To evaluate heart rate variability among adults with different risk levels for type 2 diabetes mellitus was assessed in 130 participants  based on the questionnaire Finnish Diabetes Risk Score and was classified as low risk (n=26), slightly elevated risk (n=41), moderate risk (n=27) and high risk (n=32). To measure heart rate variability, a heart-rate monitor Polar S810i® was employed to obtain RR series for each individual, at rest, for 5 minutes, followed by analysis of linear and nonlinear indexes. The risk for type 2 diabetes mellitus had significantly lower linear and nonlinear heart rate variability indexes. The groups at higher risk of type 2 diabetes mellitus have lower heart rate variability. The individuals at high risk for type 2  According to forecasts, DM will have an increasing impact on years of life lost due to premature death and disability, all over the world, moving from the 11th to 7th cause of death by 2030.3The incidence of 4 evidenced by decreased heart rate variability (HRV), which results in cardiac autonomic neuropathy and increased risk for sudden cardiac death.6 HRV is a non-invasive measurement that indirectly reflects the cardiac autonomic regulation. Its analysis is based on fluctuations in the sequential RR intervals from sinus rhythm.8Type 2 DM (T2DM) individuals present reduced autonomic function in cardiovascular system,9 The original instrument, composed of eight questions, was adapted by the Ministry of Health to the Brazilian reality. It establishes five risk categories: low, slightly elevated, moderate, high and very high.The Finnish Diabetes Risk Score (FINDRISC) is a data collection tool validated by the Department of Public Health of the University of Helsinki, Finland.8 were the pioneers in showing that the group classified as slightly elevated risk for T2DM  already had changes in linear HRV parameters (time and frequency domain) when compared to the low risk group . These authors did not include individuals with moderate, high and very high risk, or analysis of nonlinear methods.Penčić-Popović et al.10 The nonlinear methods, such as Poincaré plot11 and tone-entropy analysis,12are newly developed tools to identify nonlinear patterns of ECG data.10The time and frequency domain parameters of HRV may not represent the non-stationary characteristics of electrocardiography (ECG) due to the nonlinear dynamics of heart rhythm.diabetes mellitus.To evaluate heart rate variability, among adults with different risk levels for type 2 Instituto Federal do Sudeste de Minas Gerais, Brazil, under protocol number 822.457 and CAAE: 31828814.0.0000.5588.This is an analytical cross-sectional study carried out from November 2014 to April 2015, after approval by the Research Ethics Committee of the The study enrolled 130 individuals, mean age of 51.0 years  with no diagnosis of T2DM, and presenting severe arterial hypertension and/or heart disease. The Finnish Diabetes Risk Score was applied to assess the risk of developing T2DM within 10 years. This questionnaire addresses age, body mass index (BMI), waist circumference, physical activity, daily intake of fruits and/or vegetables, use of antihypertensive drugs, previous history of glycemic values and family history of diabetes. This tool allows a maximum score of 26 points, and scores higher than 15 indicate high probability of developing T2DM. Participants were allocated to four groups: Group 1 for those who scored <7 points and had low risk (n=26); Group 2 for those who scored from 7 to 11 points and presented slightly elevated risk (n=41); Group 3 for those who scored from 12 to 14 points and showed moderate risk (n=27) and Group 4 for those who scored from 15 to 20 points and had high risk (n=32). Individuals who used antidiabetic drugs or had been diagnosed as type 1 DM were excluded.The heart rate was extracted from data generated in the Polar Electro Oy heart rate monitor, using the Polar ProTrainer software and considering the mean, maximum and minimum values.Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using a mercury-column sphygmomanometer and a stethoscope. The mean arterial pressure (MAP) was obtained based on the mathematical formula (SBP minus DBP) divided by 3+DBP.® Active glucometer with Accu-Check®Active strips was used. Participants were oriented to fast for 8 hours and to not to drink alcoholic beverages in the previous 24 hours.To assess fasting glucose levels, an Accu-Check2). Waist circumference (cm) was considered the smallest perimeter of the abdomen, while hip circumference (cm) was measured at the level of maximum posterior extension of the buttocks. The waist-hip ratio (WHR) was calculated by dividing the waist circumference (cm) by hip circumference (cm). To determine the percentage of fat, four-pole electrical bioimpedance analysis was performed using Biodynamics® BIA 450, and the participants were on supine position. They were oriented to urinate before the test.The body mass (kg) was assessed using mechanical scales, with a maximum capacity of 300kg and accuracy of 200g. Height was measured with a stadiometer coupled to the scales. The BMI was obtained by weight (kg) divided by square height , which was previously validated for beat-to-beat capture.15The data were extracted and converted into text by using the Polar ProTrainer software. The initial and final records were disregarded so that only 300 heartbeats were analyzed by HRV analysis software.HRV was analyzed according to linear (time and frequency domain) and nonlinear methods. Time domain measurements included mean RR interval (ms); standard deviation of the NN intervals (SDNN) recorded in a period of time expressed as ms; root mean squared successive differences (RMSSD) between adjacent normal RR intervals in a time interval, also expressed as ms; percentage of adjacent RR intervals differing by >50ms (pNN50); and the triangular index.2 and percentage, the low frequency (LF) component, in ms2 and percentage, the high-frequency component (HF), in ms2 and percentage, and the LF/HF ratio. The HF component acts in HRV, reflecting parasympathetic modulation, while the LF component includes sympathetic and parasympathetic function, and the VLF reflects parasympathetic and neuroendocrine modulation.8The frequency domain covers the very low frequency component (VLF), in ms16Nonlinear methods comprised the variables short-term variability (SD1), in ms, long-term variability (SD2), in ms, approximate entropy (ApEn) and sample entropy (SampEn). The Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology present detailed explanation about each variable.post hoc Tukey tests. Categorical variable (sex) was described as absolute frequency and relative frequency and was compared according to G test. Biostatisc 5.0 software was used at 5% error probability (p<0.05).Quantitative variables were presented as mean and SD and compared with FINDRISC risk groups, according to Analysis of Variance (ANOVA) and Descriptive and inferential statistic values for sociodemographic and anthropometric variables, body composition and glucose levels of participants are shown in By and large, the cardiovascular variables SBP, DBP and MAP as well as maximum, minimum and mean heart rate, had no significant difference among groups.2 and %) were lower  in the group with the highest score, in which HF (%) was also lower (p=0.03), indicating possible silent cardiac autonomic dysfunction. The sympathetic-vagal balance given by LF/HF ratio was not significantly different among groups.Results for frequency domain are shown in 20 To date, only one study has correlated FINDRISC risk of developing T2DM with HRV measurements.8Penčić-Popović et al.8analyzed only two groups − low risk (score <7) and slightly elevated risk (score 7-11) −, based on linear indexes. Therefore, this is the first study to include the