.85135/58 12 (0.05) 1.2 (0.6 – 2.6) 0.608 1.1 (0.5 – 2.4) 0.83235/66 1 (0.00) 2 (0.1 – 32.3) 0.625 2.0 (0.1 – 35.0) 0.63939/45 1 (0.00) 1.6 (0.2 – 12.9) 0.677 1.5 (0.2 -12.9) 0.69039/51 3 (0.01) 6.2 (1.5-25.7) 0.011 5.5 (1.3 – 23.1) 0.01939/56 2 (0.01) 11 1.9 -62) 0.007 10.9 (2.0 - 64.3) 0.00839/58 1 (0.00) 0.9 (0.1 -7.0) 0.894 1 (0.1 – 6.3) 0.81039/59 1 (0.00) 9.9 (0.9 - 103) 0.056 7.8 (0.7 – 84.6) 0.09039/68 1 (0.00) 3.4 (0.4 – 30.1) 0.279 3.6 (0.5 – 30.0) 0.28045/52 1 (0.00) 1.9 (0.2 – 15.9) 0.569 1.9 (0.2 – 15.9) 0.56945/56 1 (0.00) 2.2 (0.2 – 19.3) 0.479 2.2 (0.2 – 19.3) 0.47945/58 1 (0.00) 0.4 (0.0 – 3.2) 0.400 0.4 (0.0 – 3.2) 0.40045/59 1 (0.00) 4.2 (0.4 – 43.8) 0.229 4.2 (0.4 – 43.8) 0.22945/68 1 (0.00) 2.0 (0.2 - 16.6) 0.533 1.8 (0.2 – 15.6) 0.59151/52 1 (0.00) 1.6 (0.2 – 13.7) 0.643 1.4 (0.2 – 12.0) 0.74051/56 2 (0.01) 5.3 (1.0 – 28.0) 0.051 5.3 (1.0 – 28.9) 0.05651/58 2 (0.01) 1.0 (0.2 – 4.4) 0.965 0.9 (0.2 – 3.9) 0.84758/68 2 (0.01) 2.3 (1.0 – 11.2) 0.300 2.4 (0.5- 12.5) 0.29659/68 2 (0.01) 42 (5.3 – 349.4) 0.001 35.4 (4.2 - 301) 0.00166/68 1 (0.00) 37 (2.2 – 565.6) 0.013 40 (2-766.4) 0.014a- analyses with no consideration of HIV status; b- analyses with HIV status as a co-variate5. Results: It says 58% having 2 HR-HPVs in the text but 59% in Table 3.Thank you for this observation, this statement has been clarified based on re-analysis of the data, and the actual value is 59%.6. Supplementary Table 1. Suggest adding HIV as risk factorThe authors appreciate this recommendation and have presented the data as recommended.Supplementary Table 1: Risk factors of HPV in association with the various HPV genotypes Odds ratio 95% CI P valueHPV16 Age 1.0 0.9 – 1.0 0.686Sexual debut 0.8 0.6 - 1.2 0.304Parity 1.7 0.8 – 3.6 0.146STI history 0.7 0.1 – 7.7 0.751HIV 1.0 0.6 – 1.5 0.839HPV18 Age 12.5 -13 – 38 0.333Sexual debut -6.7 -22 – 9 0.398Parity 1.3 0.5 – 3.5 0.627STI history 0.2 -0.0 – 0.09 0.489HIV 1.2 0.8 – 1.9 0.446HPV31 Age 1.0 1.0 – 1.1 0.808Sexual debut 0.9 0.7 – 1.1 0.340Parity 0.8 0.6 – 1.0 0.063STI history 2.3 0.8 – 6.3 0.117HIV 0.8 0.3 – 2.3 0.664HPV33 Age 1.0 0.9 – 1.0 0.391Sexual debut 1.0 0.8 – 1.2 0.746Parity 1.0 0.8 – 1.3 0.668STI history 1.2 0.5 – 3.0 0.684HIV 0.4 0.2 – 1.1 0.079HPV35 Age 1.2 0.9 – 1.5 0.341Sexual debut 0.7 0.3 – 1.4 0.312Parity 1.0 -4.0 – 6.0 0.683STI history 0.0 -0.0 – 0.10 0.576HIV 0.8 0.5 – 1.3 0.447HPV39 Age 1.0 1.0 – 1.1 0.307Sexual debut 1.2 0.9 – 1.4 0.148Parity 1.0 0.7 – 1.3 0.761STI history 1.4 0.4 – 5.1 0.649HIV 2.1 0.5 – 8.8 0.295HPV45 Age 1.1 0.82 – 1.44 0.551Sexual debut 0.9 0.60 – 1.24 0.414Parity 1.2 0.45 – 3.39 0.681STI history 0.3 -0.76 – 1.33 0.566HIV 1.1 0.4 – 2.8 0.854HPV51 Age 1.1 0.88 -1.31 0.480Sexual debut 0.7 0.25 – 1.7 0.374Parity 1.1 0.36 – 3.54 0.834STI history 1.0 -0.24 – 0.52 0.424HIV 1.9 0.7 - 4.7 0.179HPV52 Age 1.1 0.85 – 1.52 0.382Sexual debut 0.9 0.50 – 1.50 0.579Parity 0.5 0.09 – 3.18 0.487STI history 0.3 -0.21 – 0.71 0.242HIV 0.8 0.2 – 2.9 0.731HPV58 Age 1.0 1.0 – 1.1 0.209Sexual debut 1.0 0.9 – 1.2 0.800Parity 1.0 0.8 – 1.2 0.661STI history 0.8 0.3 – 2.0 0.573HIV 2.5 1.0 – 6.2 0.051HPV58 Age 1.1 0.86 – 1.44 0.419Sexual debut 0.7 0.25 – 1.67 0.374Parity 1.1 0.44 – 2.79 0.822STI history 0.0 -0.14 – 0.23 0.611HIV 1.6 0.8 – 3.4 0.194HPV59 Age 0.1 -0.08 – 0.24 0.198Sexual debut 1.0 0.88 – 1.22 0.650Parity -0.8 -2.53 – 1.03 0.272STI history 0.7 -0.70 – 2.03 0.219HIV 3.7 0.4 – 35.72 0.262HPV66a Age 1.1 0.81 – 1.57 0.473Sexual debut 1.0 0.81 – 1.21 0.959Parity 1.0 -0.31 – 0.54 0.369HIV 1.2 0.1 – 19.47 0.894HPV68 Age 1.1 0.83 – 1.39 0.594Sexual debut 1.1 0.91 – 1.29 0.382Parity 1.2 0.49 – 2.94 0.685STI history 0.3 -0.61 – 1.28 0.420HIV 1.5 0.40 – 5.79 0.535Number of HPV infections Age 1.0 0.95 – 1.04 0.802Sexual debut 0.9 0.82 – 1.01 0.085Parity 1.0 0.76 – 1.20 0.690STI history 0.5 0.14 – 1.62 0.239HIV 1.5 0.42 – 5.13 0.545Note: STI, sexually transmitted infection; CI, confidence interval; a – no individual with HPV genotype had STI historyReviewer #2:1. It would be informative to provide a brief description of the HPV vaccination program and uptake in Zimbabwe early in the introduction.The coverage for the HPV prophylactic vaccines is in its infancy in most developing countries, where the coverage is estimated to be more than 12-fold lower than developed countries [10]. As a matter of interest countries such as Zambia, Malawi and Zimbabwe, where the highest global cervical cancer cases are recorded, are still in the pilot and early implementation phases of the national HPV vaccination programs [11-12]. 2. Could the authors clarify the reasons of only including cervical cancer diagnosed at stage 1b-4.Thank you for noting this, we have clarified the selection criteria for our cohort. Our study recruited individuals from an outpatient radiotherapy and chemotherapy treatment facility, therefore, only patients seeking this type of care attend the clinics. Consequently, this excludes patients receiving surgical interventions only, or are receiving palliative care, because these individuals receive care from other oncological units in the hospital. The methods describe this as:The participants from the current study were nested in a pharmacogenomics of cervical cancer study, aiming to recruit women who were newly diagnosed with cervical cancer, and were eligible to receive radical therapy. Potential study participants were identified through the RTC hospital registry. To be considered eligible for this study, women had to be ≥18 years, with histologically confirmed diagnoses of invasive cervical cancer staged between 1b – 4A, and were earmarked for curative anti-cancer therapy. Individuals who were diagnosed with resectable (stage 1A) disease that did not require chemo/radio- therapies, or with advanced disease that required palliative care (stage 4B) were excluded from the study, because these individuals were referred for care outside of the RTC.3. It seems the histopathological characteristics are available for all cases included in this study. Such information should be included in the descriptive analysisThis descriptive data is now included in the demographics table (Table 1) as illustrated below. Tumour Histology Combined n(prop) HIV – n(prop) HIV + n(prop) PSquamous Cell 207 (0.80) 115 (0.83) 92 (0.77) Ref.Adenocarcinoma 25 (0.10) 14 (0.10) 11 (0.09) 0.950bAdenosquamous 11 (0.04) 2 (0.01) 9 (0.08) 0.017 bOther* 15 (0.06) 8 (0.06) 7 (0.06) 0.900bOther*= spindle cell carcinoma, papillary serous carcinoma, adenoid cystic, adenosarcoma, small cell carcinoma.4. Maybe only relevant covariables to the current study are needed to be described. Some of the information collected through the project but not used for the study might not be necessary to mention, such as weight, height, comorbidities and treatment etc.Thank you for this observation. All variables that are not further analysed in this study have been deleted. The amended statement reads: Demographic information such as age, residency, histopathological tumour characteristics, were collected from the patient folder in the RTC. In addition, behavioral and lifestyle factors were collected by interviewing the study participants, namely history of alcohol, smoking and parity. Sexual history information including age at sexual debut, number of sexual partners, history of circumcised partner, sexually transmitted infections were also collected. Data on HIV status was also collected for comparative analysis.5. I figure no significant association was detected in the univariate regression between HPV-related risk factors  and any HR-HPV genotypes might be due to limited power, when the HR-HPV types were stratified by each type. However, those are known factors that associated with HPV infection. Could the authors provide estimates (figure 3b) additionally adjusting for those factors and