Correction to: BMC Nephrol (2020) 21: 5https://doi.org/10.1186/s12882-019-1670-xFollowing publication of the original article , the autThe incorrect affiliation currently reads:2Urology research Center, Hassan Abad Sq., Sina Hospital, Tehran University of Medical Sciences, Tehran, IranThe correct affiliation should read:2Urology Research Center, Tehran University of Medical Sciences, Tehran, IranThe original article has been corrected.


Although patterns of biodiversity across the globe are well studied, there is still a controversial debate about the underlying mechanisms and their generality across biogeographic scales. In particular, it is unclear to what extent diversity patterns along environmental gradients are directly driven by abiotic factors, such as climate, or indirectly mediated through biotic factors, such as resource effects on consumers.Andes, Southern Ecuador; Mt. Kilimanjaro, Tanzania.We studied the diversity of fleshy‐fruited plants and avian frugivores at the taxonomic level, that is, species richness and abundance, as well as at the level of functional traits, that is, functional richness and functional dispersion. We compared two important biodiversity hotspots in mountain systems of the Neotropics and Afrotropics. We used field data of plant and bird communities, including trait measurements of 367 plant and bird species. Using structural equation modeling, we disentangled direct and indirect effects of climate and the diversity of plant communities on the diversity of bird communities.We found significant bottom‐up effects of fruit diversity on frugivore diversity at the taxonomic level. In contrast, climate was more important for patterns of functional diversity, with plant communities being mostly related to precipitation, and bird communities being most strongly related to temperature.Our results illustrate the general importance of bottom‐up mechanisms for the taxonomic diversity of consumers, suggesting the importance of active resource tracking. Our results also suggest that it might be difficult to identify signals of ecological fitting between functional plant and animal traits across biogeographic regions, since different species groups may respond to different climatic drivers. This decoupling between resource and consumer communities could increase under future climate change if plant and animal communities are consistently related to distinct climatic drivers. Although patterns of biodiversity across the globe are well studied, there is still a controversial debate about the underlying mechanisms and their generality across biogeographic scales. We found significant bottom‐up effects of fruit diversity on frugivore diversity at the taxonomic level. In contrast, climate was more important for patterns of functional diversity, with plant communities being mostly related to precipitation, and bird communities being most strongly related to temperature. We measured two female and two male specimens of each species. We measured bill length and bill width using a sliding caliper (±0.01 mm). We measured bill length as the distance from the commissural point of the upper and lower bill to the tip of the closed bill, and bill width as the external distance between the two commissural points, which is functionally equivalent to gape width . We used the “independent swap” algorithm  which are able to account for both direct and indirect relationships among variables in complex systems , we fitted a separate SEM. Thus, we considered only corresponding metrics of plant and bird communities in each model   and the comparative fit index (CFI)  and Cecropia (492 visits), and Miconia (32 visits) was equally among the most frequently visited genera at higher altitudes  was the most frequent bird species at lower elevations of the Ecuadorian Andes, while the Lacrimose Mountain‐Tanager  was most frequent at higher elevations (see Appendix Lannea (307 visits) and Ozoroa (91 visits) were frequently visited at lower elevations, while Schefflera  was most frequently visited by birds at high elevations  was the most frequent bird species at low elevations, while the Montane White‐Eye  was the most frequent visitor at high elevations of Mt. Kilimanjaro . Moreover, FD of plant and bird communities varied across both mountain ranges  and 2–53 (median = 23) species per plot in the Ecuadorian Andes and 5–15 (median = 7.5) and 7–23 (median = 12.5) species per plot on Mt. Kilimanjaro, respectively. Species turn‐over between plots was high and, on average, 90% of plant species in the Ecuadorian Andes and 88% on Mt. Kilimanjaro, and 80% of bird species in the Ecuadorian Andes and 83% on Mt. Kilimanjaro were replaced between plots  > 0.05; lower 90% of confidence intervals of RMSEA close to 0; CFI > 0.95). Results were consistent across biogeographic regions, with exception of plant functional dispersion . With increasing temperature, species richness in plant communities significantly increased, which in turn caused a significant increase in species richness of bird communities Figure . SimilarSES), but these links were not statistically significant  of bird communities increased significantly with mean annual temperature, while FRic of plant communities was strongly positively linked to mean annual precipitation Figure . For bot4We compared patterns of taxonomic and functional diversity of frugivorous birds across two major mountain systems of the Neotropics and Afrotropics and simultaneously disentangled direct and indirect effects of climate and resource diversity. The overall diversity of fleshy‐fruited plants and avian frugivores was higher in the Ecuadorian Andes than on Mt. Kilimanjaro, which is in line with other studies showing similar differences between the two biogeographic regions  showed signs of functional clustering, which is typically explained by environmental filtering , data curation (lead), formal analysis (lead), funding acquisition , investigation (lead), methodology (lead), project administration (lead), resources (lead), software (lead), supervision (lead), validation (lead), visualization (lead), writing – original draft (lead), writing – review and editing (lead). Jörg Albrecht: Conceptualization (supporting), data curation (supporting), formal analysis , funding acquisition (supporting), investigation (supporting), methodology , project administration (supporting), resources (supporting), software (supporting), supervision (supporting), validation , visualization , writing – original draft , writing – review and editing . Katrin Böhning‐Gaese: Conceptualization , data curation (supporting), formal analysis (supporting), funding acquisition (lead), investigation (supporting), methodology (supporting), project administration (lead), resources , software , supervision , validation (supporting), visualization (supporting), writing – original draft (supporting), writing – review and editing (supporting). Andreas Hemp: Conceptualization (supporting), data curation (supporting), formal analysis (supporting), funding acquisition (supporting), investigation (supporting), methodology (supporting), project administration (supporting), resources (supporting), software (supporting), supervision (supporting), validation (supporting), visualization (supporting), writing – original draft (supporting), writing – review and editing (supporting). Kim M. Howell: Conceptualization (supporting), data curation (supporting), formal analysis (supporting), funding acquisition (supporting), investigation (supporting), methodology (supporting), project administration (supporting), resources (supporting), software (supporting), supervision (supporting), validation (supporting), visualization (supporting), writing – original draft (supporting), writing – review and editing (supporting). Laura Kettering: Conceptualization (supporting), data curation (supporting), formal analysis (supporting), funding acquisition (supporting), investigation (supporting), methodology (supporting), project administration (lead), resources (supporting), software (supporting), supervision (supporting), validation (supporting), visualization (supporting), writing – original draft (supporting), writing – review and editing (supporting). Alexander Neu: Conceptualization (supporting), data curation (supporting), formal analysis (supporting), funding acquisition (supporting), investigation (supporting), methodology (supporting), project administration (supporting), resources (supporting), software (supporting), supervision (supporting), validation (supporting), visualization (supporting), writing – original draft (supporting), writing – review and editing (supporting). Eike Lena Neuschulz: Conceptualization , data curation , formal analysis , funding acquisition , investigation , methodology , project administration , resources , software , supervision , validation , visualization , writing – original draft , writing – review and editing . Marta Quitián: Conceptualization (supporting), data curation (supporting), formal analysis (supporting), funding acquisition (supporting), investigation (supporting), methodology (supporting), project administration (supporting), resources (supporting), software (supporting), supervision (supporting), validation (supporting), visualization (supporting), writing – original draft (supporting), writing – review and editing (supporting). Vinicio E. Santillán: Conceptualization (supporting), data curation (supporting), formal analysis (supporting), funding acquisition (supporting), investigation (supporting), methodology (supporting), project administration (supporting), resources (supporting), software (supporting), supervision (supporting), validation (supporting), visualization (supporting), writing – original draft (supporting), writing – review and editing (supporting). Till Töpfer: Conceptualization (supporting), data curation (supporting), formal analysis (supporting), funding acquisition (supporting), investigation (supporting), methodology (supporting), project administration (supporting), resources (supporting), software (supporting), supervision (supporting), validation (supporting), visualization (supporting), writing – original draft (supporting), writing – review and editing (supporting). Matthias Schleuning: Conceptualization (lead), data curation , formal analysis , funding acquisition , investigation , methodology , project administration , resources , software , supervision , validation , visualization , writing – original draft , writing – review and editing . Susanne A. Fritz: Conceptualization (lead), data curation (lead), formal analysis (lead), funding acquisition (lead), investigation (lead), methodology (lead), project administration (lead), resources (lead), software (lead), supervision (lead), validation (lead), visualization (lead), writing – original draft (lead), writing – review and editing (lead).Figure S1aClick here for additional data file.Figure S1bClick here for additional data file.Figure S2Click here for additional data file.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.


Corrigendum to: A high-protein total diet replacement increases energy expenditure and leads to negative fat balance in healthy, normal-weight adults. Am J Clin Nutr 2021;113:476–487.In the above article, Table 1 has been updated as follows online:Previous versionCorrected version


The full-genome sequences of strains chicken/Indonesia/Cilebut/010WJ/2015 and chicken/Indonesia/ITA/012WJ/1951, isolated in West Java, Indonesia, in 2015 and 1951, respectively, were examined. Chicken/Indonesia/Cilebut/010WJ/2015 (genotype VII) caused a 2015 disease outbreak in Indonesia, and chicken/Indonesia/ITA/012WJ/1951 (genotype VI) is used as a standard strain for challenge in Newcastle disease virus (NDV) vaccine trials. The full-genome sequences of strains chicken/Indonesia/Cilebut/010WJ/2015 and chicken/Indonesia/ITA/012WJ/1951, isolated in West Java, Indonesia, in 2015 and 1951, respectively, were examined. Chicken/Indonesia/Cilebut/010WJ/2015 (genotype VII) caused a 2015 disease outbreak in Indonesia, and chicken/Indonesia/ITA/012WJ/1951 (genotype VI) is used as a standard strain for challenge in Newcastle disease virus (NDV) vaccine trials. Avian orthoavulavirus 1, the type virus of the Orthoavulavirus genus in the Paramyxoviridae family  outbreaks have caused high mortality in commercial chickens, even when vaccinated with attenuated Newcastle disease virus (NDV) . Formalle family . Genotype family , 4, but e family . Chickene family . Strain e family . Strain Full-genome sequences of strains Cilebut and ITA were analyzed. Both strains were propagated in the allantoic cavities of 9-day-old specific-pathogen-free (SPF) chicken eggs, fluid was collected by day 4, and infections were confirmed using a hemagglutination test . Viral RAY562988.1). These contigs were also aligned to strain Fontana using minimap2 V2.17 . Annotation was performed as described above. Sequence alignment and phylogenetic analysis of strain Cilebut revealed that it belongs to genotype VII, with the fusion protein cleavage site motif 112R-R-Q-K-R-F117.The raw sequence data were assembled using Unicycler V0.4.8 with default parameters . The assGenotype VII NDVs are predominantly responsible for ND outbreaks in Indonesia; however, genotype II is used in most Indonesian vaccines, and the standard challenge strain used in vaccine efficacy trials is from genotype VI. Antigenic disparities between the vaccine, challenge, and field strains of NDV may enable disease outbreaks in vaccinated chickens.MN727299 and SRR11593163, respectively, and those for chicken/Indonesia/ITA/012WJ/1951 are MN727300 and SRR11593165, respectively.The GenBank and Sequence Read Archive (SRA) accession numbers for chicken/Indonesia/Cilebut/010WJ/2015 are


The correct name is: Mayfong Mayxay. The correct citation is: Wootton CI, Sodaly MK, Billamay SX, English JSC, Mayxay M (2020) Patch test results in paediatric patients with atopic dermatitis in Laos. PLoS ONE 15(4): e0231455.