moderate risk (score 12 to 14) and the high risk (score 15 to 20) groups in the HRV analysis based on linear and nonlinear indexes. The methods used were different, since in the study by Penčić-Popovic et al.,8 the ECG was used for 24 hours, while in the present study a Polar S810i was used for 5 minutes, at rest. These measurements at rest correlate and agree with the circadian variation described in the ECG.21Previous studies reported decreased HRV for diabetic patients with or without associated diabetic neuropathy.14 The major reasons for autonomic cardiovascular dysfunction were found in individuals who had lower glucose tolerance, newly diagnosed diabetes and diabetes, respectively. Penčić-Popović et al.8 observed compromised sympathetic and vagal modulation in the slightly elevated risk group (7≤ FINDRISC <12), due to decrease in SDANN (p=0.035), VLF (p=0.03), LF (p=0.006) and HF (p=0.011) values, compared to the low-risk group (score <7). Some studies21 found significantly decreased SDNN, HF and LF in diabetic patients and in individuals with high blood glucose, and the reduced HRV was identified as a sensitive marker for subclinical autonomic neuropathy.19The groups that obtained higher scores, especially Group 4, showed lower values of both parasympathetic  and sympathetic modulation (LF), resulting in lower values of SDNN, triangular index and nonlinear indexes . Different degrees of glucose intolerance are inversely proportional to reduced HRV.22 The graphic analysis according to Poincaré plot and sample and approximate entropy have been increasingly used because they compute shorter HRV intervals and provide additional information about each RR interval, which cannot be obtained by means of conventional analysis methods.22 In this study, there was a reduction in entropy for Group 4, compared to Groups 1 and 2, considering the sample entropy parameters (p=0.08), SD1 (p=0.007) and SD2 (p=0.05). Similarly, in a study23 with 63 patients showing T2DM and 29 clinically healthy individuals, approximate entropy was not significantly different (p=0.199); however, the curve for healthy participants was higher than for diabetics, suggesting that the decrease in entropy is correlated with compromised vagal modulation on the heart. Since decreased circadian rhythm values have been strongly associated with mortality, the individuals with higher FINDRISC scores should be more often monitored for cardiovascular risk factors.19Adopting only parameters in the time and frequency domain to analyze HRV decrease is not always suitable due to variance in data and presence of nonlinear phenomena.24 In addition, increased SBP is considered a feature of DM225 and is correlated with increased FINDRISC scores for both sexes.18 The increase by 20mmHg in SBP or 10mmHg in DBP doubles the risk of death from heart attack, ischemia and other vascular complications.26The use of antihypertensive drugs has been associated with increased risk of T2DM26 and was not discontinued for HRV tests.The hemodynamic variables (heart rate and BP) did not vary among groups. However, there is evidence that the increase in heart rate is proportional to the rise in blood glucose levels in individuals with no diagnosis of T2DM, especially when compared to diabetic patients.8 Makrilakis et al.1and Lindström et al.,9 who also used the FINDRISC. In the present study, the majority of the study population had waist circumference inferior to the cutoff values for cardiovascular disease risk (88cm for women and 102cm for men). However, the higher the FINDRISC, the higher the percentage of abdominal fat and adiposity; thus, waist circumference can act as a good tool to predict visceral obesity, according Meijnikman et al.27The anthropometric measurements (BMI and waist circumference) found for participants in this study were lower than results reported by Penčić-Popović et al.,17 In clinical practice, the use of FINDRISC as a tool for cardiovascular risk monitoring should be encouraged, considering the score was capable of identifying individuals who had compromised sympathetic and vagal modulation of the heart, even with no diagnosis of T2DM.The gradual decrease in HRV parameters (both in time domain and in frequency domain) correlated with increased age, BMI and visceral fat, has been associated with a possible silent dysautonomia in individuals not diagnosed as DM, but with strong risk factors.28 overweight30 and abdominal obesity31 are factors that decrease the HRV − either associated or not with diagnosed T2DM.17 However, the risk of developing T2DM is known to be higher in individuals aged over 45 years and overweight, indicating that these two conditions are almost inseparable. In addition, all these items  are considered in the FINDRISC score. The cross-sectional design of the present study did not allow to conclude that the changes in HRV found in the comparison among groups were due to the higher risk of developing T2DM, or to these individual characteristics (age and body composition). A prospective cohort study should be conducted to elucidate this issue, as carried out by Wulsin et al.,32 who predicted T2DM from HRV. The analysis of HRV in the time and frequency domain using nonlinear indexes, as a function of T2DM risk stratification according to FINDRISC, based on the study by Penčić-Popović et al.,8 can support future cohort studies. Could there be any differentiation in predicting T2DM from HRV categorized according to FINDRISC? Could this lead to the proposal of a new FINDRISC cutoff by means of HRV analysis?Thus, it must be considered that advanced age,27The combination between reduced HRV and risk factors for diabetes, according to FINDRISC, may undergo changes due to modifications in lifestyle. However, T2DM individuals should be constantly and properly instructed by health professionals to adopt healthy lifestyle habits.Early diagnosis provides interventions in pre-diabetes patients, which is important in the primary prevention of T2DM and its chronic complications, such as cardiac abnormalities.33 the interventions in lifestyle of individuals with altered fasting glucose or glucose intolerance resulted in decreased HR and increased HRV. In a study by Howorka et al.,34 HRV increased in diabetic patients who had trained on a stationary bike for 12 weeks. This result was found for patients without cardiovascular autonomic neuropathy or with the disease in its initial phase. Nevertheless, no effect on HRV was observed in patients with severe cardiovascular autonomic neuropathy. Ziegler et al.14 proposed that the reduction in HRV could be used as single-scale screening of cardiac autonomic dysfunction in individuals with varying degrees of glucose intolerance.In the diabetes prevention program developed by Carnethon et al.,The association of FINDRISC with HRV data is an efficient method for screening the risk of developing T2DM and heart dysautonomia, which is one of its serious complications. Hence, screening for early detection of diabetes and possible complications is justified, so that the intervention will be possible and effective.Although the present study innovates in showing changes between linear and nonlinear HRV indexes for individuals belonging to different FINDRISC risk groups for T2DM, some limitations must be pointed out. The first limitation was the small number of participants who obtained a score of >20 points (very high risk group), which prevented their inclusion in statistical comparisons. The second limitation was no differentiation between smokers and non-smokers among participants. All these gaps offer prospects for further studies.The groups with the highest Finnish Diabetes Risk Score had lower heart rate variability, including the variables of linear  and nonlinear methods , suggesting lower parasympathetic and sympathetic activity.