histology at least as sensitivity analysis when examine the co-occurrence of HPV types. Besides, please clarify what is the reference group for the ORs.As observed by the reviewer, these analyses had not been performed due to a lack of association detected in the univariate analysis. To ensure that these findings were not influenced by limited power of study, multivariable regression was performed using the HPV co-occurrences as predictors, and adjusting for the known HPV risk factors- age, sexual debut, parity, STI history and HIV status, and the findings were tabulated into Supplementary Table 3. Although there were no definitive indicators determined in this sensitivity analyses, we did observe a tendency towards of the HPV16/18 co-occurrence to be influences by age HIV status and age of sexual debut. A larger cohort would be needed to confirm the validity of the potential trend observed here.Supplementary Table 3. Multivariate regression analysis of co-occurring HPV genotypes with risk factors.Co-variates OR (95% CI) PHPV16/18 Age 1.0 (1.0 – 1.1) 0.684HIV 2.3 (0.9 -5.6) 0.065STI history 0.5 (0.2 – 1.3) 0.159Parity 1.1 (0.9 – 1.4) 0.274Age of sexual debut 1.1 (1.0 – 1.3) 0.076HPV16/33 Age 1.0 (0.9 – 1.0) 0.547HIV 0.4 (0.1 – 1.5) 0.174STI history 1.0 (0.2 – 3.9) 0.956Parity 1.0 (0.7 – 1.3) 0.821Age of sexual debut 1.0 (0.8 – 1.2) 0.834HPV16/35 Age 1.0 (1.0 – 1.1) 0.152HIV 0.8 (0.4 – 1.6) 0.573STI history 1.1 (0.7 – 2.2) 0.769Parity 1.0 (0.9 – 1.2) 0.985Age of sexual debut 1.0 (0.9 – 1.2) 0.532HPV35/51 Age 1.0 (1.0 – 1.1) 0.282HIV 1.0 (0.2 – 4.4) 0.999STI history 3.0 (0.8 – 12.2) 0.115Parity 0.9 (0.7 – 1.3) 0.670Age of sexual debut 1.0 (0.8 – 1.3) 0.856HPV35/52 Age 1.0 (0.8 – 1.3) 0.930HIV NA NASTI history NA NAParity 0.9 (0.3 – 3.4) 0.933Age of sexual debut 1.5 (0.7 – 3.6) 0.332HPV39/51 Age 1.0 (0.8 – 1.2) 0.827HIV NA NASTI history NA NAParity 1.2 (0.7 – 2.0) 0.602Age of sexual debut 1.0 (0.5 – 1.9) 0.984HPV39/56 Age 1.2 (1.0 – 1.4) 0.108HIV NA NASTI history NA NAParity 1.2 (0.7 – 2.3) 0.520Age of sexual debut 1.2 (0.7 – 2.3) 0.5826. A legend explaining how the HR-HPV types were classified  especially for those cases with multiple types of HPV infection in table 1 could be informative.Thank you for this recommendation. In our analysis, HPVs were only analysed at a genotype level, due to the limited coverage of the HPV genotyping assay used to test for HPV DNA. We have clarified this in the methods section of the manuscript, and have also included a figure legend in the results for reference to phylogenetic clades in the discussion section.Methods section:For analysis all individual HPV genotypes regardless of whether they occur as single or multiple infections were analysed were reported as standalone genotypes. None of the statistical analysis taking into account the species from which the HPV genotypes are from.Results section:a= Human papillomavirus alpha 9 species; b= Human papillomavirus alpha 7 species; c= Human papillomavirus alpha 5 species; d= Human papillomavirus alpha 6 species.7. In table 4, could the authors clarify the data source for the HPV types in each region of Africa if they are from external studies?https://hpvcentre.net).Thank you for this comment. We have clarified in the results section where we obtained the data from. The prevalence of the various HPV genotypes in the Zimbabwean study cohort was compared to the most common HPV genotypes on other African women across the continent, using data obtained from HPV Information Centre database ?The additional data for the HPV multiple infections by HIV status was collated into a supplementary table 2. Further inclusion of more than 2 HR-HPVsAttachmentPLOS Repsonses to reviewers_14Apr2021.docxSubmitted filename: Click here for additional data file. 12 Aug 2021PONE-D-21-04071R1High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women.PLOS ONEDear Dr. Dandara,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.==============================Please take into account the concerns raised by Reviewer #2 after the first round of revision.==============================plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Sep 26 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). 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If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: All comments have been addressedReviewer #2: (No Response)**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. 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If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: (No Response)Reviewer #2: No**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: (No Response)Reviewer #2: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: (No Response)Reviewer #2: Thank you for the extensive work from the authors in addressing the comments, and manuscript has been substantially improved. However, there are a couple responses to the comments could be further clarified:In the response to comment point 5, the author has already performed a multivariate regression analysis, using the HPV genotypes as predictors, while controlling for age and HIV status, and the ORs for significant HPV genotypes were illustrated in fig 3b. Therefore, the sensitivity analysis is expecting to be repeating exact the same regression model by additionally control for factors of sexual debut, parity, STI history and histology and presented the ORs as a supplementary figure or table instead of presenting ORs for each of the covariate as shown in the current supplementary table 3.I appreciate the work done by the authors for supplementary table 2, however, what would be informative to address the comments point 8 is to provide a simple frequency table showing the distribution of hrHPV types, including both single type infection and multiple types of infection by HIV status (HIV positive and HIV negative). Considering there are a proportion of women had more than 2 types of HPV infection (table 2), showing the actual HPV types by HIV status could be interesting to the readership.**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  24 Aug 2021Response to Reviewer Comments: PONE-D-21-04071R124 August 2021,Dear Professor Graciela AndreiThank you for reviewing our manuscript entitled “High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women” and providing us with constructive criticism on how to improve the statistical analysis and the scientific soundness of the work. Below are our responses to each of the reviewers’ and editor’s comments; Reviewer #2: Thank you for the extensive work from the authors in addressing the comments, and manuscript has been substantially improved. However, there are a couple responses to the comments could be further clarified. In the response to comment point 5, the author has already performed a multivariate regression analysis, using the HPV genotypes as predictors, while controlling for age and HIV status, and the ORs for significant HPV genotypes were illustrated in fig 3b. Therefore, the sensitivity analysis is expecting to be repeating exact the same regression model by additionally control for factors of sexual debut, parity, STI history and histology and presented the ORs as a supplementary figure or table instead of presenting ORs for each of the covariate as shown in the current supplementary table 3.Thank you for further clarifying the expectations of the reviewer, which the authors may have misunderstood in the previous peer review process. The authors have done the best they can to address this comment. To this effect, S3 Table was added, consisting of multivariate analysis for the co-segregation of the HPV genotypes, taking into consideration the proposed covariates, namely, age, history of STI, parity, HIV status, age of sexual debut, tumour histology, as suggested. The