A high cognitive load situation (HCLS) is completing two or more tasks simultaneously . Differential allocation of attentional demands creates HCLS, potentially deteriorating cognitive and/or gait performance, impacting fall risk. This study investigated whether different load types  impacted gait and cognitive performance among young , middle-aged , and older  adults. In 3-minute trials, participants completed single-task walking (ST-W) and phone conversations with easy  and difficult  topics, and also combined walking and talking (easy: HCLS-E and difficult: HCLS-D). For gait, speed, step length (SL) and stride width (SW) were analyzed with 3 x 3(Age) repeated-measures ANOVAs. HCLS resulted in slower speed , and wider SW (p=.008) across groups. Older adults exhibited shorter SL across walking conditions (p=.002) compared to young and middle-aged. For cognition, Word Count (WC) and Authenticity (i.e. honesty) were analyzed with 2(Evs.D) x 2(STvs.HCLS) x 3(Age) repeated-measures ANOVAs. Main effects emerged for conversation topic in WC (p=.04) and Authenticity (p<.001); difficult topics negatively impacted participants’ cognitive performance, likely resulting from higher attention to maintain conversations without personal interactions . Marginal age-group differences (p=.056) revealed older age resulted in less authentic conversations. The HCLS in this study negatively impacted gait and cognitive performance. Understanding this relationship may ultimately inform development of interventions to improve allocation of attentional demands, potentially mitigating fall risk.


Scientific Reportshttps://doi.org/10.1038/s41598-020-67230-z, published online 25 June 2020Correction to: This Article contains typographical errors in the Acknowledgements section.“The funding from National Natural Science Foundation of China is acknowledged, grant numbers: 31971136, U1530402, U1430237.”should read:“The funding from National Natural Science Foundation of China is acknowledged, grant numbers: 31971136, U1930402, U1430237.”


The publisher apologizes for the error. The correct citation is: Nutor JJ, Duodu PA, Agbadi P, Duah HO, Oladimeji KE, Gondwe KW (2020) Predictors of high HIV+ prevalence in Mozambique: A complex samples logistic regression modeling and spatial mapping approaches. PLoS ONE 15(6): e0234034.


Targeting mitochondrial oxidative stress with MitoQ reduces NET formation and kidney disease in lupus-prone MRL-lpr mice. Lupus Sci Med 2020;7:e000387.Fortner KA, Blanco LP, Buskiewicz I, The published version misspelled co-author’s name as Andreas Perl. The correct name should be Andras Perl.


Blood pressure monitoring in high-risk pregnancy to improve the detection and monitoring of hypertension : protocol for two linked randomised controlled trials. BMJ Open 2020;10:e034593. doi: 10.1136/bmjopen-2019-034593Dougall G, Franssen M, Tucker KL, The article has been corrected since it was published online. The affiliation of Lucy Mackillop has been updated both in author byline and Acknowledgement section.


Scientific Reports 10.1038/s41598-019-49997-y, published online 23 September 2019Correction to: This Article contains errors.Reference 17 was incorrectly given as:Verma, M., Ghangal, R., Sharma, R., Sinha, A. K. & Jain, M. Transcriptome analysis of Catharanthus roseus for gene discovery and expression profiling. PLoS One. https://doi.org/10.1371/journal.pone.0103583 (2014).The correct reference is listed below as ref. Additionally, a citation to reference 17 should be included in the Results and Discussion, where the sentence,“Assembly of the available transcriptomes of P. solenopsis discovered the presence of 55198 transcripts in second instar (SRR6782025), 55569 transcripts in adult (SRR6782023), 49193 transcripts in third instar (SRR6782022) and 64497 in the eggs (SRR6782024)”should read:17”“Assembly of the available transcriptomes of P. solenopsis discovered the presence of 55198 transcripts in second instar (SRR6782025), 55569 transcripts in adult (SRR6782023), 49193 transcripts in third instar (SRR6782022) and 64497 in the eggs (SRR6782024)


By Wenyuanyue Wang, ZiAn He, Nannan Feng, Yuyang Cai Tobacco Induced Diseases, Volume 17, Issue April, Pages 1-11 Publish date: 16 April 2019DOI:https://doi.org/10.18332/tid/105393In the original article, the acronym ITC was mistakenly defined as the International Trade Center. The correct definition for the acronym ITC is International Tobacco Control. The correction is shown below :However, the International Tobacco Control (ITC) China survey revealed that the percentage of smokers who had heard of e-cigarette rose from 29%  to 60% .


Anoplophora Hope, 1839 is a genus including more than 40 species occurring in Asia. Most species of this genus have beautiful colours on the elytra and are of great interest to insect collectors. Due to their developing in and consuming wood in the larval stage, species in this genus could be economically important, such as A.glabripennis, an introduced species to North America.Anoplophorafanjingensis sp. n., based on specimens from Mount Fanjing , Jiankou County, Guizhou, China. The new species is characterised by its elytra with metallic iridescent sheen and the non-annulated antennae. Habitus of two similar species, Anoplophorachiangi and Anoplophoraleechi, are also presented.We described  Anoplophora Hope, 1839 is a genus of the tribe Lamiini  that encompasses more than 40 species occurring in Asia  and one female, of the new species were collected by light traps in Huixiangping, Jiangkou County, Guizhou Province, China. Morphological characters were examined using an AmScope SM-4TZ stereomicroscope. Habitus pictures were taken with a Canon EOS 6D digital camera fitted with a Carl Zeiss Milvus 100 mm lenses. Male genitalia were photographed with an Olympus SZX7 stereomicroscope using an Olympus DP22 camera. Type materials were deposited in the School of Life Sciences, Guizhou Normal University, Guiyang, China (GZNULS).The data underpinning the analysis reported in this paper are deposited at GBIF, the Global Biodiversity Information Facility, http://ipt.pensoft.net/resource.do?r=xxxxxx.Yang, Yang and Tian, 2020sp. n.831F60F7-D217-515B-9BFB-7FA3D5F33C0FF5ADCAD9-3DA0-4DB5-BA64-970355D694E6Type status:Holotype. Occurrence: recordedBy: Boyang Li; individualCount: 1; sex: male; Location: country: China; stateProvince: Guizhou; county: Jiangkou; locality: Huixiangping, Mount Fanjing; verbatimElevation: ca. 1700 m; verbatimLatitude: 27°54.18’ N; verbatimLongitude: 108°42.40’ E; Event: year: 2017; month: 7; day: 6-14Type status:Paratype. Occurrence: recordedBy: Boyan Li; individualCount: 1; sex: female; Location: country: China; stateProvince: Guizhou; county: Jiangkou; locality: Huixiangping, Mount Fanjing; verbatimElevation: ca. 1700 m; verbatimLatitude: 27°54.18’ N; verbatimLongitude: 108°42.40’ E; Event: year: 2016; month: 6; day: 20Type status:Paratype. Occurrence: recordedBy: Boyan Li; individualCount: 1; sex: male; Location: country: China; stateProvince: Guizhou; county: Jiangkou; locality: Huixiangping, Mount Fanjing; verbatimElevation: ca. 1700 m; verbatimLatitude: 27°54.18’ N; verbatimLongitude: 108°42.40’ E; Event: year: 2016; month: 6; day: 25Male: black, body length, male . Hairs on the antennae and the dorsal tarsomeres 1—3 of female are bluish, while pale in male. Metallic iridescent sheen on the elytra of female is blue to green, while purple to green in male. Anterior projection of the mesosternal intercoxal process is weakly developed in female, but not developed in male. Middle notch at apex of terminal ventrite of female is deeper than that of male.Anoplophorafanjingensis sp. n., can be distinguished from most of its congeners by the non-annulated antennae, the pronotum with only one indistinct posterior callus and the metallic iridescent sheen on elytra. Anoplophorachiangi Hua & Zhang, 1991  . However, A.albopicta was described from Taiwan and has not been recorded in mainland China . Although the type of A.tonkinea is not available for examination, it was designated as incertae sedis by A.tonkinea has multiple calli on the pronotum.nd China . Based oThe specific name refers to the collecting location, Mount Fanjing.


Correction to: BMC Med Inform Decis Mak (2020) 20:177https://doi.org/10.1186/s12911-020-01199-7Following publication of the original article , the autThe incorrect names were: Fung Jinchao, Kaleem Arshad, and Girridhar Reddy BojaThe correct author names are: Feng Jinchao, Kaleem Arshid, and Giridhar Reddy BojaThe author group has been updated above and the original article  has been