To investigate the trends in mortality, as well as in the timing and cause of death, among extremely preterm infants at the limit of viability, and thus to identify the clinical factors that contribute to decreased mortality.We retrospectively reviewed the medical records of 382 infants born at 23–26 weeks’ gestation; 124 of the infants were born between 2001 and 2005 (period I) and 258 were born between 2006 and 2011 (period II). We stratified the infants into two subgroups–“23–24 weeks” and “25–26 weeks”–and retrospectively analyzed the clinical characteristics and mortality in each group, as well as the timing and cause of death. Univariate and multivariate logistic regression analyses were done to identify the clinical factors associated with mortality.vs. 8.1% [10/124], respectively) and air-leak syndrome (0.8% [2/258] vs. 4.8% (6/124), respectively) during period II than during period I. Among the clinical factors of time period, 1-and 5-min Apgar score, antenatal steroid identified significant by univariate analyses. 5-min Apgar score and antenatal steroid use were significantly associated with mortality in multivariate analyses.The overall mortality rate in period II was 16.7% (43/258), which was significantly lower than that in period I . For overall cause of death, there were significantly fewer deaths due to sepsis (2.4% [6/258] Improved mortality rate attributable to fewer deaths due to sepsis and air leak syndrome in the infants with 23–26 weeks’ gestation was associated with higher 5-minute Apgar score and more antenatal steroid use. Recent improvements in perinatal and neonatal intensive care have resulted in improved survival in extremely preterm (EPT) infants near the limit of viability –7. NonetTo understand the factors that contribute to death in EPT infants, it is important to identify the timing and specific causes of death. Historically, most EPT infants died within a few days of birth –18, immaRecently, we noted that improved active perinatal and neonatal intensive care markedly improved survival among EPT infants born at 23–26 weeks’ receiving active treatments including intubation of all these infants in the delivery room throughout the study periods . In the Data collection was approved by the Institutional Review Board of Samsung Medical Center, who waived the requirement for informed consent in this retrospective chart review (IRB No. SMC 2016-03-121). We retrospectively reviewed the medical records of 382 preterm infants who were born at 23 and 26 weeks’ gestation and admitted to the neonatal intensive care unit (NICU) at Samsung Medical Center; 124 of the infants were born between January 1, 2001 and December 31, 2005 (period I), while 258 were born between January 1, 2006 and December 31, 2011 (period II). The study period was divided on the basis of changes in the survival rate of these EPT infants. We compared overall mortality, as well as timing and cause of death, between the two time periods. In admission, we compared the proportionate cause-specific mortality rate in terms of timing of death between the two time periods.th percentile. Chorioamnionitis was confirmed by placental pathology, and PROM was identified when the duration of PROM was more than 24 hours.Mortality in the delivery room was included in the study, and all the infants were discharged alive or dead from our NICU; none of the infants was transferred to another center during the study period. Cause of death, defined as the disorder directly and immediately causing death of the infant, was determined by the direct and immediate cause of death ascertained from the death certificate, and these causes of death were adjudicated with the information obtained from the medical records. These listed disorders were not categorized, and instead any changes in the incidence of these specific causes of death during the study periods were analyzed. There was no death directly and immediately caused by major congenital anomalies in these infants. Pulmonary hypoplasia was defined as clinical findings associated with oligohydramnios from premature rupture of membrane (PROM) plus aggressive ventilator support, but not histologically confirmed. Air leak syndrome was defined as radiologic findings of extra-pulmonary air requiring chest tube insertion and drainage. Pulmonary hemorrhage was defined as presenting with bloody fluid from the endotracheal tube plus radiologic suggestion of pulmonary hemorrhage. Acute renal failure was defined as urine output of less than 0.5 mL/kg/ day for ≥24 hours combined with a serum creatinine level of ≥ 2.0mg/dL. Sepsis was defined as clinical symptoms/signs suggestive of sepsis including hypotension plus positive blood culture. BPD was defined as the need for supplemental oxygen and/or positive pressure to maintain oxygen saturation >90% at ≥ 28 postnatal days . IVH wast-test were used to analyze continuous variables. Univariate logistic regression analyses in each subgroup were applied to calculate the crude odds ratios of individual variables for mortality. In order to identify the independent risk factors associated with mortality, multivariate logistic regression analysis was performed to control for the effects of confounding variables. Multivariate analysis included only variables with a P value < 0.05 on univariate analysis. The adjusted odds ratio (OR) and 95% confidence interval (CI) for each possible risk factor were calculated. A P value of <0.05 was considered statistically significant. All statistics were analyzed using SPSS version 18 .To analyze categorical variables, the chi-square test or Fisher’s exact test were used, while the Mann-Whitney U test or Student’s P = 0.514)  .With regards to timing of death, during period I, mortality was the common from postnatal day P2 to P7 (12.1%), while during period II, mortality was most common after P28 (7.5%) . From P2From P8 and P28, the proportionate mortality rate was 23.7% during period, which had increased during period II, without statistical significance, to 34.9%. In the subgroup analyses, from P8 to P28, the proportionate mortality rate due to air-leak syndrome in the infants born between 25 and 26 weeks’ gestation during period I was 22.2%; this had significantly improved (to 0%) during period II.During period I, the overall proportionate mortality rate after P28 was of 21.1%; this had significantly increased (to 41.9%) during period II. The mortality due to the most common cause of death after P28, BPD, did not change significantly between the study periods; however, mortality after P28 due to the sequelae of IVH such as PHH had significantly increased from 0% during period I to 11.6% during period II.In the univariate analyses of possible clinical factors associated with mortality, the following were identified as significant: study period, 1- and 5-minute Apgar score, antenatal steroid use . HoweverMortality rates in EPT infants have been declining incrementally during the past decades due to continuing improvements