comment was addressed in the manuscript to read: In order to ensure that lack of significance observed was not a result of low study power, a sensitivity appraisal was conducted, using multivariate regression analyses controlling for age, history of sexually transmitted infections, parity, HIV status, age at sexual debut and tumour histology. In this sensitivity analyses, all the co-segregation patterns remained statistically significant except HPV16/33 , HPV35/52  (S3 Table). Table S3. Sensitivity analysis for HPV co-segregation.HPV genotypes* OR (95% CI) P16/18 0.3 (0.1-0.5) <0.0116/33 0.4 (0.2-1.0) 0.0516/35 3.6 (1.8-7.2) <0.0135/51 8.8 (2.3-34.3) <0.0135/52 0.5 (0.1-4.3) 0.5039/51 4.2 (0.7-25.9) 0.1239/56 9.5 (1.1-82.9) 0.04*multivariate analysis for the co-segregation of HPV genotypes, taking into consideration age, history of STI, parity, HIV status, age of sexual debut, tumour histology. I appreciate the work done by the authors for supplementary table 2, however, what would be informative to address the comments point 8 is to provide a simple frequency table showing the distribution of hrHPV types, including both single type infection and multiple types of infection by HIV status (HIV positive and HIV negative). Considering there are a proportion of women had more than 2 types of HPV infection (table 2), showing the actual HPV types by HIV status could be interesting to the readership.Thank you for indicating which analyses and data would be more interesting to the readers. The comments were received well and taken into consideration. The authors have amended the table to report on absolute frequencies, by HIV status, as shown below. This tabulation was favoured as it clearly represents the number of individuals with specific HPV genotypes depending on HIV status.S2 Table. The number of individuals harbouring specific HPV genotypes stratified by HIV status. Number of HPVs (n)HPV genotype HIV status 1 2 3 4 5 616 - 19 18 5 1 0 0 + 15 13 3 1 1 018 - 13 12 1 0 1 1 + 6 13 1 0 0 031 - 1 0 1 2 0 0 + 1 0 0 0 0 133 - 2 0 1 2 0 0 + 2 0 0 0 0 035 - 15 15 3 0 1 0 + 3 10 3 0 1 139 - 0 2 0 1 0 0 + 0 1 0 0 0 045 - 1 0 2 3 0 0 + 0 0 1 1 0 051 - 0 0 1 0 1 0 + 1 1 1 1 1 152 - 1 2 1 0 0 0 + 0 3 1 0 0 156 - 0 0 0 1 0 0 + 0 0 1 0 1 058 - 0 2 1 1 1 0 + 0 2 0 0 1 059 - 0 0 0 2 0 0 + 2 0 1 1 0 066 - 0 0 0 1 0 0 + 0 0 1 0 0 068 - 2 0 1 0 1 0 + 1 0 1 1 2 1TOTAL 85 94 31 19 12 6AttachmentPONE-D-21-04071R1-responses to reviewers 24082021.docxSubmitted filename: Click here for additional data file. 31 Aug 2021High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women.PONE-D-21-04071R2Dear Dr. Dandara,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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Apis dorsata is unusual in being able to forage during both the day and the night. To date, the extent of this unique nocturnal foraging behavior and the environmental factors correlating with it have not been deeply investigated. We conducted the first systematic investigation into the nocturnal behavior of A. dorsata in Southern India by tracking the daily and nightly foraging activity of A. dorsata colonies in an urban environment for 8 months, over multiple seasons and lunar cycles. We found strong evidence that A. dorsata can behave in a manner that is “cathemeral” (active over the entire diel cycle) when environmental illumination is sufficient for nocturnal flight. However, workers were not always active even when the environment should have been bright enough for them to forage, suggesting that their nocturnal foraging behavior was also affected by seasonal changes in resource availability. The foraging activity observed during the day versus twilight versus night differed between seasons; notably, nocturnal activity rates were higher than diurnal activity rates during the winter. We found that at our study site A. dorsata routinely exhibits both diurnal and crepuscular activity, foraging just as intensely during the short twilight hours as during the day. The high foraging activity observed during the twilight and nighttime hours shows that A. dorsata colonies can extend their foraging beyond the daylight hours and reveals that foraging during these dimly lit hours is an integral part of their foraging ecology. This evidence of the importance of nocturnal and crepuscular foraging by A. dorsata paves the way for future studies examining the role of this species in nocturnal pollination networks, the contribution of nocturnal foraging to colony-level nutrition and energy budget, and the evolution of this unusual behavior. Future work comparing nocturnal activity in light polluted urban environments versus unpolluted natural environments is particularly encouraged to determine the generalizability of these findings.The giant honey bee  Most animal species have evolved to be active only during limited periods of the 24-hour cycle . Those periods of activity, often referred to as the species’ temporal niche, are usually tightly controlled by the endogenous circadian clock, which has been entrained by environmental cycles such as the light-dark cycle, temperature, humidity, etc. –3. An anApis dorsata. This species, which lives throughout tropical Asia, is highly unusual among bees in being able to actively fly and forage during the day as well as some nights , these phenomena are found throughout the animal kingdom. In bees, for example, evidence that species are adapted to be active only during certain portions of the diel cycle are shown through adaptations in anatomy  and e nights , 17.A. dorsata to extend its temporal niche into the night is likely due in part to traits associated with their large size. As the second largest honey bee species in body size, A. dorsata has correspondingly large eyes, as well as more and larger-sized ommatidia, which is correlated with better visual sensitivity in dim light [A. laboriosa, does not forage at night, though this might be due in part to the colder nocturnal temperatures it experiences living in higher altitudes [Apis dorsata workers also have a raised vertex so their ocelli are more pronounced, improving their light sensitivity [A. dorsata has neural circuitry in the optic lobes that perform photon summation across wide parts of their visual field, which improves light capture and would allow them to discriminate coarse images in moonlight even if the resolution of those images would be lower [A. dorsata are thought to be much less sensitive than those of bee species that are specialized for crepuscular or nocturnal activity [A. dorsata has been shown to deviate from the expected size-related patterns in flight energetics and is actually less able to fly at low ambient temperatures than the smaller species A. cerana and A. mellifera [The ability of im light , 11, 18.ltitudes –22. The sitivity , 24, as sitivity . There ibe lower , 26. Nonactivity , 28. Howellifera . Taking A. dorsata foragers to be nocturnally active. Reports show that worker bees can actively forage throughout the night if the moon is at least half full [A. dorsata may be capable of flying using artificial sky glow from city lights even when the moon is below the horizon [A. dorsata might therefore be an example of positive light effects on activity, where the light available from the moon or artificial lights stimulate activity at a time the species would not otherwise be active [Behavioral evidence supports the hypothesis that some level of illumination is necessary for alf full , 16. Fur horizon . The noce active . Regardle active .A. dorsata to fly during the night and during the day is all the more intriguing given that while all three closely related species belonging to the A. dorsata complex , and A. dorsata breviligula (Philippines)) can fly during the night, the even larger Himalayan