Subgroup analysis was conducted to evaluate the influence of study design. The Grading of Recommendations Assessment, Development, and Evaluation approach was used to determine the level of evidence. In total, 2544 records were identified through database searches; 914 duplicate records were excluded, 1452 records were removed after screening of titles and abstracts, 151 records were excluded after full-text screening, and 27 articles were included in the final meta-analyses. A total of 240,706,026 patients  were considered. No significant difference in sleep duration between the allergic rhinitis and the control groups was found. Patients with allergic rhinitis presented with significantly higher sleep quality scores, sleep disturbances scores, and sleep latency scores; more frequent use of sleep medications; and lower sleep efficiency as measured by the Pittsburgh Sleep Quality Index and polysomnography. Meta-analyses for adjusted odds ratios showed that allergic rhinitis was also associated with higher risks of nocturnal dysfunctions, including insomnia, nocturnal enuresis, restless sleep, sleep-disordered breathing, obstructive sleep apnea, and snoring. Meta-analysis for adjusted odds ratio also showed that allergic rhinitis was associated with daytime dysfunction, including difficulty waking up, daytime sleepiness, morning headache, and the use of sleep medications. The overall quality of evidence ranged from low to very low, indicating that caution is required when interpreting these results. This study demonstrates that there is a significant association of AR with sleep characteristics.This systematic review and meta-analysis examines the associations of allergic rhinitis with sleep duration and sleep impairment. Observational studies published before August 2019 were obtained through English language literature searches in the PubMed, Embase, and CINAHL databases. Mean differences and odds ratios with 95% confidence intervals were extracted and used for meta-analysis. Heterogeneity was confirmed by the I Allergic rhinitis (AR) is a common inflammatory disorder generally caused by an immunoglobulin (Ig) E-mediated response to a variety of environmental allergens, including cockroach frass, animal dander, pollens, dust mites, and molds . It is cSleep is crucial for human mood, memory, endocrine and immune system functions, and cognition –14. PoorThere is a growing body of population-based research on the association between AR and sleep patterns. AR has been found to be positively associated with sleep- disordered breathing (SDB) , obstrucThus, the present study aims to assess the association of AR with sleep pattern by conducting a systematic review and meta-analysis of published observational studies.This systematic review and meta-analysis was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement .Cross-sectional, case-control, and cohort studies examining associations between AR and sleep published before August 2019 were searched in the following databases: PubMed, Embase, and CINAHL. Combinations of sleep-related search terms  and AR-related search terms  were employed when screening titles/abstracts/keywords of articles. The full electronic search strategy can be found in the The following inclusion criteria were applied to the literature search process: (1) cross-sectional, case-control, or cohort studies published in refereed English language journals; (2) the study exposure of interest was AR; (3) for studies comparing differences in sleep  between AR and a control group, the outcomes were reported as mean + standard deviation (SD), mean + confident interval (CI), mean + standard error (SE), median + interquartile (IQ), median + range, or number of cases with adverse events; (4) for studies evaluating the effect of AR on sleep-related outcomes, estimation of an odds ratio (OR), risk ratio (RR), or hazard ratio (HR) with 95% CI was provided; and (5) study control group was a population without AR.Data extraction was performed using a standard data extraction form by one investigator (J.L.) and reviewed by another investigator (Y.W.). Disagreements were resolved through negotiations with a third investigator (Y.Z.) until a consensus was reached. The following information was extracted from included studies: title, last name of the first author, country, number and age of subjects, exposure and outcome, measures of exposure and research endpoints, mean + SD/CI/SE, median + IQ/range, number of cases, and HR/OR/RR with 95% CI.The quality of studies included in this review were assessed by the Newcastle-Ottawa Scale (NOS) . A studyet al  with high heterogeneity  in patients with AR. The AR group presented with a higher, though not significant, sum ESS score [MD with 95% CI = 1.53 ]. Five articles reporting PSG outcomes showed lower scores of sleep efficiency  in the AR group  scores in patients with AR. Compared with the control group, the AR group presented with higher sum PSQI scores [MD with 95% CI = 0.68 ], higher sleep disturbances scores [MD with 95% CI = 0.20 ], and higher sleep latency scores ). The IAR group .2 = 28.50%], restless sleep , SDB , OSA , and snoring . AR was also associated with a higher risk of daytime sleep-related dysfunctions, including difficulty waking up [OR with 95% CI = 2.58 ], daytime sleepiness , morning headache [OR with 95% CI = 6.16 ], and the use of sleeping pills [OR with 95% CI = 1.69 ].We conducted subgroup analysis stratified by study design for sleep duration, apnea-hypopnea index (AHI), sleep efficiency measured by PSG, the percent of sleep stage REM measured by PSG, OSA, and snoring. Significant differences in results from different study designs were observed for AHI, sleep efficiency measured by PSG, and the percent of sleep stage REM measured by PSG, indicating the potential influence of study design on pooled results. Detailed information is shown in the supplemental material –S11 FigsMost of the included studies were of high quality according to the NOS criteria. Detailed information is shown in et al [By omitting one study each time using a random effects model, sensitivity analysis was conducted for meta-analyses of sleep duration, ESS score, AHI assessed by PSG, sleep efficiency assessed by PSG, the percentage of sleep stage REM assessed by PSG, nocturnal enuresis, restless sleep, OSA, snoring, and daytime sleepiness. Pooled results of sleep duration and AHI assessed by PSG were significantly sensitive to the study of Zheng et al , becauseThe overall quality of evidence using GRADE’s Summary of Findings table was judged to be low or very low. Risk of bias, inconsistencies , indirectness, and imprecision were found due to lack of adjusting confounding factors, incorporation of different age groups and study designs, self-reported AR, and sleep outcomes. The Summary of Findings table can be found in In the present review, no significant differences in sleep duration between the AR and control groups were observed. AR patients presented with increased sleep quality scores, sleep disturbance scores, and sleep latency scores on the PSQI scale and a decreased sleep efficiency score using PSG. AR was also found to be associated with a higher risk of nocturnal sleep-related dysfunctions, including insomnia, nocturnal enuresis, restless sleep, SDB, OSA, and snoring. Additionally, AR was found to be associated with a higher risk of daytime sleep-related dysfunctions, including difficulty waking up, daytime sleepiness, morning headache, and the use of sleeping pills.The main underlying mechanisms for the association between AR and altered sleep patterns can be summarized as follows: (1) inflammatory cytokines related to AR produce fatigue directly; (2) AR symptoms and underlying pathophysiologic changes affect sleep indirectly; and (3) the effect of autonomic system dysfunction in patients with AR.Inflammatory mediators, including histamine, are released in AR and have a direct influence on the central nervous system, contributing to sleep disturbances and daytime sleepiness , 42. HisSymptoms of AR include nasal congestion, rhinorrhea, sneezing, and pruritus. Among these symptoms, nasal obstruction is the most troublesome one for patients –47. NasaImbalance of the autonomic system in AR is also thought to be involved to the association between AR and sleep impairment . As one To the best of our knowledge, this is the first systematic review and meta-analysis evaluating the association between AR and sleep based on observational investigations. A 2018 systematic review focused on the association between AR and OSA and applied a meta-analysis of population-based studies; however, this article concentrated mainly on the prevalence of AR in subjects with or without OSA/SDB . Anotheret al., which was conducted in patients with OSA. We included a limited number of studies focusing on OSA patients and failed to detect a difference between the normal population and patients with OSA. More investigations conducted in patients with OSA are needed. Third, some outcomes of sleep impairment are confirmed by self-reported questionnaires, which are vulnerable to recall bias and potentially affected by social norms. Further longitudinal research with objective measurements are warranted.There are some limitations to the current study. First, most of the included studies are cross-sectional or case-controlled, which do not allow for inferences of causal relationships. Second, some results from this study’s meta-analyses display sensitivity to a single study included in the analysis, potentially due to the limited number of included studies. In terms of the sensitivity analysis of sleep duration and AHI assessed by PSG, the inverse MD is caused by the study of Zheng There is a significant association of AR with sleep characteristics; however, due to the very low GRADE level of evidence, caution is required when interpreting our results.S1 FigCI: confidence interval; MD: mean difference; SD: standard deviation.(TIF)Click here for additional data file.S2 FigCI: confidence interval; MD: mean difference; PSQI: Pittsburgh Sleep Quality Index; SD: standard deviation.(TIF)Click here for additional data file.S3 FigCI: confidence interval; ESS: Epworth Sleepiness Scale; MD: mean difference; SD: standard deviation.(TIF)Click here for additional data file.S4 FigAHI: apnea-hypopnea index; CI: confidence interval; ESS: Epworth Sleepiness Scale; MD: mean difference; PSG: polysomnography; REM: rapid eye movement; SD: standard deviation.(TIF)Click here for additional data file.S5 FigAR: allergic rhinitis; CI: confidence interval; OR: odds ratio; OSA: obstructive sleep apnea; SDB: sleep-disordered breathing.(TIF)Click here for additional data file.S6 FigCI: confidence interval; MD: mean difference; SD: standard deviation.(TIF)Click here for additional data file.S7 FigAHI: apnea-hypopnea index; CI: confidence interval; MD: mean difference; SD: standard deviation.(TIF)Click here for additional data file.S8 FigAHI: apnea-hypopnea index; CI: confidence interval; MD: mean difference; PSG: polysomnography; SD: standard deviation.(TIF)Click here for additional data file.S9 FigCI: confidence interval; MD: mean difference; REM: rapid eye movement; SD: standard deviation.(TIF)Click here for additional data file.S10 FigCI: confidence interval; OR: odds ratio; OSA: obstructive sleep apnea.(TIF)Click here for additional data file.S11 FigCI: confidence interval; OR: odds ratio.(TIF)Click here for additional data file.S12 FigCI: confidence interval; MD: mean difference.(TIFF)Click here for additional data file.S13 FigCI: confidence interval; ESS: Epworth Sleepiness Scale; MD: mean difference.(TIFF)Click here for additional data file.S14 FigAHI: apnea-hypopnea index; CI: confidence interval; MD: mean difference; PSG: polysomnography.(TIFF)Click here for additional data file.S15 FigCI: confidence interval; MD: mean difference; PSG: polysomnography.(TIFF)Click here for additional data file.S16 FigCI: confidence interval; MD: mean difference; PSG: polysomnography; REM: rapid eye movement.(TIFF)Click here for additional data file.S17 FigCI: confidence interval; OR: odds ratio.(TIFF)Click here for additional data file.S18 FigCI: confidence interval; OR: odds ratio.(TIFF)Click here for additional data file.S19 FigCI: confidence interval; OR: odds ratio; OSA: obstructive sleep apnea.(TIFF)Click here for additional data file.S20 FigCI: confidence interval; OR: odds ratio.(TIFF)Click here for additional data file.S21 FigCI: confidence interval; OR: odds ratio.(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 TableAHI: apnea-hypopnea index; CI: confidence interval; ESS: Epworth Sleepiness Scale; MD: mean difference; REM: rapid eye movement; PSQI: Pittsburgh Sleep Quality Index; PSG: polysomnography.(DOCX)Click here for additional data file.S6 TableCI: confidence interval; MD: mean difference; OR: odds ratio; OSA: obstructive sleep apnea; SD: standard deviation; SDB: sleep-disordered breathing.(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S1 File(TXT)Click here for additional data file.


Open Biol.10, 200029 (Published Online 2 September 2020) (doi:10.1098/rsob.200029)This correction refers to an error in the affiliation of author Paromita Das. The affiliation should read as follows - Academy of Scientific and Innovative Research (AcSIR), Ghaziabad- 201002, India.


In this study, we investigated whether pulmonary function tests such as forced oscillation technique parameters could predict perioperative respiratory complications. In the results of our study, perioperative respiratory complications cannot be predicted using the results of preoperative pulmonary function tests and forced oscillation technique parameters. Patients who are judged by comprehensive preoperative judgment to be suitable for general anesthesia may not need to consider the risk of perioperative complications using pulmonary function test. Dear Editor,1). MostGraph  is a device that measures the resistance and reactance of the respiratory system using the FOT and was developed in Japan . Previoawa, 2011). Airwayawa, 2011). th June, 2014). Written informed consent was obtained from all subjects. This study was performed according to the principles outlined in the Declaration of Helsinki. Fifty-five patients underwent cardiovascular surgery, and 48 patients underwent lung or mediastinal surgery. There were 25 cases of perioperative respiratory complications . No deaths due to perioperative respiratory complications were observed. There were no significant differences in FVC or FEV1 between groups with or without perioperative respiratory complications. There was also no significant difference between groups in FOT parameters  for the English language review.Akira Igarashi: Data curation, Original draft preparation, Sumito Inoue: Conceptualization, Study design, Yoko Shibata: Conceptualization, Writing-Reviewing, Keiko Nunomiya: Administration of data base, Takahito Ota, Yu Ishibashi, Hiroaki Murano, Kodai Furuyama, Sujeong Yang, Hiroyoshi Machida: Data analysis. Hiroshi Nakano, Kento Sato, Masamichi Sato, Takako Nemoto, Michiko Nishiwaki, Keiko Yamauchi, Jun Suzuki: Data collection, Explanation and receipt of informed consent from patients, Mitsuaki Sadahiro, Masafumi Watanabe: Reviewing and Editing, All authors: Data interpretation, Final approval of the manuscript.This work was supported by JSPS KAKENHI Grant Number JP26861222.AI received lecture fees from AstraZeneca K.K., Boehringer Ingelheim Japan.SI received lecture fees from Otsuka Pharmaceutical Co., Ltd, Novartis Pharma K.K., AstraZeneca K.K., GlaxoSmithKline K.K., Chugai Pharmaceutical Co., Ltd., Boehringer Ingelheim Japan, MSD K.K., ONO PHARMACEUTICAL CO., LTD., Meiji Seika Pharma Co., Ltd., KYORIN Pharmaceutical Co.,Ltd., Taiho Pharmaceutical Co.,Ltd., Eli Lilly Japan K.K., NIHON PHARMACEUTICAL CO., LTD., TEIJIN PHARMA LIMITED., Pfizer Japan Inc., DAIICHI SANKYO COMPANY, LIMITED, and received research grant from Chugai Pharmaceutical Co., Ltd., Novartis Pharma K.K., MSD K.K., KYORIN Pharmaceutical Co.,Ltd., and JSPS KAKENHI (grant number 19K08658).YS received lecture fees from Boehringer Ingelheim Japan, AstraZeneca K.K., Novartis Pharma K.K., KYORIN Pharmaceutical Co.,Ltd., Chugai Pharmaceutical Co., Ltd., Pfizer Japan Inc., GlaxoSmithKline K.K., CHEST, received advisory fee from GlaxoSmithKline K.K., and received research grant from Boehringer Ingelheim Japan, AstraZeneca K.K., Novartis Pharma K.K., KYORIN Pharmaceutical Co.,Ltd., Chugai Pharmaceutical Co., Ltd., and JSPS KAKENHI (grant number 19K08658). TO received lecture fee from MSD K.K.KF received lecture fee from Boehringer Ingelheim Japan.SY received lecture fees from Boehringer Ingelheim Japan, AstraZeneca K.K. HMa received lecture fees from Boehringer Ingelheim Japan.HN received lecture fees from Pfizer Japan Inc., Boehringer Ingelheim Japan, AstraZeneca K.K. KS received lecture fee from Chugai Pharmaceutical Co., Ltd., GlaxoSmithKline K.K., AstraZeneca K.K., Sanofi K. K. and research grant from GlaxoSmithKline K.K.MS received lecture fees from AstraZeneca K.K., Boehringer Ingelheim Japan. TN received lecture fee from Teijin Pharma Ltd Japan. KY received lecture fee from Pfizer Japan Inc. MW received research grants from MSD K.K., Asahi Kasei Pharma Corporation., Asahi Kasei Medical Co., Ltd., KISSEI PHARMACEUTICAL CO., LTD., Kyowa Kirin Co.,Ltd., Sanofi K.K., SHIONOGI & CO., LTD., Taisho Pharma Co., Ltd., CHUGAI PHARMACEUTICAL CO., LTD., TEIJIN PHARMA LIMITED., Torii Pharmaceutical Co., Ltd., Nihon Medi-Physics Co.,Ltd., Novartis Pharma K.K., BIOTRONIK Japan, Inc., FUJIFILM Toyama Chemical Co., Ltd., Roche Diagnostics K.K., Kowa Company, Limited, Abbott Vascular Japan Co., Ltd., Cardinal Health Japan, Kowa Company, Limited, research grants and personal fees from Actelion Pharmaceuticals Japan Ltd., Astellas Pharma Inc., Otsuka Pharmaceutical Co., Ltd., ONO PHARMACEUTICAL CO., LTD., DAIICHI SANKYO COMPANY, LIMITED, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited, Nippon Boehringer Ingelheim Co ., Ltd., MEDTRONIC JAPAN CO., LTD., Bayer Yakuhin, Ltd., Pfizer Japan Inc., MOCHIDA PHARMACEUTICAL CO.,LTD., personal fees from Mitsubishi Tanabe Pharma Corporation, KOWA PHARMACEUTICAL COMPANY LTD., TOA EIYO LTD., Bristol-Myers Squibb K.K., AstraZeneca K.K., Edwards Lifesciences Corporation, Amgen. Inc., Japan Lifeline Co., Ltd..KN, YI, HMu, MN, JS, and MS have no COI.