in perinatal and neonatal intensive care –7. HowevEPT birth is a leading cause of infant mortality and morbidity in survivors; for this reason, researchers are concerned that improved mortality rates in EPT infants at the verge of viability will be accompanied by an increase in disabling morbidities among survivors , 27, 28.Several recent studies reported improved survival among infants near the limit of viability , 11, 15;It is important that clinicians and researchers identifying the practices primarily responsible for the improved mortality rates in these infants. Patel et al.  conjectuIn the present study, during period II, there were significantly higher rates of delivery by cesarean section and antenatal steroid use, especially among infants born between 23 and 24 weeks’ gestation; furthermore, the 1- and 5-minute Apgar scores were better during period II. However, only the clinical factors of 5-minute Apgar score and antenatal steroid uses were significantly associated with mortality in multivariate analyses. These findings suggest that both active antenatal steroid use , 39, 40 The present study had several limitations; for instance, it had a retrospective design. Furthermore, even though we reviewed all the medical records to confirm the primary cause of death written on death certificate, it is sometimes difficult to differentiate a single primary cause of death in a complex situation where multiple causes interact; for this reason, the listed cause of death may have been subjective. Another limitation was that, as our data were obtained from a single institution, the findings observed in the present study might not be generalizable to other institutions. However, we used a relatively large sample of 382 infants born between 23 and 26 weeks’ gestation at a single institution; moreover, the infants had similar baseline characteristics, which implies that this study was better placed to identify those therapeutic strategies that are associated with improved mortality rates in these infants. The relatively small sample size of the subgroups might be another limitation of this study. However, although consistency of treatment have not been measured, treatments given by the same neonatologists in a single center and statistically significant improved mortality even in these small sample size subgroups night redeem this limitation.In conclusion, overall mortality, especially in infants born between 23 and 24 weeks’ gestation, was significantly decreased, as were deaths due to sepsis and air-leak syndrome. The proportion of deaths caused by complications of severe IVH increased. The improved mortality in these EPT infants was significantly associated with higher 5-minute Apgar score and more antenatal steroid use. These findings underscore the developing and implementing new therapeutic strategies to overcome the currently known limit of viability in EPT infants.


There is general agreement that the technical potential for sequestration of carbon in soil is significant, and some consensus on the magnitude of that potential. Croplands worldwide could sequester between 0.90 and 1.85 Pg C/yr, i.e. 26–53% of the target of the “4p1000 Initiative: Soils for Food Security and Climate”. The importance of intensively cultivated regions such as North America, Europe, India and intensively cultivated areas in Africa, such as Ethiopia, is highlighted. Soil carbon sequestration and the conservation of existing soil carbon stocks, given its multiple benefits including improved food production, is an important mitigation pathway to achieve the less than 2 °C global target of the Paris Climate Agreement.The role of soil organic carbon in global carbon cycles is receiving increasing attention both as a potentially large and uncertain source of CO Both land-use change to agriculture and agricultural production have and continue to contribute significantly to the increase in atmospheric carbon dioxide (CO2), accounting for as much as 24% of global greenhouse gas (GHG) emissions5. Almost 50% of all potentially vegetated land surface globally has been converted to croplands, pastures and rangelands4. This land-use change and soil cultivation have contributed 136 ± 55 petagrams of carbon (Pg C) to the atmosphere from change in biomass carbon since the beginning of the Industrial Revolution, with depletion of soil organic carbon (SOC) accounting for a further contribution of 78 ± 12 Pg C. This estimated 214 ± 67 Pg C from the land-use sector compares to the estimated 270 ± 30 Pg of C contributed by fossil fuel combustion6 as a historical carbon source. More recently soil organic matter also has gotten increasing attention as a potentially large and uncertain source of carbon to the atmosphere in the future in response to predicted global temperature rises8.Historical and ongoing increase of agricultural production worldwide has profoundly impacted global carbon, water and nutrient cycles10. An extensive body of research has shown that land management practices can increase soil carbon stocks on agricultural lands with practices including addition of organic manures, cover cropping, mulching, conservation tillage, fertility management, agroforestry, and rotational grazing12. There is general agreement that the technical potential for sequestration of carbon in soil is significant, and some consensus on the magnitude of that potential13. On this basis, the 4p1000 initiative on Soil for Food Security and Climate14, officially launched by the French Ministry of Agriculture at the United Nations Framework Convention for Climate Change: Conference of the Parties (UNFCCC COP 21) in Paris, aims to sequester approximately 3.5Gt C annually in soils. Croplands will be extremely important in this effort, as these lands are already being actively managed, and so amenable to implementation of improved practices12. Furthermore, because almost all cropped soils have lost a large percentage of their pre-cultivation SOC15, they potentially represent a large sink to re-absorb carbon through the introduction and adoption of improved or proper management aimed towards increased SOC. However, carbon is rarely stored in soils in its elemental form, but rather in the form of organic matter which contains significant amounts of other nutrients, above all nitrogen. Nutrients, biomass productivity, the type of vegetation and water availability, among other constraints therefore can be major limiting factors inhibiting increases in soil carbon sequestration16. Further imperative to sequester carbon in soils arises from the multiple co-benefits that are obtained from sequestration of carbon in soils that have been depleted of their organic matter17. Soil fertility, health, and functioning are immediate consequences of the amount of soil organic matter (and hence carbon) a soil contains; this is even more important for highly weathered soils, as is the case for the majority of soils in the humid lowland tropics. Increasing carbon in soils also means