giant honey bee A. laboriosa cannot, though as mentioned this is likely due in part to colder nighttime temperatures in its range [Apis, with the possible exception of the African bee A. mellifera adansonii [A. dorsata that has led to the evolution of this ability? This question is challenging to answer because we lack basic information about the importance of nocturnal behavior in the foraging ecology of this species, and the environmental conditions that limit the ability to forage actively at night.The ability of ts range –22, 24. dansonii , are strdansonii . Given tA. dorsata benefits from foraging in low light conditions. Nocturnal foraging by animals is often associated with reduced predation and reduced competition [A. dorsata appears to have relatively low metabolic heat output in flight, so we would expect it to be disadvantageous at cooler nighttime temperatures [A. dorsata may be related to food gathering: by extending their diurnal activity into the night, they could prolong their ability to exploit flower species that offer pollen and nectar resources near sunset and sunrise, and gain access to an entirely new set of night-blooming floral resources [Regardless of the underlying mechanisms that permit crepuscular and nocturnal activity, it is likely that petition , 34–36. petition , 37. Howeratures , 29. A pesources , 38–40.A. dorsata. Over the course of eight months, we observed colonies of A. dorsata in Bangalore, India to examine how their diurnal and nocturnal behavior changed over the course of multiple seasons and lunar cycles. We paid particular attention to correlations between nocturnal activity and light availability, as well as between nocturnal activity and temperature, while also relating their behavior to expected resource availability in the surrounding environment. Our data reveal a rich picture of the environmental factors that influence this behavior.Here we present the first systematic investigation into the nocturnal behavior of A. dorsata is widespread and successful in urban cities [A. dorsata in a wider range of environments.We conducted our studies at a scientific research institute on the outskirts of a large city. The advantage of this study site was that we could work with colonies nesting on buildings, and thus could monitor foraging from multiple colonies at a close distance and over an extended period of time. The disadvantage of this site was that light pollution, in the form of “sky glow”, was present, and this may have enabled bees to forage at times they would not have been able to in areas without light pollution . This lin cities , 42, andThis study was conducted on the campus of the National Centre for Biological Sciences–Tata Institute of Fundamental Research (NCBS) in Bangalore, Karnataka, India, from October 2018 through May 2019. NCBS is located in a primarily residential district in the northern part of the city of Bangalore, adjacent to the University for Agricultural Sciences–Bangalore (GKVK). NCBS has extensive ornamental vegetation on its campus and is surrounded by fields of agricultural vegetation on the GKVK campus, as well as by residential tracts offering ornamental plants . BecauseApis dorsata can be found through southern Asia [A. dorsata is eusocial, with perennial colonies that reproduce by colony fission. Each colony consists of a single queen, a large number of workers, and a substantial but variable number of males (drones) during the swarming season. As foragers, the workers are ecological generalists and exploit a wide variety of floral species for nectar and pollen [A. mellifera [A. dorsata can be found in forests, agricultural areas, and urban areas, and often form aggregations of 10 or more colonies in a single location. Apis dorsata is a migratory species, traveling up to 200 km through a series of shorter steps to follow currently available resources [A. dorsata colonies tend to be found within Bangalore during the dry season (which lasts from October to May), migrating into Bangalore in November and December and then migrating out of Bangalore in May and June before the start of monsoon season (which lasts from June to September) [ern Asia , 44. It ern Asia . Like ald pollen , 47. Recellifera . Colonieellifera –51. Coloesources –54. A. dptember) .A. dorsata were observed over the course of this study , we were unable to collect A. dorsata colonies until the day of the full moon, and so colonies were only recorded from the night of the full moon onward for those months. Also, the occurrence of rain at night occasionally prevented us from recording nocturnal activity, though this was rare. Monthly observations were categorized into seasonal categories; October through December comprised “autumn”, January through February comprised “winter”, and March through May comprised “spring/summer” based on the suggestions of local Bangalore residents (We used a video camcorder (Panasonic 4K Ultra HD Model VX981K) placed 0.75m away from the colony to record the nocturnal activity of ough May . At thisough May . Using tesidents .A. dorsata colony for two complete lunar cycles: January 21 to February 19, 2019 and March 21 to April 19, 2019  were identified using the website https://www.timeanddate.com/moon/india/bengaluru. The end of astronomical twilight and beginning of astronomical dawn mark the beginning and ending of night, respectively, as light from the sun is not available in the sky and only the moon, stars, and artificial lights provide illumination. Based upon these astronomical references, activity occurring between sunrise and sunset was considered “diurnal activity”; activity occurring from sunset to the end of astronomical twilight or from the beginning of astronomical dawn to sunrise was considered “twilight activity”; and activity occurring between the end of astronomical twilight and the beginning of astronomical dawn was considered “night activity”. Daily temperature data from the Kempegowda International Airport Weather Station in Bangalore, India (14 km from the NCBS campus) was accessed and recorded from the Weather Underground website .After recording, we analyzed videos to quantify the activity of 2 values, and performance during model diagnostic tests that checked for outliers, dispersion, and deviation . Final A. dorsata colonies exhibited peak nocturnal activity in the hours before and just after sunrise  and before and just after sunset . In support of this assumption, we observed strong positive correlations, at all parts of the diel cycle, between the number of bees arriving and the number of recruitment dances during the same census period (day: Spearman P = 0.0084): as illumination increased the arrival rate also increased . The amount of time that had passed since sunset did not have a significant effect on the arrival rate, nor did the time at which we censused arrivals. We were only able to record illumination during the month of January, and so this correlation between illumination and arrivals derives from the observations of Colony 4 alone.During the night, illumination had a significant positive effect on bee arrivals (P < 2e-16) and moon phase (P < 2e-16), and that the activity seen during a given diel time varied depending on the moon phase  and during twilight  were constant across the moon phases. Arrivals rates during the day and during twilight on a given moon phase generally did not differ (P > 0.05), except for on the night of the waning gibbous moon, where twilight arrival rates were higher than daytime arrival rates (P < 0.05). In contrast, arrival rates at night were highly dependent on proximity to the full moon (P < 0.05) but did match twilight arrival rates on the nights of the full, waxing gibbous, waning gibbous, and the third quarter moons .When examining the effects of