Journal of Experimental Botany, Advance Access publication: 20 May 2020, doi: 10.1093/jxb/eraa245An equation in the above article has been corrected as below.Incorrect equation:Corrected equation:


The correct name is: Aparajita Chattopadhyay. The correct citation is: Saha UR, Chattopadhyay A, Richardus JH (2019) Trends, prevalence and determinants of childhood chronic undernutrition in regional divisions of Bangladesh: Evidence from demographic health surveys, 2011 and 2014. PLoS ONE 14(8): e0220062.


Journal of the Brazilian Society of Tropical MedicineRevista da Sociedade Brasileira de Medicina Tropical/Title:Anopheles deaneorum: a new potential malaria vector in State of Santa Catarina, Brazil (Diptera: Culicidae) Vol.:46(1): 2013 - doi: https://doi.org/10.1590/0037-8682201200342013 - AUTHORPage 121Vinicios Ferreira de FreitasShould read:Lílian Ferreira de Freitas


Inadequate weather conditions are one of the main threats to the correct development of sensitive crops, where a bad situation can lead to greater stress on plants and their weakness against various diseases. This statement is especially decisive in the cultivation of the vineyard. Meteorological monitoring of vineyard parcels is vital to detect and prevent possible fungal diseases. The development of new Information and Communication Technologies, linked to the Smart Farming movement, together with the reduced cost of electronic components, have favoured a greater availability of meteorological monitoring stations to get to know first-class hand the state of the vineyard smallholdings. This work provides a set of over 750,000 environmental raw data records collected by low-cost Internet of Things nodes, primarily located within vineyard smallholdings. The published observations were collected between 2018-04-01 and 2018-10-31 and were validated in previous research to determine the data's reliability. Also, the low-cost sensors can be compared to other low-cost sensors, and a qualitative and performance comparative can be carried out. Data has been kept raw (including outliers), thus allowing sensor reliability to be verified.•Another value of this data set is in the development of energy strategies. The battery levels of IoT nodes are also provided; these values can be used to study how meteorological variables affect the discharge of the battery and its charge using a solar panel.1SEnviroSEnviro nodes feature different improvements, such as the feasibility of performing remote updates managing Over-the-Air (OTA) updates; a greater autonomy supporting 3G connectivity, and solar panel plus applied energy policies; and replicability because it is made up of open hardware and other elements such as 3D-printed pieces SEnviro node collects environmental sensor measurements, such as temperature, air humidity, soil moisture, atmospheric pressure, rain, and wind speed/direction phenomena. Battery level values are also recorded. Seven SEnviro nodes were deployed in outdoor environments; four of them were located within vineyard smallholdings. The following tables  and Si7021 (relative humidity and temperature) chips. Moreover, it contains two RJ11 connectors to attach anemometer and rain gauge sensors.•Solar panel and lithium-ion battery. A waterproof solar panel is included to offer an autonomous energy supply. This panel charges the battery and provides a continuous IoT node operation. A lithium-ion battery is included; it holds a 2000 mA capacity and provides an output voltage of 3.7 V.•Soil moisture. This element measures the moisture of the soil. It has two pads, and they act as a variable resistor; it gets better conductivity when more water therein in the soil. To obtain the final output (percentage) a re-map function is needed from 0–4096 to 0–100.•Weather meters. An anemometer and rain gauge compose it. They measure the speed and direction of wind and precipitation. Both sensors are connected using RJ11 connectors.Next list summarizes each component used to build a node:•Temperature. Manufacturer: SparkFun; Model: Si7021; Data Interface: Analog; Units: Centigrade; Range: ; Accuracy: ±0.4 degrees (C)•Humidity. Manufacturer: SparkFun; Model: Si7021; Data Interface: Analog; Units: Percentage; Range: ; Accuracy: ±3 RH•Barometric pressure. Manufacturer: SparkFun; Model: MPL3115A2; Data Interface: I2C; Units: Hectopascal; Range: ; Accuracy: ±0.04 hPa•Soil moisture. Manufacturer: SparkFun; Model: DS18B20; Data Interface: Analog; Units: Percentage; Range: ; Accuracy: ±0.5 RH•Wind speed. Manufacturer: SparkFun; Model: SEN08942; Data Interface: Analog (RJ11); Units: km/h; Range: N/A; Accuracy: N/A•Wind direction. Manufacturer: SparkFun; Model: SEN08942; Data Interface: Analog (RJ11); Units: Direction (degrees); Range: (0) North, (1) NE, (2) East, (3) SE, (4) South, (5) SW, (6) West, (7) NW and (-1) error; Accuracy: N/A•Rain meter. Manufacturer: SparkFun; Model: SEN08942; Data Interface: Analog (RJ11); Units: millilitres (mm); Range: (0) North, (1) NE, (2) East, (3) SE, (4) South, (5) SW, (6) West, (7) NW and (-1) error; Accuracy: N/A•Battery. Manufacturer: N/A; Model: N/A; Data Interface: N/A; Units: Percentage; Range: ; Accuracy: N/AFollowing each phenomena collected is described providing units, range and accuracy.2.2SEnviro node have been deployed; four nodes have been placed in vineyard smallholdings in the province of Castelló : 40.133098, −0.061000; Area (Metres2): 20,000; Grape Variety: Monastrell.•SEnviro Id.: 380033001951343334363036; Location : 40.206870, 0.015536; Area (Metres2): 18,000; Grape Variety: Cabernet, Syrah, Merlot and Chardonnay.•SEnviro Id.: 4e0031000251353337353037; Location : 40.141384, −0.026397; Area (Metres2): 15,000; Grape Variety: Bonicaire.•SEnviro Id.:46005a000351353337353037; Location : 40.167529, −0.097165; Area (Metres2): 20,000; Grape Variety: Merlot, Tempranillo, Cabernet sauvignon, Syrah and others.Seven units of the Castelló  and 2. A200034001951343334363036, 46004e000251353337353037 and 4e0031000251353337353037. The location of these nodes was the Universitat Jaume I .The other three nodes were deployed in outdoor environments for testing proposes. These testing IoT nodes correspond to identifiers article sketches (code)PSEnviro source code developed to collect and send measurements to a main server using 3G connectivity is available in the following repository The Sergio Trilles: Conceptualization, Methodology, Writing - original draft, Project administration, Funding acquisition, Writing - original draft. Alberto González-Pérez: Software, Data curation, Writing - review & editing. Benito Zaragozí: Formal analysis, Validation, Writing - original draft. Joaquín Huerta: Supervision, Writing - review & editing.The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


The publisher apologizes for the error. The correct name is: Frederik Buchvald. The correct citation is: Garioud ALdB, Skoven FH, Gregersen R, Lange T, Buchvald F, Greisen G (2020) The increased susceptibility to airway infections after preterm birth does not persist into adolescence. PLoS ONE 15(9): e0238382.


Correction to: Reprod Health (2020) 17:59https://doi.org/10.1186/s12978-020-0900-9Following publication of the original article , we haveThe following authors have to be added:Moazzam AliMercedes BonetNathalie BroutetEdna KaraCaron KimAnna ThorsonSoe Soe ThwinAffiliation of the above-mentioned authors is:UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), Geneva, Switzerland