improving its physical properties and related ecosystems services, such as better water infiltration, water holding capacity, as well as potentially increasing agricultural productivity and ecological resilience12.Soils, however, can act as both sources and sinks of carbon, depending upon management, biomass input levels, micro-climatic conditions, and bioclimatic change. Substantially more carbon is stored in the world’s soils than is present in the atmosphere. The global soil carbon (C) pool to one-meter depth, estimated at 2500 Pg C, of which about 1500 Pg C is soil organic carbon (SOC), is about 3.2 times the size of the atmospheric pool and 4 times that of the biotic pool17, such that the SOC status of almost all cultivated soils can be increased. It is expected that these cropped soils will be able to sequester carbon for at least 20 years before reaching saturation points and new SOC equilibriums18, while meta-analysis of field studies14 suggests that in some instances significant sequestration can continue for 30 or even up to 40 years before reaching new equilibriums. We used the recently released ISRIC SoilGrids250m19 global database of soil information, to identify and derive basic soil characteristics, i.e. SOC and soil bulk density, and the FAO GLC-Share Land Cover database20 to identify and calculate areal extent of the cropland landcover class. The analysis gives a spatially articulated estimate of the distribution and increase of SOC if equal sequestration is reached, within the medium and high scenarios, on all available cropland soils through improved practices. The results of this paper provide an estimate of what the potential amount of sequestered carbon would be in terms of tons of carbon per hectare, spatially articulated at 250 m resolution, and in terms of Pg C regionally and globally, allowing for a quantified discussion of the importance of this carbon pool within on-going global discussions regarding mitigation potential within the agricultural sector.In this analysis, we illustrate where carbon might be sequestered, and how much, if, through improved practices and management, we could increase SOC on agricultural land by a generally accepted (as attainable) moderate to optimistic amount, based on the medium and high sequestration scenarios of Sommer and Bossio (2014). These scenarios from Sommer and Bossio (2014) resulted in an 0.27 and 0.54% increase in SOC in the top 30 cm of soils after 20 years, for the medium and high scenarios, respectively, that is, a 0.012 to 0.027% annual increase. The low scenario in Sommer and Bossio (2014) was not used because it refers to sequestration rates estimated primarily for unimproved pasture land. An implicit basic assumption is that in general, 50 to 70% of soil carbon stocks have been lost in cultivated soils16, particularly within the context of a warming climate22, are now central to important discussions ongoing within various international fora, notably the discussions on including agricultural land within mitigation strategies and protocols at the UNFCCC, and are the basis for the 4p1000 Initiative14. The spatial distribution of SOC on croplands , however with substantial variability among both spatially- and non-spatially-explicit estimates and a range of from 500 to 3000 Pg C.Estimates of global soil carbon stocks, trends and sequestration potential2 of cropland  and which are considered likely to lose SOC under any form of cropping management, and sandy soils unlikely to sequester carbon due to high sand content (> 85%), were excluded from the analysis as “unavailable”  identified as potentially available for enhanced carbon sequestration through improved soil management and farming practices11. Global distribution of SOC is strongly influenced by temperature and precipitation29. SOC is generally lower in the tropics where it is hotter and/or drier, and higher in the cooler, wetter, more northerly, and to a somewhat lesser extent, southerly, latitudes  is stored on the 15.9 million kmdes Fig. .The regions of North America, Eurasia (Russia) and Europe currently store the greatest amount of carbon on cropland, each with more than 21 Pg C, and all together accounting for over 50% of all SOC stocks on cropland globally Table .17. The annual increment (on a per hectare basis) ranged from 0.50 up to 1.28 t C/ha across the various regions , although North Africa has very little cropland and hence only a small potential for total carbon soil sequestration.Our analysis shows that if the SOC content in the 0–30 cm depth layer of all available cropland increased from 0.27% to 0.54%, i.e. from the Sommer and Bossio 2014) medium to high scenarios, from 0.56 to 1.15 t C/ha/yr could be sequestered  on over 1.6 million km2 of cropland, shows a potential total annual increment ranging from 1.0 to over 2.1 Pg C. Likewise, China and Russia, each with about 1.2 million km2 of cropland, both have a large potential to increase total carbon storage, ranging from 0.63 to over 1.3 Pg C annually each. Many smaller countries exhibit high per hectare annual increments, although their total C sequestration is low due to small areas of cropland. For example, 45 countries are in the higher range above 0.58 to 1.2 t C/ha, however, many of these are found in drier climates where water and biomass availability may be a limiting factor.Among countries of the world, the United States showed the highest total annual potential in SOC (1.2–2.6 Pg C), with an average increase of 0.62–1.27 t C/ha/yr on over 2 million km12 is more immediately practical and likely than on the other available landuse types, e.g. forestry or grazing land. On these croplands adoption of improved management practices offers the opportunity to sequester significant amounts of carbon in the near term, and potentially to make an important contribution to global mitigation efforts. The 4p1000 Initiative has identified an aspirational sequestration target of 3.5 Pg C/yr to provide substantive global mitigation. Our estimates suggest that from 26% up to 53% (0.90–1.85 Pg C) of this target could be reached in the top 30 cm of cropland soils alone, and continue over at least 20 years after adoption of SOC enhancing management, such as incorporation of organic manures, cover cropping, mulching, conservation tillage, some types for agroforestry practices, rotational grazing, or other practices known to increase soil carbon at the decadal scale. This requires that croplands increase SOC storage between 0.55–1.15 t/ha/yr. However, as Sommer and Bossio (2014) point out, adoption can be expected to be phased in over a period of years, with delays in roll out as various countries, production systems and farm types may be slower to adopt improved practices. Likewise, our estimates do not account for differences in climate and important soil process issues, notably nutrient and water limitations, biomass production and turnover rates. However, given the large amount of cropland potentially available, sequestering