moon phase on bee arrival rates, we found that arrival rates varied greatly depending on diel time , though there was not a difference in total activity between lunar cycles (P = 0.27). Arrival rates during the night were higher in the January lunar cycle than the March lunar cycle . Arrival rates during twilight did not differ between lunar cycles (P = 0.99), but twilight had the highest rates of activity within both lunar cycles . Diurnal arrival rates were the next highest in both lunar cycles, with nocturnal activity the lowest in both cycles . Data for both moon phase and lunar cycle analyses stem exclusively from observations of A. dorsata Colony 4 during the lunar cycle observation periods from January 21, 2019 –February 19, 2019 and March 21, 2019 –April 19, 2019.Consistent with these patterns, variation in bee arrival rates at a given diel time was dependent on the lunar cycle (Diel Time*Lunar Cycle: P < 2e-16), and the activity seen at a given diel time differed among seasons , but arrival rates during the day were lower in winter than in spring/summer . During the winter season, in fact, nocturnal arrival rates were higher than diurnal rates . During autumn, arrivals rates were the same regardless of diel time , except for higher arrival rates during twilight than during the night (P < 0.05). In both winter and spring/summer though, there was more activity during twilight than during the day or the night .When we examined all data collected from the five colonies observed over the course of the study, bee activity varied significantly with diel time . While s bright , 30, we Apis dorsata colonies exhibited consistent peaks in activity at sunrise and sunset across the study period .A. dorsata is an example of positive light effects on activity, or “masking”, whereby increased illumination stimulated activity outside of the endogenous circadian rhythm, as has been seen in other diurnal species [A. dorsata’s nocturnal activity [The amount of light available during the nocturnal hours was the primary determinant of nighttime foraging. While this partially supports the idea that nocturnal foraging is an extension of diurnal foraging, the low nocturnal activity seen during the waxing moon phases when the moon is up during the first half of the night , S1 Fig  species , 67. Thiactivity , 16, 30.A. dorsata foragers to get a competitive edge in exploiting morning-blooming flowers [A. mellifera) are reported to have rhythmic circadian patterns of activity that are shorter than 24 hours . Studie.5 hours ; it is p2 , bees were still actively foraging . As we e family , 76, thorocarpum ). Howevetudy had , 76, furtudy had . The seaA. dorsata’s nocturnal foraging , where the temperature can be much cooler than in Bangalore . Because of those differences, some of our results cannot be generalized to all environments. Our findings that the nocturnal activity of A. dorsata is highly dependent on illumination and moon phase does match previous reports of their behavior [A. dorsata is found, then our results provide a critical perspective on the nocturnal behavior of A. dorsata not only in light-polluted areas where A. dorsata is common [A. dorsata forages at night and how their behavior differs depending on the environment.Our study location at a research campus on the edge of the large urban city of Bangalore provided the unique ability to systematically study behavior , 16, 40.behavior , 76, eves common , 42, butA. dorsata should not just be considered a diurnal species that can sometimes forage during twilight or at night, but a cathemeral species when illumination is sufficient to allow for positive direct light effects on activity. There seems to be a tendency toward “crepuscular” activity, but that may simply be a result of an abundance of flower resources combined with sufficient light to permit flight. By studying single colonies over an extended period of time, we provide an initial systematic investigation of the nocturnal foraging behavior of A. dorsata in urban systems, but more research is necessary to draw broad conclusions about the generality of these findings to other systems such as tropical forests without light pollution. We suggest research efforts also be exerted to gain a better understanding of the resource availability at night versus during the day and across seasons in the range of A. dorsata to further clarify the relationship between activity, illumination, resource availability, and season.Our results suggest that, at least in urban systems with light pollution, S1 FileThis file contains model selection tables for (A) the effects of illumination; (B) the effects of moon phase, lunar cycle, and diel time; and (D) the effects of season and temperature on arrivals. Final models selected are highlighted in bold. It also contains (C) estimated marginal means for the interaction between moon phase and diel time for the model of effects of moon phase, lunar cycle, and diel time, as well as (E) a table showing pairwise differences in arrival rates during each diel time across the seasons.(DOCX)Click here for additional data file.S2 FileThis file contains the results of a supplemental analysis comparing activity during the dawn twilight period to activity during the dusk twilight period across seasons. (A) The ANOVA output for the statistical analysis and (B) a summary figure of the results are included.(DOCX)Click here for additional data file.S1 FigDaily foraging activity on each day recorded during the January to February (left) and March to April (right) lunar cycles are shown. Purple shaded regions indicate astronomical twilight, gray shaded regions indicate night, and white indicates daytime. The red dotted line indicated moonrise, while the solid red line indicates moonset. Activity tended to peak during the twilight periods and was only seen at night during the full moons and the weeks before and after them.(DOCX)Click here for additional data file.S1 VideoA. dorsata foragers. Other dances occur during the video but are not marked. A longer unannotated video from the same day provides a larger variety of foragers landing and dancing and can be found at https://www.dropbox.com/s/tv3jcozp6km8p1l/22-1-19_1215_long.mov?dl=0.A short, annotated clip of a video taken during the day on January 22, 2019 (start time 1215 h) is included to serve as a representative example of our diurnal video data. Pink circles during the video show examples of dancing (MP4)Click here for additional data file.S2 VideoA. dorsata foragers; note all three dancers carry pollen. Other dances occur during the video but are not marked. A longer unannotated video from the same night provides a larger variety of foragers landing and dancing and can be found at https://www.dropbox.com/s/1p666dwrgx95knl/21_3_19_2325_long.mp4?dl=0.A short, annotated clip of a video taken during the night on January 21, 2019 (start time 2325 h) is included to serve as a representative example of our nocturnal video data. Three pink circles during the video show examples of dancing (MP4)Click here for additional data file.S1 DataR analysis script. The script was used to analyze all data discussed in the manuscript. Data was analyzed in RStudio.(R)Click here for additional data file.S1 DatasheetThis CSV file was used for the analyses discussed in the manuscript.(CSV)Click here for additional data file.S2 DatasheetThis CSV file was used for creating the moon phase related graphs in this manuscript. The only differences from the dorsata activity datasheet are: it includes only the data collected during the moon phase observations and it includes an additional column for moon phase that allows the moon phases to be easily graphed in order of appearance instead of alphabetically by their name.(CSV)Click here for additional data file.