Clubionamilingae Barrion-Dupo, Barrion & Heong, 2013 was described from a single male and no additional specimens have been recorded. The original description was brief and the illustrations were inadequate.Clubionamilingae is redescribed and illustrated based on new material from the type locality and the new distribution region . The female is reported for the first time. Clubionaapiculata species group, ﬁrst deﬁned by Clubiona sensu lato. The group presents a distinct set of characters and has been considered as putatively monophyletic  due to their characteristic genital organs. Additionally, we found some characters were overlooked in the original description of the male. The aim of the current paper is to redescribe the male and report the female for the first time, providing detailed morphological descriptions and illustrations.lication . RecentlSpiders were fixed and preserved in 80% ethanol. Specimens were examined with an Olympus SZX7 stereomicroscope; details were studied with an Olympus CX41 compound microscope. Female epigynes and male palps were examined and illustrated after dissection. Epigynes were removed and cleared in warm lactic acid before illustration. The image of the vulva was made after being embedded in Arabic gum. Photos were made with a Cannon EOS70D digital camera mounted on an Olympus CX41 compound microscope. The digital images were taken and assembled using the Helicon focus 6.80 software package.All measurements were obtained using an Olympus SZX7 stereomicroscope and are given in millimetres. Eye diameters were measured at the widest point. The total body length does not include the chelicerae or spinnerets. Leg lengths are given as total length . The terminology used in the text and figure legends follows All specimens are deposited in the Museum of Guizhou Education University, Guiyang, Guizhou, China .Barrion-Dupo, Barrion & Heong, 2013F96C37DF-8B6C-5E0D-B74A-3B13EF724B95Type status:Other material. Occurrence: recordedBy: Qianyu Wan; individualCount: 3; sex: 2 females and 1 male; lifeStage: adult; behavior: foraging; occurrenceStatus: present; preparations: whole animal (ETOH); Taxon: scientificName: Clubionamilingae; acceptedNameUsage: Clubionamilingae Barrion-Dupo, Barrion & Heong, 2013; order: Araneae; family: Clubionidae; genus: Clubiona; specificEpithet: milingae; taxonRank: species; taxonomicStatus: accepted; Location: continent: Asian; island: Hainan; country: China; countryCode: CHN; stateProvince: Hainan; county: Ledong; verbatimElevation: 900-1000 m; verbatimCoordinateSystem: decimal degrees; decimalLatitude: 18.726088; decimalLongitude: 108.902750; Identification: identifiedBy: Hao Yu; dateIdentified: 11-2018; identificationReferences: Barrion et al. 2013; Event: samplingProtocol: Beating; eventDate: 10-4-2018; year: 2018; month: 4; day: 10; habitat: Rubber-tea plantationType status:Other material. Occurrence: recordedBy: Jie Liu; Haiqing Ren; individualCount: 3; sex: 2 females and 1 male; lifeStage: adult; behavior: foraging; occurrenceStatus: present; preparations: whole animal (ETOH); Taxon: scientificName: Clubionamilingae; acceptedNameUsage: Clubionamilingae Barrion-Dupo, Barrion & Heong, 2013; order: Araneae; family: Clubionidae; genus: Clubiona; specificEpithet: milingae; taxonRank: species; taxonomicStatus: accepted; Location: continent: Asian; island: Hainan; country: China; countryCode: CHN; stateProvince: Hainan; county: Qiongzhong; verbatimElevation: 600-700 m; verbatimCoordinateSystem: decimal degrees; decimalLatitude: 19.206780; decimalLongitude: 109.768095; Identification: identifiedBy: Hao Yu; dateIdentified: 11-2018; identificationReferences: Barrion et al. 2013; Event: samplingProtocol: Pitfall trap; eventDate: 18-8-2009; year: 2009; month: 8; day: 18; habitat: Rubber-tea plantationFemale  slightly recurved, posterior eye row (PER) procurved, PER wider than AER. Eye sizes and inter-distances (mm): anterior median eyes (AME) 0.06, anterior lateral eyes (ALE) 0.08, posterior median eyes (PME) 0.09, posterior lateral eyes (PLE) 0.07; distance between AMEs (AME–AME) 0.02, distance between AME and ALE (AME–ALE) 0.04, distance between PMEs (PME–PME) 0.19, distance between PME and PLE (PME–PLE) 0.10. Length of median ocular quadrangle (MOQ) 0.20, MOQ anterior width 0.24, MOQ posterior width 0.35. Chelicerae coloured as carapace, with 5 teeth on promargin and 3 on retromargin. Labium and endites light brown. Sternum 0.86 long, 0.52 wide.Abdomen elongate-oval, white, with inconspicuous anterior tufts of hairs, dorsum with a pair of inconspicuous muscular depressions; venter white.Legs uniformly yellowish-white. Leg lengths: I 2.10 , II 2.55 , III 1.91 , IV 3.11 .Epigyne , but differing from C.yaoi by the copulatory openings situated at the medial portion of the epigynal plate posterior margin . Males also resemble those of C.yaoi in having a retrolateral tibial apophysis with a bifurcate tip , but can be recognised by the relatively long embolus and by the absence of a conductor . In addition, the two species can by separated by their habitus: abdomen marked with numerous inconspicuous spots in C.milingae (Fig. C.yaoi.Females of gin Fig. A, B, D (tor Fig. A–D (vs. gae Fig. A, D, butKnown from Mt. Diaoluo and Mt. Limu, Hainan Island, China Fig. .Most of the new material was collected by pitfall-traps set in a rubber-tea plantation.Clubionamilingae was first reported by apiculata species group for four Borneo species. Maybe, due to the brief description, the inadequate illustrations, and the lack of female individuals, Clubionamilingae has not been assigned so far. In the present study, pairs of specimens were obtained from the type locality of C.milingae. The small body and widely separated PME, the presence of the apophysis on the dorsal cymbium and the strongly-developed retrolateral tibial apophysis in the male with the copulatory ducts connected to bursae at the central portion of the epigyne in the female, all indicate that this species should belong to the apiculata-group.C.milingae, our new topotypes bear a striking similarity to the original illustrations by Although we have not examined the type specimen of


MPEG1) is highly expressed in macrophages and other phagocytes. The gene encodes a bactericidal pore-forming protein, dubbed Perforin-2. Structural-, animal-, and cell-based studies have established that perforin-2 facilitates the destruction of phagocytosed microbes upon its activation within acidic phagosomes. Relative to wild-type controls, Mpeg1 knockout mice suffer significantly higher mortality rates when challenged with gram-negative or -positive pathogens. Only four variants of MPEG1 have been functionally characterized, each in association with pulmonary infections. Here we report a new MPEG1 non-sense variant in a patient with the a newly described association with persistent polymicrobial infections of the skin and soft tissue.Macrophage expressed gene 1  by significantly increasing the expression of MPEG1. IFN-γ treatment supported perforin-2 dependent bactericidal activity and wound healing.A young adult female patient was evaluated for recurrent abscesses and cellulitis of the breast and demonstrated a heterozygous, rare variant in MPEG1 deficiency to include severe skin and soft tissue infection. We showed that haploinsufficiency of perforin-2 reduced the bactericidal capacity of human phagocytes. Interferon-gamma therapy increases expression of perforin-2, which may compensate for such variants. Thus, treatment with IFN-γ could help prevent infections.This case expands the phenotype of  MPEG1 expression is constitutive in phagocytes and inducible in parenchymal cell lines by interferons or bacterial infection .Until now, Enterobacter cloacae), and infections of the central line site (Staphylococcus aureus). Because of a concern for a primary immunodeficiency, she had been treated empirically with immune globulin, without evidence of hypogammaglobulinemia or specific antibody deficiency. At age 22, she presented with a breast abscess and cellulitis requiring incision and drainage (I&D). Over the next several months, she suffered recurrent abscesses despite serial debridement and intravenous antibiotics. During a prolonged inpatient stay for treatment of recurrent breast abscess at her local hospital, she was transferred to our institution for further evaluation and treatment. Her initial diagnostic work-up included laboratory studies that demonstrated a normal leukocyte count, mild anemia, overall normal lymphocyte counts, and a mild thrombocytosis, likely reactive to an acute infection . Despite completing an extended course of appropriate antimicrobial coverage along with multiple I&D procedures, the infections did not resolve.The patient reported is a 23-year-old adopted woman with a limited history of early childhood. She has a complex medical history including bilateral sensorineural hearing loss and non-anatomic gastroparesis requiring feeds by jejunal tube. She also carries the heterozygous, pathogenic Factor V “Leiden” variant discovered after suffering multiple deep vein thromboses and pulmonary emboli, now on lifelong anticoagulation. The patient experienced numerous infections throughout adolescence and young adulthood including repeated facial impetigo, recurrent tonsillitis requiring tonsillectomy and adenoidectomy, skin abscesses, recurrent central line-associated blood stream infections  , this variant was felt unlikely to be the cause of her immunodeficiency.A polymicrobial breast infection in a young woman persisting over several months despite treatment with multiple antimicrobials was suspicious for primary immunodeficiency. Flow cytometric analysis of peripheral blood lymphocytes showed mild NK cell lymphopenia, which was normal in prior and subsequent tests. Whole genome sequencing revealed a few, rare genetic variants including a nonsense variant in  dbSNP rs73347395 MPEG1 variant was suspected to be pathogenic  or Yersiniapseudotuberculosis. The phagocytes were then treated with membrane impermeable gentamicin to eliminate the extracellular population of bacteria prior to enumeration of intracellular bacteria. This assay, commonly referred to as a gentamicin protection assay, revealed that the patient’s neutrophils were unable to suppress the intracellular replication of S. Typhimurium  family, which includes the terminal components of complement (C9) responsible for pore-forming membrane disruption to eradicate microbial invaders ; the multiplicity of infection (MOI) was 50. Extracellular bacteria were eliminated with 50 μg/ml gentamicin. PMNs were lysed with 0.1% Triton X-100 at the specified times. Released bacteria were serially diluted for enumeration of colony forming units (CFUs) on LB agar plates supplemented with 50 μg/ml of the appropriate antibiotic.For neutrophil killing, polymorphonuclear neutrophils (PMNs) were isolated from whole blood by Ficoll-Paque centrifugation then seeded onto tissue culture plates for 2 h. Adherent cells were subsequently infected for 45 min with For macrophage killing, PBMCs isolated from whole blood by Ficoll-Paque centrifugation were seeded onto tissue culture treated 100 mm petri plates in IMDM + 10% FBS media supplemented with 50 ng/ml rh-MCSF (BioLegend cat #570206). Monocytes were allowed to adhere and differentiate over 7 days with media changes every 2 days. Following differentiation macrophages were infected with the indicated pathogens; MOI 50. Gentamicin protection assays were conducted as above.6 differentiated patient macrophages were stimulated for 14 h with 100 ng/ml of human recombinant IFN-γ and subsequently lysed in RLT buffer + 1% BME. Whole culture RNA was extracted with RNeasy columns and converted to cDNA using QuantiTec Reverse Transcription Kit. Samples were run in triplicate using TaqMan probes specific for human MPEG1. Results were normalized to expression of GAPDH and presented relative to untreated macrophages. P value as determined by unpaired t test.1x10Data for The studies involving human participants were reviewed and approved by UCLA IRB. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.Maria T. Acosta; Margaret Adam; David R. Adams; Pankaj B. Agrawal; Mercedes E. Alejandro; Justin Alvey; Laura Amendola; Ashley Andrews; Euan A. Ashley; Mahshid S. Azamian; Carlos A. Bacino; Guney Bademci; Eva Baker; Ashok Balasubramanyam; Dustin Baldridge; Jim Bale; Michael Bamshad; Deborah Barbouth; Pinar Bayrak-Toydemir; Anita Beck; Alan H. Beggs; Edward Behrens; Gill Bejerano; Jimmy Bennet; Beverly Berg-Rood; Jonathan A. Bernstein; Gerard T. Berry; Anna Bican; Stephanie Bivona; Elizabeth Blue; John Bohnsack; Carsten Bonnenmann; Devon Bonner; Lorenzo Botto; Brenna Boyd; Lauren C. Briere; Elly Brokamp; Gabrielle Brown; Elizabeth A. Burke; Lindsay C. Burrage; Manish J. Butte; Peter Byers; William E. Byrd; John Carey; Olveen Carrasquillo; Ta Chen Peter Chang; Sirisak Chanprasert; Hsiao-Tuan Chao; Gary D. Clark; Terra R. Coakley; Laurel A. Cobban; Joy D. Cogan; Matthew Coggins; F. Sessions Cole; Heather A. Colley; Cynthia M. Cooper; Heidi Cope; William J. Craigen; Andrew B. Crouse; Michael Cunningham; Precilla D'Souza; Hongzheng Dai; Surendra Dasari; Joie Davis; Jyoti G. Dayal; Matthew Deardorff; Esteban C. Dell'Angelica; Shweta U. Dhar; Katrina Dipple; Daniel Doherty; Naghmeh Dorrani; Argenia L. Doss; Emilie D. Douine; David D. Draper; Laura Duncan; Dawn Earl; David J. Eckstein; Lisa T. Emrick; Christine M. Eng; Cecilia Esteves; Marni Falk; Liliana Fernandez; Carlos Ferreira; Elizabeth L. Fieg; Laurie C. Findley; Paul G. Fisher; Brent L. Fogel; Irman Forghani; Laure Fresard; William A. Gahl; Ian Glass; Bernadette Gochuico; Rena A. Godfrey; Katie Golden-Grant; Alica M. Goldman; Madison P. Goldrich; David B. Goldstein; Alana Grajewski; Catherine A. Groden; Irma Gutierrez; Sihoun Hahn; Rizwan Hamid; Neil A. Hanchard; Kelly Hassey; Nichole Hayes; Frances High; Anne Hing; Fuki M. Hisama; Ingrid A. Holm; Jason Hom; Martha Horike-Pyne; Alden Huang; Yong Huang; Laryssa Huryn; Rosario Isasi; Fariha Jamal; Gail P. Jarvik; Jeffrey Jarvik; Suman Jayadev; Lefkothea Karaviti; Emily G. Kelley; Jennifer Kennedy; Dana Kiley; Isaac S. Kohane; Jennefer N. Kohler; Deborah Krakow; Donna M. Krasnewich; Elijah Kravets; Susan Korrick; Mary Koziura; Joel B. Krier; Seema R. Lalani; Byron Lam; Christina Lam; Grace L. LaMoure; Brendan C. Lanpher; Ian R. Lanza; Lea Latham; Kimberly LeBlanc; Brendan H. Lee; Hane Lee; Roy Levitt; Richard A. Lewis; Sharyn A. Lincoln; Pengfei Liu; Xue; Zhong Liu; Nicola Longo; Sandra K. Loo; Joseph Loscalzo; Richard L. Maas; John MacDowall; Ellen F. Macnamara; Calum A. MacRae; Valerie V. Maduro; Marta M. Majcherska; Bryan C. Mak; May Christine V. Malicdan; Laura A. Mamounas; Teri A. Manolio; Rong Mao; Kenneth Maravilla; Thomas C. Markello; Ronit Marom; Gabor Marth; Beth A. Martin; Martin G. MartinJulian A. Martínez-Agosto; Shruti Marwaha; Jacob McCauley; Allyn McConkie-Rosell; Colleen E. McCormack; Alexa T. McCray; Elisabeth McGee; Heather Mefford; J. Lawrence Merritt; Matthew Might; Ghayda Mirzaa; Eva Morava; Paolo M. Moretti; Deborah Mosbrook-Davis; John J. Mulvihill; David R. Murdock; Anna Nagy; Mariko Nakano-Okuno; Avi Nath; Stan F. Nelson; John H. Newman; Sarah K. Nicholas; Deborah Nickerson; Shirley Nieves-Rodriguez; Donna Novacic; Devin Oglesbee; James P. Orengo; Laura Pace; Stephen Pak; J. Carl Pallais; Christina GS. Palmer; Jeanette C. Papp; Neil H. Parker; John A. Phillips III; Jennifer E. Posey; Lorraine Potocki; Bradley Power; Barbara N. Pusey; Aaron Quinlan; Wendy Raskind; Archana N. Raja; Deepak A. Rao; Genecee Renteria; Chloe M. Reuter; Lynette Rives; Amy K. Robertson; Lance H. Rodan; Jill A. Rosenfeld; Natalie Rosenwasser; Francis Rossignol; Maura Ruzhnikov; Ralph Sacco; Jacinda B. Sampson; Susan L. Samson; Mario Saporta; C. Ron Scott; Judy Schaechter; Timothy Schedl; Kelly Schoch; Daryl A. Scott; Vandana Shashi; Jimann Shin; Rebecca Signer; Edwin K. Silverman; Janet S. Sinsheimer; Kathy Sisco; Edward C. Smith; Kevin S. Smith; Emily Solem; Lilianna Solnica-Krezel; Ben Solomon; Rebecca C. Spillmann; Joan M. Stoler; Jennifer A. Sullivan; Kathleen Sullivan; Angela Sun; Shirley Sutton; David A. Sweetser; Virginia Sybert; Holly K. Tabor; Queenie K.-G. Tan; Mustafa Tekin; Fred Telischi; Willa Thorson; Audrey Thurm; Cynthia J. Tifft; Camilo Toro; Alyssa A. Tran; Brianna M. Tucker; Tiina K. Urv; Adeline Vanderver; Matt Velinder; Dave Viskochil; Tiphanie P. Vogel; Colleen E. Wahl; Stephanie Wallace; Nicole M. Walley; Chris A. Walsh; Melissa Walker; Jennifer Wambach; Jijun Wan; Lee-kai Wang; Michael F. Wangler; Patricia A. Ward; Daniel Wegner; Mark Wener; Tara Wenger; Katherine Wesseling Perry; Monte Westerfield; Matthew T. Wheeler; Jordan Whitlock; Lynne A. Wolfe; Jeremy D. Woods; Shinya Yamamoto; John Yang; Muhammad Yousef; Diane B. Zastrow; Wadih Zein; Chunli Zhao; Stephan Zuchner.MJB, GPM, and LCM developed the approach for cell based killing assays and MPEG1 quantification. LCM conducted the experiments, analyzed and visualized data. KKP, JLB, and MJB provided clinical care. HL and SFN performed sequencing data analysis. SYJ organized data and wrote the first draft. All authors participated in manuscript editing. All authors have read and approved the submitted version.Perforin-2 research in the laboratory of GPM is supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number R01AI110810. MJB is supported by National Institutes of Health under R01 GM110482 and by the Jeffrey Modell Foundation. Sequencing and data analysis were supported by awards from the National Institutes of Health (NIH) Common Fund, U01HG007703 and the UCLA California Center for Rare Diseases of the Institute for Precision Health.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