carbon via increases in the soil component on agricultural land is an achievable and potentially effective route to quickly increasing CO2 sequestration in the near term. For comparison, above-ground losses due to tropical land use conversion are currently estimated at 0.6–1.2 Pg C yr-130. A strategy of enhancing agriculture with soil carbon enriching improved practices, e.g. via appropriate policy mechanisms, thus offers significant potential to mitigate landuse related carbon emissions and provide an opportunity for agricultural production to positively contribute to global mitigation efforts. SOC may be either enhanced by, or enhance above- and below-ground biomass carbon on agricultural land24, allowing for synergistic increases in on-farm carbon stocks. Agroforestry systems and planting trees, for example, may increase soil carbon sequestration12.Increasing soil organic carbon on the vast areas of cropland globally which are already intensively managed31. It is generally recognized that changes in the moisture regime (e.g. drought or heavy precipitation events) can significantly impact crop productivity32. These climatic conditions are mitigated by SOC33, which adds structure, improves water infiltration and holding capacity, increases cation exchange capacity, and impacts soil fertility, a major controlling factor of agricultural productivity and both regional and household food security34. Soil conditions have dramatic effects on the abundance and efficiency of N-fixing bacteria35, which are vitally important in cropping systems that lack fertilizer inputs36. Thus increased SOC through improved management practices is likely to add substantial resilience to croplands and farming systems, particularly during drought years or increased seasonal variability, helping to avoid edaphic (soil related) droughts that result from land degradation37.The benefits of increasing soil organic matter in croplands goes far beyond climate change mitigation potential. Facilitation of increased SOC through improved farming and soil conservation practices, enhancing resilience through improved fertility status and water holding capacity, also provide important adaptation benefits2 in the atmosphere40. Given that hundreds of millions of small farmers for their subsistence depend upon croplands around the world, mitigation benefits of enhanced SOC storage must be recognized as only one significant component of an array of multiple benefits to achieve.For the most part, agricultural practices that increase soil organic matter are supportive of enhanced food production and other ecosystem services. This is in contrast to other proposed negative emission strategies, such as afforestation (plantations of fast growing trees) and BECCS (bioenergy and carbon capture and storage) that will entail destruction of huge amounts of natural ecosystems or productive agriculture land if implemented at scales large enough to impact CO16. In addition, there may be tradeoffs with productivity, food security or hydrologic balances41, as well as concerns regarding other GHGs, such as N2O16. As with any efforts to sustain notable changes in practice significant understanding of cultural, political and socioeconomic contexts are required11. Enhanced understanding of land potential is also necessary to target limited resources37. While numerous constraints to achieving the technical potential of soil carbon sequestration exist, there are cases in which far higher sequestration rates are proposed than commonly accepted, even for annual cropping systems42. Refining our understanding of the technical potential for carbon sequestration in soils, and the practical implementation of improved soil management and farming practices aimed towards increasing SOC, offers a strategy for mitigation within the land use sector in the near-term, with potentially positive implications for food security and ecological resilience in the long-term.Despite the large technical potential to sequester carbon in soils, there are often significant limitations to achieving that potential in any particular place and within specific farming systems, including lack of biomass and other inputsThree publicly available global geospatial datasets were used for this study:3) and sand content (weight %), at several depths and depth layers, from 0–30 cm, were utilized in the analysis19.The recently released Soils Grid 250 m is a global database of soil properties at a spatial resolution of 250 m, provided by ISRIC World Soil Information. Datasets for SOC (g/kg), bulk density  fractional landcover geospatial database, provides an estimate of the percent of cropland area within a 1 km grid cell, and was used to identify cropland extent. The designation “cropland” is an aggregated category based upon the UN Land Cover Classification System, which includes herbaceous crops, woody crops, and multiple or layered crops. This dataset was resampled to allow for the analysis and geoprocessing at the finer 250 m (0.002083333 degree) resolution of the soils data43.The GADM database of Global Administrative Areas v 2.8 was used to analyze results by regions and countriesThe geospatial analysis uses the equation for potential carbon sequestration delineated in Sommer and Bossio (2014) as the estimate of the potential attainable increase of SOC on croplands after twenty years. These starting estimates, i.e. 0.27 increase for the medium scenario and 0.54 increase for the high scenario, are used to determine the estimate of SOC sequestration potential in tons of carbon (or Pg C), on a grid cell by grid cell basis. The SoilsGrid250m SOC and bulk density datasets were used to parameterize the conversion from SOC as a percent of the top 30 cm soil depth layer, to SOC in t C . Only the medium and high sequestration scenarios pertinent to croplands are presented, since the low scenario in Sommer and Bossio (2014) refers to sequestration rates for unimproved pasture land. SOC sequestration. We have estimated the increase after a 20-year time span in order to provide an operational description of the linear portion of the SOC sequestration curve, noting that the rate of increase is likely to decrease sometime thereafter and eventually reach a new equilibrium , a and b are empirical constants and t the time expressed in years. t0 is the year where the slope of the curve is largest, i.e. the annual sequestration rate highest. The parameters for the two scenarios (based upon Sommer and Bossio 2014), were:The increase in %-SOC in response to improved management or other sequestration measures was described in Sommer and Bosio (2014) with a four-parameter sigmoid function of the form:20) was calculated from this curve for the two scenarios as:The percent increase of SOC after 20 years (TBulk density was used to first convert SOC (t/ha) (as presented in the SoilsGrid250m data) into SOC (%). The estimated percentage increase was then added, and the result was then converted back in SOC (t/ha).3, and/or