AMCs reduces the unsustainable and inefficient blue water consumption by 2–17 BCM and 2–13 BCM, respectively. The combination of yield gap closure, drip irrigation, soil mulching, and deficit irrigation has the largest effect on blue water saving; it releases or changes the status of monthly blue water scarcity in 11 provinces; increases field-employees by 132%, food security by 9%, international food-export by 87%, and gross domestic production by 54%. However, it doesn’t fully address blue water overconsumption in the summer period; hence, further measures are needed to reduce blue water scarcity to the sustainable level in these environmental hotspots.Sustainable development requires modifying the current consumption pattern of natural resources. This study investigates efficient tactics for reducing the unsustainability and inefficiency of human’s food-related blue water consumption alongside improving national environmental and socioeconomic status. As a case study for Iran, 15 alternative management scenarios (AMS) were defined compared to the current on-farm management, and their effects were assessed on a monthly scale. Based on the results, 45.5 billion m Agriculture is the biggest freshwater user in the world and has the largest contribution of 92% in global blue water consumption  , is one of the promising solutions which received large attention by many researchers  into the green and blue fractions. Crop’s ET was simulated by using the AquaCrop model p,m and (WFblue)p.m are absolute green and blue WFs (m3 month−1) consumed by all crops grown in province p and in month m, U.WFgreen and U.WFblue are unit green and blue WFs (m3 t−1), t year−1) of crop i in province p, and n is the number of the crops.Total green or blue water consumed within a specific province per month was then estimated by the following equations:t ha−1), planting/harvesting dates, and farming practices were obtained from Iran's Ministry of Agriculture Jihad (IMAJ All required data for the study period of 2005–2015 were collected per crop per province per year. Daily weather data were obtained from observations at 52 synoptic stations spread out over the country. Reference evapotranspiration (ETo) was then calculated based on the FAO-Penman–Monteith equation , 3 month−1), 3 month−1), 3 month−1), p denotes province, and m denotes month. Data on monthly ny IWRMC . Followiny IWRMC , EFRs weny IWRMC .Table 3Dc in province p and month m, blue)c,p,m are, respectively, total and the considered crop’s blue WF in province p and month m, and (BWavailable)p,m is local blue water availability in province p and month m.A month/place with a BWS > 1 is called an environmental hotspot, in which part of the blue water consumption which is beyond blue water availability, is unsuitable. In the hotspot, the unsustainable fraction of the blue WF of a specific crop will be determined based on the ratio of the blue WF of that crop to total blue water consumed in the considered hotspot:Inefficiency. Crop’s blue water consumption is inefficient when it goes beyond its benchmark levels. We obtained the climate-specific benchmark levels for the considered crops from Karandish et al.  of yield statistics reported for that crop in the considered zone over the study period 2005–2015. Yield gap was then estimated as the difference between actual and attainable yields.In Drip irrigation scenario, the existing irrigation system is assumed to be replaced by drip irrigation system except for rice, wheat and barley, for which applying drip irrigation is not technically feasible in the study area.Soil mulching was assumed to be applied within all croplands unless rice fields, since rice is irrigated by basin method; hence, soil mulching is not relevant. In deficit irrigation scenario, crops were assumed to receive 15% less blue water in each irrigation event during their entire growing periods. The other 11 combined scenarios were defined as different combinations of these four measures.The environmental consequences of all scenarios were then assessed in terms of their effects on blue water consumption and saving, unsustainable/inefficient fraction of blue WFs, and BWS. These consequences were compared with the relevant values under the current condition, which is called the base case in this research. Among 15 defined scenario, the best scenario was then selected as one which has the largest effect on reducing unsustainable and inefficient blue WF and BWS. We name it as “the best scenario” afterward.−1) , provincial food security (FSp), the fraction of international food export (IFE), and gross domestic production (GDP). Per crop per province, Iran's Ministry of Agriculture Jihad (IMAJ) defined standard numbers for NFE per unit of crop production (cap t−1) IMAJ . These v−1) IMAJ . For the3 year−1 of blue water is consumed unsustainably within the hotspots (BWS > 1) , and Gilan and Mazandaran (in the humid zone), blue water is fully consumed sustainably in the agricultural sector .Annually, 182–3181 million m3 year−1 of blue water is consumed inefficiently in different provinces  has the highest ones.On an annual scale, 67–1661 million mces Fig. b. Provinblue  in the national IE.WFblue (15.5 billion m3 year−1), followed by fruits (21%), and nuts (15%). Such a dominant contributions of these crop categories are also observed at the province scale.Figure lue Fig. a, b and lue Fig. c, d over3 month−1). Replacing current irrigation systems with drip irrigation when feasible  comes with the lowest blue water saving of 4.8 billion m3 year−1 (a 5% saving compared to the current condition). In contrast, combining yield gap closure with drip irrigation, mulching, and deficit irrigation at the rate of 15% , will save a total of 38.3 billion m3 year−1 blue water (a 42% saving compared to the current condition). In this scenario, the largest saving occurs within the cereals field.Figure blue and IE.WFblue. Except for a few cases, the lowest and the highest reduction in US.WFblue and IE.WFblue occurs under the DR and the DR.MU.DI.YGC scenarios, respectively. Based on the ensemble averages of all scenarios, provincial US.WFblue will decrease from 96 ± 45 million m3 year−1 (in Qom province) to 1337 ± 556 million m3 year−1 (in Fars province)  to 931 ± 321 million m3 year−1 (in Fars province) . This result is caused by the fact that potato, as the only crop in root and tuber category, has the lowest blue WF, and so does the lowest unsustainable and inefficient fractions.Table blue and IE.WFblue). For the current condition, overconsumption of blue water occurs during the April–November period at the cost of violating EFRs (BWS > 1), and possibly, using the fossil water resources. In April, moderate to significant BWS occurs in roughly half of the country, while the next half absents from BWS. In May, most of the provinces experience a sever BWS. During the July–September period, sever BWS occurs for almost all provinces since the growing periods of many crops fall within this period in the study area. However, BWS in Autumn (October and November) is low and occurs only in a few provinces.Figure Reduced blue water consumption under the DR.MU.DI.YGC scenario will change the status of BWS in many provinces and months. Unless for a few provinces, the overall blue water consumption remains below blue water availability (BWS < 1) during the October-December period, and in April. In May, the status of BWS will change from ‘sever’ to ‘moderate’/ ‘significant’ in ten provinces, and will change to ‘not-scarce’ in four provinces. However, the status of BWS remains unchanged in the majority of the provinces over the period June–September.While the DR.MU.DI.YGC scenario can alleviate BWS to some extent, it is still insufficient to address the problem of blue water overconsumption in the agricultural sector. Despite of the promising outcomes, there will be many provinces and months with BWS > 1; hence, other measures should be explored in addition to those introduced in this research to solve the problem.