No: 621 Gaziosmanpaşa, Istanbul, Turkey; 2 Department of Urology, Istanbul Gaziosmanpasa Taksim Training and Research Hospital, Karayolları Str. No: 621 Gaziosmanpaşa, Istanbul, Turkey; 3 Department of Urology, Yeni Yüzyıl University, Istanbul, TurkeyWorld J Urol. 2019; 9. [Epub ahead of print]DOI: 10.1007/s00345-019-02904-6 | ACCESS: 10.1007/s00345-019-02904-6In this interesting paper Dr. Arslan from Turkey showed the increasing use of online resources to seek visual information for both patients and health care providers. The YouTube has become the second largest search engine with more than one billion users and six billion hours of video watched for each month . The aut


Flabelligenahakuhoaesp. nov., is described from off the South Orkney Islands, the Southern Ocean. Individuals of the new species were collected by rock dredging, 2036–2479 m in depth.A new acrocirrid species, The new species can be distinguished from its congeners by the number of branchiae, position and length of paired ventral large papillae and length of body papillae. Acrocirridae Banse, 1969 (clade Cirratuliformia) consists of 43 species in nine genera distributed from the intertidal zone to the deep seafloor  notochaetae, 1–3 pairs of branchiae and a pair of frontal palps  and F.erratica  from off Falkland Islands (South Atlantic Ocean)  , there aHakuho maru, the first author found individuals of Flabelligena from off the South Orkney Islands. In this paper, we describe the specimens as a new species.During the research cruise KH19-06-Leg4 by R/V 60°33.54'S, 35°24.43'W–60°34.07'S, 35°23.40'W), 2036 – 2479 m in depth by a rock dredge  for SEM observations. Observations were conducted using an SEM instrument (HITACHI S-3000N). Type material is deposited in the National Museum of Nature and Science, Tsukuba (NSMT) and Invertebrate Collection of the Hokkaido University Museum (ICHUM).Specimens were collected from off the South Orkney Islands Fig. A, the Sodge Fig. B and extWe followed the morphological terminology of Jimisp. n.9297C098-C02D-5779-918F-9DA3AD471734urn:lsid:zoobank.org:act:9E6C4840-0D03-4A24-B825-AC4EADD0CC8BType status:Holotype. Taxon: phylum: Annelida; family: Acrocirridae; genus: Flabelligena; Location: higherGeography: Southern Ocean; off the South Orkney Islands; waterBody: Southern Ocean; locality: off the South Orkney Islands; verbatimDepth: 2036-2479 m; Identification: identifiedBy: Naoto Jimi; Event: samplingProtocol: rock dredge; eventDate: 02-01-2020; year: 2020; month: 1; day: 2; habitat: rocks and sands; Record Level: language: en; ownerInstitutionCode: NSMTType status:Paratype. Taxon: phylum: Annelida; family: Acrocirridae; genus: Flabelligena; Location: higherGeography: Southern Ocean; off the South Orkney Islands; waterBody: Southern Ocean; locality: off the South Orkney Islands; verbatimDepth: 2036-2479 m; Identification: identifiedBy: Naoto Jimi; Event: samplingProtocol: rock dredge; eventDate: 02-01-2020; year: 2020; month: 1; day: 2; habitat: rocks and sands; Record Level: language: en; ownerInstitutionCode: ICHUMHolotype (NSMT-Pol H-813) 1.8 cm long, 1 mm wide  for 27 chaetigers (incomplete). Body cylindrical  1.2 cm in length, 0.9 mm in width , 23 chaetigers. Pygidium rounded , 2036 – 2479 m in depth.The new species is only known from the type locality, off the South Orkney Islands, the Southern Ocean  and F.gascognensis Aguirrezabalaga and Ceberio, 2006 have paired ventral papillae between chaetigers 6–7, but do not have them at the ventral side of a parapodium in chaetiger 6 (F.gascognensis has long (as long as neurochaetae) papillae around the parapodium, while the new species does not have them (1/6 of neurochaetae). Two species, F.amoureuxi Gillet, 2001 and F.erratica, have been found in the vicinity of the Southern Ocean as well as this new one. The new species can be distinguished from the other two species by having three pairs of branchiae, paired ventral large papillae near the parapodium of chaetiger 6 and absence of eyes. Flabelligenaamoureuxi has a pair of branchiae and does not have paired ventral large papillae and eyes. Flabelligenaerratica has a pair of branchiae, paired ventral large papillae between chaetigers 6–7 and eyes (see Table ochaetae . This spochaetae : i) pairetiger 6 . Additio


The correct initials are: Bertoldi AD. The correct citation is: da Silva Dal Pizzol T, Turmina Fontanella A, Cardoso Ferreira MB, Bertoldi AD, Boff Borges R, Serrate Mengue S (2019) Analgesic use among the Brazilian population: Results from the National Survey on Access, Use and Promotion of Rational Use of Medicines (PNAUM). PLoS ONE 14(3): e0214329.


The noise transmission coefficients and PA field distributions of the T-type resonant PA cell have been evaluated using the finite element method and thermoviscous acoustic theory. The optimized T-type resonant PA cell, together with a near-infrared (NIR) distributed feedback (DFB) laser source, a high-speed spectrometer and a fiber-optic acoustic sensor constitutes a PAS system for CH4 detection. The sensitivity is measured to be 1.8 pm/ppm and a minimum detectable limit (MDL) of 9 parts per billion (ppb) can be achieved with an averaging time of 500 s. The optimized T-type longitudinal resonant PA cell features of high PA cell constant, fast response time and simple manufacturing process.This paper presents an optimized T-type resonant photoacoustic (PA) cell for methane (CH Tianli Gao: Data curation, Software, Investigation. Liang Mei: Software. Ke Chen: Funding acquisition, Methodology, Formal analysis. Yewei Chen: Formal analysis. Bo Zhang: Resources. Wei Peng: Funding acquisition, Project administration. Qingxu Yu: Visualization.The authors declare that there are no conflicts of interest.


The correct title is: Mitochondrial event localiser (MEL) to quantitatively describe fission, fusion and depolarisation in three-dimensional space. The correct citation is: Theart RP, Kriel J, du Toit A, Loos B, Niesler TR (2020) Mitochondrial event localiser (MEL) to quantitatively describe fission, fusion and depolarisation in three-dimensional space. PLoS ONE 15(12): e0229634. The publisher apologizes for the error.