any gird cells with more than 400 t C/ha. These high carbon soils were excluded from further analysis. Sandy soils were mapped in a separate layer by identifying grid cells with sand content (at 15 cm) equal to or greater than 85%, and likewise, were excluded from further analysis.High SOC soils were mapped in a separate layer by identifying grid cells with a weighted average bulk density (0–30 cm) equal to or less than 1.0 kg/mhttp://ciat.cgiar.org/global-soil-carbonAll result grids converted to World Sinusiodal projection to allow for area calculations. The GLCShare – Dominant (Class 2 = Cropland) dataset, in percent area of a 1 km grid cell, was resampled to 250 m and multiplied times the various results (t/ha) to calculate actual total tC in each grid cell, i.e. given the actual area of agricultural land in that grid cell. The sum of grid cells was then multiplied by the number of hectares to provide regional and country zonal statistics. A full description of the geospatial methodology used to calculate the agricultural area, percent SOC, the conversion to tons per hectare, and the aggregation of tons of SOC on cropland is given in the Supplemental Materials. Results datasets are archived and available from Harvard Dataverse, accessible from the following link: DOI: 10.7910/DVN/HYFICT.  High resolution maps are available online at: Supplementary Information


Carney complex (CNC) is a very rare disease. Although thyroid lesions are included in the diagnostic criteria for CNC, they are an infrequent occurrence.99mTc in these lesions, which was consistent with toxic multinodular goiter, and she was diagnosed with CNC. Genetic studies showed no mutation in the PRKAR1A (protein kinase A regulatory subunit 1-α) gene. From then on, she received annual brain magnetic resonance imaging (MRI), abdominal computed tomography (CT), and thyroid US examinations. Her follicular thyroid nodules gradually increased in number and size. Although aspiration cytology samples from the thyroid nodules diagnosed them as class III, thyroid cancer could not be ruled out. The patient underwent a partial thyroidectomy, and the pathological diagnosis was multiple follicular adenomas.The patient was a 69-year-old woman who had undergone the removal of a left atrial myxoma 10 years earlier, at the age of 59. At the time of the operation, thyroid ultrasonography (US) revealed multiple hypoechoic nodules. Thyroid scintigraphy revealed an increased uptake of Careful and frequent evaluation of the thyroid gland should be required for CNC patients due to the potential for carcinoma to develop in the context of a variety of follicular thyroid lesions. Carney complex (CNC) is a familial tumor syndrome first reported by J. Aidan Carney in 1985. The diagnostic criteria include myxoma, endocrine overactivity, and spotty skin pigmentation. CNC is a very rare disease, with about 750 cases worldwide and only 33 cases in Japan having been reported to date. Half of CNC is inherited in an autosomal dominant fashion, and the rest is sporadic. The causative genes of CNC are located at 2p16 (CNC type 2) or 17q2 (CNC type 1), and they are heterogeneous in this disorder [In past reviews, 10% of CNC patients had carcinomatous thyroid lesions. The data also suggests that patients with CNC are more susceptible to the development of thyroid carcinoma than those without, and patients with a PRKAR1A mutation are at a higher risk of developing thyroid tumors . In patiA 69-year-old Japanese female was admitted to our hospital due to thyroid tumors that had been increasing in number and size on routine thyroid ultrasonography (US). On physical examination, the patient was in a good nutritional state and in no acute distress. She had no symptoms of heart failure. There was a palpable soft mass in the thyroid gland, and the patient’s serum CEA level was a little high (5.1 ng/mL). Free T3 and free T4 levels were normal, but her TSH level was high (4.56 μIU/mL). Full blood count, blood chemistry, and serum thyroglobulin levels were normal. She had been taking thiamazole for 25 years for hyperthyroidism.99mTc in the same lesions in the left lobe, being consistent with toxic multinodular goiter. These corresponded to the criteria of CNC, and she was diagnosed with CNC. Genetic studies found no mutation in the PRKAR1A gene. From that time on, she received annual brain MRI, abdominal CT, and thyroid US examinations. The thyroid nodules on thyroid US gradually increased in number and size. Cervical CT showed heterogeneously enhanced masses with microcalcifications in the thyroid gland, but no apparent lymph node swelling was found , pigmented lesions (lentigines and blue nevi), and endocrine overactivity. Diagnostic criteria for CNC were proposed by Stratakis CA in 2001 (Table The major worldwide reviews of CNC –5 and thp = 0.0339). Our patient with no PRKAR1A mutation was diagnosed with CNC at the age of 59 years old.PRKAR1A genetic mutation helps with CNC diagnosis, and it is included in the supplementary diagnostic criteria. PRKAR1A was identified in 2000 as a causative gene of CNC type 1, but not of CNC type 2 [In CNC patients, primary tumors are present synchronously and/or metachronously in multiple organs, including cardiac myxomas, pituitary tumors, breast tumors, adrenocortical tumors, and thyroid tumors, and they require repeated surgeries over several years. Six of the 33 (18%) Japanese CNC patients underwent surgery more than three times. The CNC patients should thus be monitored closely for clinical manifestations of the disease and be aware of necessity for polysurgery to improve prognoses.Worldwide, up to 60% of CNC patients had thyroid nodules; nonspecific cystic disease was present in 75%, follicular adenomas in 25%, and papillary or follicular carcinomas in up to 10% of the cases –11, as sThe evaluation and treatment of thyroid lesions in the 5 Japanese CNC patients are summarized in Table In summary, the characteristics of thyroid lesions in CNC are as follows: (1) the thyroid lesions are present without major symptoms and are usually accompanied by other neoplastic syndromes in various organs, (2) adenomas are the predominant pathology of thyroid lesions, and they tend to increase in size and number during a long follow-up, (3) the age of onset of thyroid lesions is younger in cases with PRKAR1A genetic mutation, and (4) thyroid cancer develops in 6% of CNC patients, but they are well-differentiated cancers, i.e., papillary or follicular cancer, with favorable prognoses.Data on thyroid cancers and non-cancerous thyroid tumors associated with CNC has become increasingly available due to the accumulation of cases and advances in molecular genetic studies in recent years. Thyroid carcinomas can develop in the background of a variety of follicular thyroid lesions in CNC patients despite biochemical euthyroid status and no palpable mass. Long-term follow-up examinations of the whole body, including the thyroid gland, are essential in CNC patients.