−1 due to yield gap closure . Hence, national GDP will increase by 54%, from 21 billion US$ year−1 to 32.4 billion US$ year−1, and provincial GDP will increase by 380 ± 415 million US$ year−1 (Table −1 to 21.1 thousand people days−1.The socioeconomic consequences of implementing the best water saving scenario among 15 defined ones (the DR.MU.DI.YGC scenario) are estimated and results are summarized in Table In this study, we tried to map out the WF scheme of the agricultural sector in Iran, and propose pathways for alleviating blue water scarcity in the country. This study includes various limitations and uncertainties. First, the AquaCrop model was used to estimated ET and yield while using the default parameters in some cases. When data are available, calibrating and validating the model against field conditions will provide more accurate results. Second, the use of data with different spatial resolutions may cause some inconsistencies in the given results. In addition, the results may be sensitive to different environmental conditions; for instance, the results may change when hydrologically differentiating between the dry and wet periods  has the largest water saving potential. In this scenario, YGC has the largest effect on reducing unsustainable and inefficient blue WFs. Earlier researchers also indicated the key role of yield improvement in reducing crop’s WFs  in a large part of the country over the period April–November. Hence sustaining agriculture in this country requires substantial adjustments to current technologies and agricultural practices and water management. In this regard, blue water saving possibilities were assessed through defining 15 alternative management scenarios compared to the base case by considering the individual or combined effects of four major measures: yield gap closure, drip irrigation, soil mulching, and deficit irrigation. Results show that all scenarios have positive impacts on reducing unsustainability and inefficiency in blue water consumption nationwide, while combining all four mentioned measures, with a potential blue water saving of 19 million m


Papillomaviruses are a diverse viral species, but several types such as HPV16 are given special attention due to their contribution towards the pathogenesis of several major cancers. In this review, we will summarize how the knowledge of HPV16 entry has expanded since the last comprehensive HPV16 entry review our lab published in 2017. In this review, we will focus on the infectious entry of human papillomavirus (HPV), especially HPV type 16 (HPV16), the most intensely studied member of this group of viruses. We published a comprehensive HPV entry review in 2017 [The HPV capsid is a 55–60 nm diameter T = 7 icosahedral capsid composed of 360 copies of the major capsid protein L1 that are assembled into 72 pentamers, also called “capsomeres” ,5,6,7,8.Our lab along with others in the HPV field utilize “pseudoviruses” (PsVs) to study HPV entry process, as PsVs are regarded as generally following the same entry pathway as “authentic” HPV particles . InitialHPV entry into cells requires capsid binding to extracellular matrix (ECM) as the ECM contains substrates for capsid binding and thus HPV infection. While PsVs can also directly bind to the cell surfaces of transformed cells, they preferentially bind to ECM components. ECM resident receptors identified for HPV16 include heparan sulfate proteoglycans (HSPGs)  and lamiNext, HPV virion transport along filopodia assists with virion transport to the cell body and its efficient uptake ,50. HPV During the last few years, several groups have studied HPV entry by observing the effect on HPV infection in cells over-expressing trans-membrane (TM) proteins or in cells expressing inducible small, artificial proteins. These proteins are comprised of primarily hydrophobic amino acids and are small, less than 25 residues in length. They are called “traptamers”, short for “transmembrane protein aptamers” . The useAfter endocytic internalization, the compartment containing the HPV capsid fuses with an early endosome (EE) as shown by L1 colocalization with the early endosome 1 (EEA) marker. Ras-related in brain (Rab) GTPase 5 (Rab5) is needed for HPV infection as it colocalized with HPV16; Rab5 is needed for function of EEs ,69. It iAt this stage, the late endosome containing the HPV virion must enter the retrograde pathway or it is degraded in the lysosome . L2 protAt this point in HPV entry, the available data support a model where the HPV genome is protected by an endosome-derived vesicular structure with L2 protein protruding from the vesicle and into the cytoplasm, where it can act as a substrate to facilitate intracellular trafficking using cellular factors ,97. An oThe L2 protein has been shown by pulldown experiments and infection experiments to interact directly with the VPS35 subunit of retromer complex , helpingThe L2 with its HPV-containing vesicle is thought to be sorted to the TGN in part due to endosomal tubulation. HPV infection was found to stimulate endosomal tubulation and if this is blocked via loss of the vesicle-associated membrane protein (VAMP)-associated protein, (VAP), the result is an infection block prior to HPV genome trafficking to the TGN ,106. VAPIt is now well-established that HPV utilizes mitosis as part of its lifecycle and that nuclear envelope breakdown is another rate-limiting step for HPV infection ,108,109.After the HPV genome-harboring vesicle with L2, along with the aforementioned small amount of L1 associated with the HPV genome as capsomers, enters the nucleus, the HPV genome-laden vesicle remains intact until after mitosis is complete and the nuclear envelope is reformed ,112,114.PML NBs targeted by HPV have been proposed to have a protective role during its early viral cycle stages ,116,117.PML NBs are subnuclear punctate ring-like structures that are comprised of a few permanent and a large number of transient proteins, making PML NB composition incredibly versatile and dependent on cellular states ,119. ThePML NBs become disorganized during mitosis, initiated by de-SUMOylation of PML protein ,130. PMLAfter completion of mitosis and reformation of the nuclear envelope, PML proteins are associating with HPV-harboring nuclear vesicles before the HPV genome egresses from its vesicular structure ,117. It Surprisingly, HPV genomes are lost after successful nuclear delivery if PML proteins are absent. This genome loss can be partially abated by chemically inhibiting the Jak/STAT pathway . It is tHPV16 L2 has a defined “chromosome binding region” (CBR) that was mapped to residues 188–334, and this CBR shares eight conserved residues between multiple HPV types, between residues 251–258, and more between residues 302–334 . MutatioThe main advances in the study of HPV entry from our last review include further elaboration on the secondary receptor utilized by HPV as an entry platform(s), further confirmation of the existence of the vesicular structure used by the HPV genome during intracellular trafficking and nuclear import that we initially proposed, the identification of gamma-secretase as a chaperone required for L2 to engage retromer, and further elaboration of proteins involved in post-TGN HPV genome trafficking steps. Additionally, the DiMaio lab has started to use the aforementioned traptamer proteins to study HPV entry. These traptamers could be used to study other viral entry pathways.There are at least two main avenues that warrant investigation about post-TGN HPV trafficking. First, further elaboration is needed on HPV trafficking from the TGN to the nucleus during mitosis. Lai et al. provide additional information on this that adds upon recent data from our lab and others ,84,112 wThe second main avenue to follow is to determine how the HPV genome egresses from its protective vesicular structure to associate with the condensed chromosomes. One candidate facilitating this genome egress are phospholipases, as several of these have been identified by siRNA screens as being required for HPV infection ,109. ThiFollowing up on HPV entry should continue to provide insights into how small tumor viruses evade the cellular immune response as well as identify new therapeutics and targets for HPV. There is an additional boon here with the use of traptamers for virology research and utilizing the CPP of L2 to deliver drugs or peptides into the cell.