To identify the genomic regions for yield and NUE of rice genotypes and lines with promising yield under low N, a recombinant inbred population (RIL) developed between BPT5204  and PTB1 (variety with high NUE) was evaluated for consecutive wet and dry seasons under low nitrogen (LN) and recommended nitrogen (RN) field conditions. A set of 291 RILs were characterized for 24 traits related to leaf, agro-morphological, yield, N content and nitrogen use efficiency indices. More than 50 RILs were found promising with grain yield >10 g under LN. Parental polymorphism survey with 297 SSRs and selective genotyping revealed five genomic regions associated with yield under LN, which were further saturated with polymorphic SSRs. Thirteen promising SSRs were identified out of 144 marker trait associations under LN using single marker analysis. Composite interval mapping showed 37 QTL under LN with five pleiotropic QTL. A major stable pleiotropic (RM13201—RM13209) from PTB1 spanning 825.4 kb region associated with straw N % (SNP) in both treatments across seasons and yield and yield related traits in WS appears to be promising for the MAS. Another major QTL (RM13181-RM13201) was found to be associated with only relative trait parameters of biomass, grain and grain nitrogen. These two major pleiotropic QTL (RM13201-RM13209 and RM13181-RM13201) on chromosome 2 were characterized for their positive allele effect and could be deployed for the development of rice varieties with NUE. Rice is one of the main cereal crops and staple food for more than half of the world population. Despite the increase in world rice production since the past half century, rice yield improvement is still required to support the growing needs of the increasing world population. Nitrogen (N) is the key nutrient required in large quantities for rice production . Nearly Mapping of the genomic regions associated with nitrogen use efficiency (NUE) would be useful for marker assisted selection and introgression into popular rice varieties. Several QTL have been reported in rice for various agro- morphological and yield characters under low N . Genomicviz., BPT5204 and PTB1. The two parents and their RILs were evaluated under low N as well as recommended N for leaf, agro-morphological, yield and yield related traits, N content and N use efficiency indices,. Genomic regions/QTL for target traits and parameters of the study were identified through single marker analysis, selective genotyping and Composite Interval Mapping (CIM) method.With an objective of identifying QTL for yield, nitrogen and their related traits and promising lines under low N, a RIL population was developed from two rice varieties with significant differences for yield under low N viz., BPT5204 and PTB1 were selected (www.rkmp.co.in). A set of 54 genotypes was also grown under LN and RN during 2014 (WS) and 2015 (DS)  .In situ leaf chlorophyll content (SPAD) was recorded with Minolta Corporation’s Chlorophyll SPAD-502 plus, USA. Three observations from each leaf at three positions were taken in three replications and average was reported. Days to 50% flowering (DFF) of each RIL was noted. At physiological maturity, plant height (cm) (PH), number of tillers (TNO), panicles per plant (PNO), grain yield (g/plant) (GY), hundred seed weight (g) (HSW), grain number per plant (GRNO) and total dry matter (g/plant) (TDM) were recorded from five plants of parents and RILs. Nitrogen percentage in grain (GNP), straw (SNP), total grain nitrogen per plant (GNPP) and straw nitrogen per plant (SNPP) were estimated following Kjeldahl method and nitrogen harvest index (NHI) was calculated. Physiological nitrogen use efficiency (PNUE), agronomic nitrogen use efficiency (ANUE) and agro-physiological efficiency (APE) were determined according to Fageria et al. [viz., relative grain yield (RGY), relative biomass yield (RBM), relative grain nitrogen (RGN) and relative biomass nitrogen (RBN) were calculated according to Wei et al. [Leaf length (cm) (LL), leaf width (cm) (LW) and leaf area (cm) (LA) were measured according to Yoshida et al. . In situa et al. . Nitrogei et al. . Data fohttp://www.gramene.org). The details of SSRs and their status of polymorphism were given in DNA from the fresh leaf tissues of parents and RILs was isolated according to modified protocol of Zheng et al. . Parentaviz., RM1, RM7075, RM9, RM13021 and RM13197 were considered as anchor SSRs. Eight to 10 SSRs spanning each associated anchor SSR were selected from Gramene database for parental polymorphism and the nine polymorphic SSRs were screened in RILs.For amplification of the SSRs, PCR was carried out following the procedure described by Rao et al.  with 15μTwo way Analysis of variance (ANOVA) was performed for the phenotypic data using open software R language  with agrThe phenotypic data of 291 RILs and genotypic data of 41 32+9) polymorphic SSRs was subjected to SMA using ANOVA of open software R [+9 polymoThe linkage maps for chromosomes 1 and 2 spanning polymorphic SSRs in five anchor regions were constructed using Mapmaker and were imported into QTL Cartographer (Win QTL Cart 2.5). Composite interval mapping (CIM) was performed with kosambi function for 25 SSRs  and QTL were identified with minimum LOD score of 2.5 with 1000 permutations.i = phenotypic effect of the allele i = ∑xij/ni—∑Nk/nk; xij = phenotypic measurement values of j genotype carrying the allele i; ni = the number of genotypes carrying the allele of i, Nk = phenotypic value of the genotype of k carrying null allele, nk = the number of genotypes for the null allele [The average allelic effect (AAE) of two SSRs (RM13209 and RM13181) from two major QTL viz., RM13201-RM13209 and RM13181-RM13201 was estimated by comparing the mean phenotypic data for that trait with respect to each allele to the phenotypic data of the null allele . The positions of flanking SSRs of genomic regions associated with two or more than two QTL under LN were retrieved from Significant variation was observed between the parents (BPT5204 and PTB1) for 24 phenotype traits/parameters studied under LN . SubstanUnder LN, GY was found to be positively correlated with PH, TNO, PNO, GRNO, TDM and GNPP across seasons as expected. Association of GY with PH was observed exclusively under LN across seasons . Signifiviz., genomic region I—RM1, genomic region II—RM7075, genomic region III—RM9 and two SSRs on chromosome 2 (genomic region IV—RM13021 and genomic region V—RM13197)  out of 254 SSRs and 32 SSRs were selected for further analyses based on their resolution. Yield being identified as the important selection criterion under LN, selective genotyping of 22 RILs (12 RILs with highest grain yield and 10 RILs with the lowest yield under LN) was performed. Five SSRs have shown association with grain yield under LN using MapDisto . Among tRM13197) .viz., one at genomic region I, two at genomic region II, two at genomic region III, one at genomic region IV and three at genomic region V  across seasons under LN for 22 traits (marker associations were not found for SNP and RBN) . More nuCIM identified 37 QTL under LN  and 32 QFour QTL were identified for leaf length (LL) with two QTL under LN and two QTL under RN. QTL with marker interval of RM279—RM12568 was identified in both WS and DS under LN. A total of four QTL were detected for leaf width (LW) with two QTL each under LN and RN. Four QTL were identified only under RN for leaf area (LA) with two each in WS and DS. There were no QTL identified for LA under LN. Under LN, two QTL are identified for LA with one each in WS and DS.Four QTL were identified for grain yield (GY)/plant with two QTL each under LN and RN. Under LN, a major QTL with marker interval of RM13201—RM13209 (WS) (PV 43.3%) and a minor QTL (RM279—RM12568) during DS were identified. A total of 10 QTL were detected for hundred seed weight (HSW), with five each under LN and RN. Under LN, a common QTL was identified in WS and DS with marker interval of RM5641—RM10890. One QTL was detected for grain number (GRNO) under RN.A total of three QTL were detected for total dry matter (TDM)/plant under LN (1 QTL) and RN (2 QTL). A total of seven QTL were detected for plant height (PH) under LN (5 QTL) and RN (2 QTL) with two QTL detected in common across seasons under LN. One QTL was detected tiller number (TNO) under RN and QTL were not detected under LN.A total of 14 QTL were detected for days for 50% flowering (DFF) with eight QTL under LN and six QTL under RN.A total of seven QTL were detected for straw N % (SNP) with four QTL under LN and three QTL under RN. Under LN, one major QTL (PV >48%) was detected for SNP across seasons with marker interval of RM13201—RM13209. Two major QTL for relative grain N (RGN) and another two major QTL for relative biomass N (RBN) on chromosomes 1 and 2 were detected. One major QTL for relative biomass yield (RBM) shared its location with QTL for RGN and RBN.The identified QTL for the chromosomes 1 and 2 co-localized with positions of reported for similar trait QTL were presented trait wise in detail. The co-localization of associated SSRs with the reported QTL regions was also presented.viz., gw-1 and qTGW 1–1 [viz., grain weight, grain number per plant, spikelet number per plant and spikelets per panicle [With reference to chromosome 1, RM10090, RM1167 and RM5641 associated with HSW under LN, co-localized with genomic region for thousand grain weight under LN  and RN . InteresqTGW 1–1 , 39. Ano panicle , 43.qGN 2), hundred grain weight (qHW 2) and grain density (qGD 2) [o rachis/panicle and biomass/plant [viz., RM1189 associated with HSW under LN, coincided with genomic region for thousand grain weight [For chromosome 2, genomic region for HSW (RM13021-RM13034) in the present study corresponded with reported QTL for number of grains per panicle ( (qGD 2) . GRNO ma (qGD 2) , number  (qGD 2) –48. RM27 (qGD 2) . RM3763  (qGD 2) , 49. The (qGD 2) , yield p (qGD 2) , spikele (qGD 2) , 52, 53, (qGD 2) , spikele (qGD 2)  under RN (qGD 2) , 36. Two (qGD 2) , Fu et a (qGD 2) , Liu et  (qGD 2) . RM15855 (qGD 2) , 57. RM2 (qGD 2) , grain y (qGD 2)  under LN (qGD 2) , 59, 60, (qGD 2) , 62. RM2n weight  and grain weight . RM24829n weight , grain lThe locations of RM11307, RM6716, RM11258 (chromosome 1) associated with RGY in DS corroborated with genomic regions for relative grain weight reported by Ogawa et al. . Associaspp) responsible for chloroplast biogenesis at early growth stage and root development [The SSR markers on chromosomes 1 and 2 (RM5641 and RM13021) associated with LL, LA and LW coincided with reported QTL for leaf traits , 67–69. elopment . RM22524elopment . RM1189 elopment .qhd-6) identified by Bai et al. [qPHT 2–1) as reported by Marathi et al. [QTn1) and Kaladhar et al. [nt 1.2). RM7110 (chromosome 7) associated with PH and DFF under LN in DS has also co-localized with genomic regions for plant height and heading dates by Kotla et al. [Markers for DFF  on chromosome 1 were also found to be coinciding with QTL location for days to heading , 48. In i et al.  and 50% i et al. . In the i et al. . The SSRi et al.  (QTn1) ar et al.  (nt 1.2)a et al.  and Liu a et al. . Similara et al. , 71 and a et al. .On amplification of two SSRs (RM13201 and RM13181) from two major pleiotropic QTL (RM13201-RM13209 and RM13181-RM13201) in 54 genotypes, three alleles each were obtained per SSR. Two alleles (RM13029-255 bp and RM13181-270bp) same size as PTB1 have shown positive effect with grain yield (g) .viz., RM13201-RM13209 region spanning 825.4 kb region has 54 putative candidate genes; RM13181-RM13201 covering 467.706 kb has 42 candidate genes; RM5641-RM10890 spanning 9929.476 kb has 1207 candidate genes; RM7075-RM10916 covered 191.769 kb region with 27 candidate genes and RM279-RM12568 spanned 1392.02 kb region with 247 candidate genes , duration (short vs medium vs long) and other factors [viz., RGY, RBM, RGN and RBN with NUE parameters suggests that the relative traits could be contradictory for selection.Significant variation was observed for 24 traits/parameters of the present study between the parents and among the RILs across wet and dry seasons corroborating reported genetic and seasonal variability in rice under LN . Wide ge factors . Genotyp factors , 77–79.  factors . Decreas factors , 81. Tho factors –85. The  factors . The eff factors , 86–88.  factors . The use factors . The negIdentification of markers/QTL associated with traits of interest under LN would be useful in MAS for the development of rice varieties with NUE. Trait wise marker association exclusively under LN through SMA has identified more markers during the dry season over wet season. GNPP (grain nitrogen per plant), TDM  and GY (grain yield) found to be associated with >10 markers which is expected owing to the complexity of the traits . Around viz., QTL common for both treatments and QTL found to be exclusive for either LN or RN. The basic mechanism for N metabolism in rice is similar, thus common QTL are expected for LN and RN as reported [Selective genotyping approach showed five genomic regions associated with yield under LN reiterating the utility of this approach in saving of time and resources for identification of marker associated genomic regions as reported in earlier studies . Since yreported , 90. Howreported . Pyramidviz., RBN, RBM and RGN with more than 58% PV. Wei et al [In the present study, we found five genomic regions harboring two or more than 2 QTL. The major stable pleiotropic QTL identified in our study (RM13201—RM13209) from PTB1 spanning 825.4 kb region associated with straw N % (SNP) in both treatments across seasons and yield and yield related traits in WS appears to be promising for the MAS. Out of eight QTL identified in this region, five QTL  have shown PV >38%. The second major QTL (RM13181-RM13201) was found to be associated with only relative trait parameters ei et al  indicateAfter the first QTL study under LN in rice , more thGrain yield and nitrogen use efficiency are complex traits and depend on interaction of various primary traits. Correlation and path coefficient analysis of the present study suggests that PH, TNO, PNO, GRNO, TDM, and GNPP need to be considered for the rice yield improvement under LN. More than 50 promising RIL >10 g yield under LN were identified. Using single marker analysis, 144 marker trait associations were noted under LN, out of which 13 markers could be deployed either for MAS or for fine mapping. A major stable pleiotropic (RM13201—RM13209) from PTB1 region associated with straw N % (SNP) in both treatments across seasons and yield and yield related traits in WS and another major QTL (RM13181-RM13201) associated with only relative trait parameters of biomass, grain and grain nitrogen were identified for utilization in marker assisted breeding programs and further characterization.S1 TableWS: Wet season, DS: Dry season, Low N: Low nitrogen, Rec N: Recommended nitrogen.(XLSX)Click here for additional data file.S2 TableA. Details of soil properties of the experimental plot. B. Details of weather parameters during the experiement.(XLSX)Click here for additional data file.S3 TableWS: Wet season, DS: Dry season, P: Parent.(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 TableWS: Wet season, DS: Dry season.(XLSX)Click here for additional data file.S6 TableSPAD: SPAD value, LL: leaf length (cm), LW: leaf width (cm), LA: leaf area (cm), DFF: days to 50% flowering, PH: plant height (cm), TNO: tiller number, PNO: panicle number: GRNO: grain number, HSW: hundred seed weight (g), GY: grain yield per plant (g/plant), TDM: total dry matter (g/plant), GNP: grain nitrogen percent, SNP: straw nitrogen percent, GNPP: grain nitrogen/ plant, SNPP: straw nitrogen/ plant.(XLSX)Click here for additional data file.S7 TableA. Path coefficient analysis of morpho physiological, agronomic and yield traits under low nitrogen in wet season. SPAD: SPAD value, LL: Leaf length (cm), LW: Leaf width (cm), LA: Leaf area (cm), DFF: Days to 50% flowering, PH: Plant height (cm), TNO: Tiller number, PNO: Panicle number: GRNO: Grain number, HSW: Hundred seed weight (g), GY: Grain yield per plant (g/plant), TDM: Total dry matter (g/plant), GNP: Grain nitrogen percent, SNP: Straw nitrogen percent, GNPP: Grain nitrogen/ plant, SNPP: Straw nitrogen/ plant, NHI: Nitrogen harvest index. B. Path coefficient analysis of morpho physiological, agronomic and yield traits under low nitrogen in dry season. SPAD: SPAD value, LL: Leaf length (cm), LW: Leaf width (cm), LA: Leaf area (cm), DFF: Days to 50% flowering, PH: Plant height (cm), TNO: Tiller number, PNO: Panicle number: GRNO: Grain number, HSW: Hundred seed weight (g), GY: Grain yield per plant (g/plant), TDM: Total dry matter (g/plant), GNP: Grain nitrogen percent, SNP: Straw nitrogen percent, GNPP: Grain nitrogen/ plant, SNPP: Straw nitrogen/ plant, NHI: Nitrogen harvest index. C. Path coefficient analysis of morpho physiological, agronomic and yield traits under recommended nitrogen in wet season. SPAD: SPAD value, LL: Leaf length (cm), LW: Leaf width (cm), LA: Leaf area (cm), DFF: Days to 50% flowering, PH: Plant height (cm), TNO: Tiller number, PNO: Panicle number: GRNO: Grain number, HSW: Hundred seed weight (g), GY: Grain yield per plant (g/plant), TDM: Total dry matter (g/plant), GNP: Grain nitrogen percent, SNP: Straw nitrogen percent, GNPP: Grain nitrogen/ plant, SNPP: Straw nitrogen/ plant, NHI: Nitrogen harvest index. D. Path coefficient analysis of morpho physiological, agronomic and yield traits under recommended nitrogen in dry season. SPAD: SPAD value, LL: Leaf length (cm), LW: Leaf width (cm), LA: Leaf area (cm), DFF: Days to 50% flowering, PH: Plant height (cm), TNO: Tiller number, PNO: Panicle number: GRNO: Grain number, HSW: Hundred seed weight (g), GY: Grain yield per plant (g/plant), TDM: Total dry matter (g/plant), GNP: Grain nitrogen percent, SNP: Straw nitrogen percent, GNPP: Grain nitrogen/ plant, SNPP: Straw nitrogen/ plant, NHI: Nitrogen harvest index.(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 TableA. Trait wise marker association under low nitrogen identified through single marker analysis. SPAD: SPAD value, LL: leaf length (cm), LW: leaf width (cm), LA: leaf area (cm), DFF: days to 50% flowering, PH: plant height (cm), TNO: tiller number, PNO: panicle number: GRNO: grain number, HSW: hundred seed weight (g), GY: grain yield per plant (g/plant), TDM: total dry matter (g/plant), GNP: grain nitrogen percent, SNP: straw nitrogen percent, GNPP: grain nitrogen/ plant, SNPP: straw nitrogen/ plant, NHI: nitrogen harvest index, RGY: relative grain yield, RBM: relative biomass yield, RGN: relative grain nitrogen, RBN: relative biomass nitrogen, PNUE: physiological nitrogen use efficiency, ANUE: agronomic nitrogen use efficiency, APE: agro- physiological efficiency. B. Chromosome wise marker trait association under low nitrogen identified through single marker analysis. SPAD: SPAD value, LL: leaf length (cm), LW: leaf width (cm), LA: leaf area (cm), DFF: days to 50% flowering, PH: plant height (cm), TNO: tiller number, PNO: panicle number: GRNO: grain number, HSW: hundred seed weight (g), GY: grain yield per plant (g/plant), TDM: total dry matter (g/plant), GNP: grain nitrogen percent, SNP: straw nitrogen percent, GNPP: grain nitrogen/ plant, SNPP: straw nitrogen/ plant, NHI: nitrogen harvest index, RGY: relative grain yield, RBM: relative biomass yield, RGN: relative grain nitrogen, RBN: relative biomass nitrogen, PNUE: physiological nitrogen use efficiency, ANUE: agronomic nitrogen use efficiency, APE: agro- physiological efficiency.(XLSX)Click here for additional data file.S11 TableA. Trait wise associations with phenotypic traits with single marker analysis of for nine polymorphic SSRs. SPAD: SPAD value, LL: leaf length (cm), LW: leaf width (cm), LA: leaf area (cm), DFF: days to 50% flowering, PH: plant height (cm), TNO: tiller number, PNO: panicle number: GRNO: grain number, HSW: hundred seed weight (g), GY: grain yield per plant (g/plant), TDM: total dry matter (g/plant), GNP: grain nitrogen percent, SNP: straw nitrogen percent, GNPP: grain nitrogen/ plant, SNPP: straw nitrogen/ plant, NHI: nitrogen harvest index, RGY: relative grain yield, RBM: relative biomass yield, RGN: relative grain nitrogen, RBN: relative biomass nitrogen, PNUE: physiological nitrogen use efficiency, ANUE: agronomic nitrogen use efficiency, APE: agro- physiological efficiency. B. Marker wise associations with phenotypic traits with single marker analysis of for nine polymorphic SSRs. SPAD: SPAD value, LL: leaf length (cm), LW: leaf width (cm), LA: leaf area (cm), DFF: days to 50% flowering, PH: plant height (cm), TNO: tiller number, PNO: panicle number: GRNO: grain number, HSW: hundred seed weight (g), GY: grain yield per plant (g/plant), TDM: total dry matter (g/plant), GNP: grain nitrogen percent, SNP: straw nitrogen percent, GNPP: grain nitrogen/ plant, SNPP: straw nitrogen/ plant, NHI: nitrogen harvest index, RGY: relative grain yield, RBM: relative biomass yield, RGN: relative grain nitrogen, RBN: relative biomass nitrogen, PNUE: physiological nitrogen use efficiency, ANUE: agronomic nitrogen use efficiency, APE: agro- physiological efficiency.(XLSX)Click here for additional data file.S12 TableSPAD: SPAD value, LL: leaf length (cm), LW: leaf width (cm), LA: leaf area (cm), DFF: days to 50% flowering, PH: plant height (cm), TNO: tiller number, PNO: panicle number: GRNO: grain number, HSW: hundred seed weight (g), GY: grain yield per plant (g/plant), TDM: total dry matter (g/plant), GNP: grain nitrogen percent, SNP: straw nitrogen percent, GNPP: grain nitrogen/ plant, SNPP: straw nitrogen/ plant, NHI: nitrogen harvest index, RN: Recommended nitrogen, LN: Low nitrogen, WS: Wet season, DS: Dry season.(XLSX)Click here for additional data file.S13 Table(XLSX)Click here for additional data file.S1 FigDetails of inter trait correlation under recommended nitrogen in wet season (1-A), recommended nitrogen in dry season (1-B), nitrogen efficiency indices in wet season (1-C) and nitrogen efficiency indices in dry season (1-D). SPAD: SPAD value, LL: leaf length (cm), LW: leaf width (cm), LA: leaf area (cm), DFF: days to 50% flowering, PH: plant height (cm), TNO: tiller number, PNO: panicle number: GRNO: grain number, HSW: hundred seed weight (g), GY: grain yield per plant (g/plant), TDM: total dry matter (g/plant), GNP: grain nitrogen percent, SNP: straw nitrogen percent, GNPP: grain nitrogen/ plant, SNPP: straw nitrogen/ plant, NHI: nitrogen harvest index, RGY: relative grain yield, RBM: relative biomass yield, RGN: relative grain nitrogen, RBN: relative biomass nitrogen, PNUE: physiological nitrogen use efficiency, ANUE: agronomic nitrogen use efficiency, APE: agro physiological efficiency.(DOCX)Click here for additional data file.S2 Fig(DOC)Click here for additional data file.