To validate the reference ranges proposed by the manufacturer of the Oculus HMC Anomaloscope MR for Rayleigh and Moreland tests in healthy young adults.1/M1) and the upper (R2/M2) limits; the center (RC/MC) and the width (RW/MW) of the matching ranges.The manual Rayleigh (red-green) and the Moreland (blue-green) anomaloscope tests were performed on 90 healthy subjects  residing in Poland, aged between 18–45 years, and without color vision disorders (assessed with HRR test). The analyzed parameters for both the Rayleigh and the Moreland tests were as follows: the lower  was mainly due to the measurements in male subjects , which suggests that this group might be diagnosed with subtle protanomaly. The results of the Moreland test showed a high MW which did not correspond to the reference range described in the anomaloscope user’s manual. The observed significant correlations between R1 and M1 suggest that the M1 parameter seems to be the best indicator of blue vision quality.The results of the Rayleigh test were similar to the values proposed in the anomaloscope user’s manual, however, with a small shift of ROculus HMC Anomaloscope MR is a sensitive tool for detection of prot-deuteranomalies but the reference ranges for young adults require a certain adjustment towards the red color. The parameters obtained for the Moreland test varied significantly between the subjects and therefore the test should not be used as is to diagnose color vision deficits in the green-blue area . However, the first conclusive tests became available at the beginning of the 20th century. The tests available nowadays can be divided into screening . TheIshihara .Anomaloscopic measurement includes a comparison of the hues of two illuminated fields. For the red/green measurement, the reference field is illuminated with an adjustable proportion of red 666 nm) and green (549 nm) diodes which, depending on the proportion of red and green colored light, create the impression of green, lemon, yellow, orange or red color. The test field is illuminated with a yellow diode (589 nm) and the field’s brightness level can be adjusted by the test subject ; vs 560.4±0.3 nm [R(S180)]  . AssuminThe other parameter that could be related to the Rayleigh test parameters is the difference between the maximum absorption spectra for L and M cones since the spectra are similar and only about 30nm apart. Thus, even a tiny shift in the absorption spectra between L or M cones may contribute to a significant increase in distinguishing the hues and tones between red and green.W). Red-green deficits are much more common in the population, especially in males, as this congenital defect is related to the X chromosome responsible for coding the L/M cones [The results of this study confirmed that human eyes are highly sensitive to red-green colors  is a parameter that describes the relative ability to see blue and green colors. A high value of this parameter may be interpreted as a relatively lower sensitivity to green as compared to blue. On the contrary, lower values suggest a lower sensitivity to blue color. 1 parameter which varies broadly between 0 and 78. However, M2 varies to a significantly lower extent, ranging mainly between 60 and 100 for individual eyes. Also, only in one case the value of M2 was 40. The width of the color equalization range (Mw) showed a considerable variation in individual eyes in the range between 8 and 95. High MW value indicates a poor ability to distinguish between different shades and tones of green and blue. In the majority of the population, the parameter is usually higher than 60. Such individuals poorly distinguish the shades and tones of green and blue and perceive a wide range of green/blue diode proportions as the same color. When green light is added, only increased brightness is perceived but the perceived hue remained unchanged. However, certain individuals show a narrow range of equalization, which means that they are able to distinguish more shades of a color (e.g. see colors such as turquoise and aquamarine). The intersubject differences regarding green-blue area detection may result from different distributions of S-cones on their retinas. The S-cones are also less numerous than the L and M-cones and represent only 2% of the total number of cones in the central fovea [The data presented for the Moreland test indicates very high interpersonal differences in the results. The central point of the equalization range  may be too small in order to correctly measure blue color deficits.The design of the Moreland test is imperfect. The wavelength of the cyan diode (480nm) is very similar to the wavelength of the green diode (490nm). The difference may be too small to properly estimate blue-green color vision differences and may cause problems with color matching. In the opinion of the authors of this study, another problem seems to be the high intensity of the blue diode which dominates the color impression in the reference field.Further studies are necessary in order to make the test more reliable and sensitive, possibly using additional sensitivity tests for hue brightness perception. This issue falls into the scope of future research planned by the authors.1/RW and M1/MW/MC, respectively. R2 is also correlated with the Moreland parameters, however, the correlation is poorer than that between R1 and RW. Since MC and MW depend on M1 (as M2 is quite stable) and RW depends on R1, the relation between R1 and M1 seems to be the most promising one for further analysis.The correlations between the Rayleigh and the Moreland parameters are most significant between R1 is significantly correlated with the Moreland test parameters as it is highly related to green light sensitivity. Subjects with slightly poorer green detection (low R1) also showed a lower ability to distinguish the hues between blue and green . It should be pointed out that R1 increases together with the increment of M1 and such relation was most significant for high M1. High M1 group (M1≥40) exhibited good sensitivity to blue color as compared to green. This group also showed low MW, which indicates a good ability to distinguish between different hues and tones of green and blue. Also in the Rayleigh test, these subjects were able to see green well (high R1) and showed narrow RW, which denotes a good distinguishing of tones between green, yellow and red. It may be concluded that such individuals show a relatively better ability to see blue and green in comparison to red.R2 was negatively correlated with M1 and positively with MW. It means that good red color vision (low R2) to some extent improves the ability to distinguish the hues and tones in the green/blue axis (narrow MW). This observation may depend on the L cones which have a broad absorption spectrum extending into the range of blue wavelengths. Stimulation of the L cones with blue and green light may provide additional information and improve the distinction between hues of blue and green.It has also been found that R1 and R1 in subjects with M1≥40 may be due to high sensitivity to green. However, there is a large group of individuals who show a low M1 (M1<40) with highly varied R1 values. These subjects have a relatively poorer ability to see blue. It may be concluded that the poorer ability to see blue does not influence the results of the Rayleigh test.According to a rival theory of color vision process , the perThe last issue concerns the potential ability the blue diode, used in the Moreland test (430nm), to slightly stimulate the M and L cones. This ability may cause a moderation of the Moreland test results by stimulation of the red cones. The authors also consider this factor to be a probable cause of the specific pattern of the M1 vs R1 correlation presented and discussed in The authors have found differences in color perception between genders, however, the numbers of female and male participants were not equal. In future studies, it would be advisable to increase the number of males in order to investigate gender differences in color vision using the Moreland test.2 ≥ 50 group was the least numerous one, which could have influenced the statistics. It would be interesting to carry out tests in a larger group in order to find more subjects with high R2 and to try to correlate this parameter to certain genetic mutations. In future studies, it would also be interesting to check the differences between the eyes, especially in older individuals, as certain ocular pathologies might affect color vision sensitivity.Additionally, the R1 values below 40 and in a broad range between 0 and 40. Due to the above, the test can falsely indicate deficits in blue color vision. However, the test in its present form is able to distinguish between individuals whose ability to perceive the blue color is very good from those whose blue light perception is poor. From this perspective, the method should not be used as a diagnostic test. However, it may be applied to monitor the deterioration in the perception of blue in subjects with good baseline blue vision who suffer from illnesses that may potentially deteriorate this ability . However, further research is required in this area.The results of the present study show that the built-in Moreland test of the Oculus HMC Anomaloscope MR should not be used to diagnose blue color vision deficits. In the group of subjects who showed normal blue color vision in the HRR test, the majority had MS1 Appendix(DOCX)Click here for additional data file.S1 File(XLSX)Click here for additional data file.