P < 0.001) were identified. They were situated in 27 quantitative trait loci (QTLs), including 11 for SE, 6 for PE, and 10 for SSR. It was shown that six of the ten QTLs for SSR were tightly linked to QTLs for SE or/and PE with the expected allelic direction. These QTL clusters could be targeted to improve the outcrossing of female parents in hybrid rice breeding. Our study also indicates that GWAS-base QTL mapping can complement and enhance previous QTL information for understanding the genetic relationship between outcrossing and its related traits.Stigma exsertion and panicle enclosure of male sterile lines are two key determinants of outcrossing in hybrid rice seed production. Based on 43,394 single nucleotide polymorphism markers, 217 cytoplasmic male sterile lines were assigned into two subpopulations and a mixed-group where the linkage disequilibrium decay distances varied from 975 to 2,690 kb. Genome-wide association studies (GWAS) were performed for stigma exsertion rate (SE), panicle enclosure rate (PE) and seed-setting rate (SSR). A total of 154 significant association signals ( In other Asian countries, it is 1.0–1.5 t ha−1. Such low yields in seed production have been a major constraint to hybrid rice dissemination. Improving outcrossing, which is an important and complicated trait in determining female parent seed yield, is thus always a priority in hybrid rice breeding.The availability of affordable hybrid rice seeds is crucial to the success of hybrid rice commercialization. Currently, the average yield of hybrid rice seed production in China is about 2.7 t ha2 in hybrid rice breeding practice. Stigma in a rice spikelet could be exserted from the spikelet when it is flowering, and may be found outside the spikelet when the flowering is over and spikelet is closed, which increases the chance of being fertilized. Panicles of cytoplasmic male sterile (CMS) rice are partially enclosed inside the sheath and this leaves a portion of the spikelet in the panicle unavailable for pollination, with that portion of enclosed panicle being varied across genotypes.Outcrossing of rice sterile parent is a secondary and final trait resulting from flower structure and flowering characteristics. Among multi-traits affecting outcrossing, stigma exsertion and panicle enclosure are usually observed and measured as indicators of outcrossing2 populations9, recombinant inbred lines16, doubled haploid lines19 and backcrossing populations24 These efforts have shown that these traits are of great complexity and strongly influenced by environments, resulting in slow progress in the fine-mapping of QTLs detected in primary populations.In the past two decades, quantitative trait loci (QTLs) for stigma exsertion and panicle enclosure have been mapped by using segregating populations such as F25. In recent years, association mapping has been shown to be a useful tool in the dissection of complex trait variations in many plant species such as rice26, maize27, wheat28, soybean29 and rapeseed30.While traditional QTL mapping using bi-parental crosses has a low resolution rate, which is usually restricted to 10–20 cM, QTL mapping with genome-wide association study (GWAS) has proven to be a promising new approach to localize QTLs in a quite precise position. GWAS is based on the availability of linkage disequilibrium (LD), known as non-random association of alleles at two or more polymorphic loci, which makes it possible to exploit the correlations between genetic markers and phenotypic variation to localize QTLs in fine-scale level33. A more recent study extended the trait to stigma exsertion, showing that GS3, GW5 and GW2 play an important role in the genetic basis of stigma exsertion in rice34. Nevertheless, none of these studies targeted at CMS line which is an essential category of rice cultivars for hybrid rice production. Moreover, it is inconvenient to use traditional population segregation for mining QTLs controlling natural outcrossing in rice for being a self-pollinating species. Association mapping could be a potential tool for connecting genomics and phenomics in CMS rice germplasm, and fill the gap on the genetic basis of natural outcrossing. Using a collection of diverse wild abortive CMS (CMS-WA) lines, the objectives of the present GWAS study were (i) to investigate the genetic architecture of CMS lines developed at the International Rice Research Institute (IRRI) based on a 44 K single nucleotide polymorphism (SNP) genotyping array; (ii) to identify candidate genomic regions controlling outcrossing; and (iii) to characterize the genetic relationship between outcrossing and its related traits.GWAS-base QTL mapping has been successfully employed for a wide range of agronomically important traits in rice, including flowering time, plant height, yield traits and quality traitsP < 0.0001) for the two traits, contributing 26.2% and 50.7% to the total phenotypic variance for SSR and PE, respectively , moderate between TSE and SSE , and low and insignificant between DSE and SSE. This indicates that phenotypic variation of TSE among the 217 rice lines was mainly contributed by DSE, which is expected since DSE was much more variable than SSE. Accordingly, while TSE and DSE were significantly positively correlated with SSR , SSE was not significantly correlated with SSR. In the meantime, SSR was significantly negatively correlated with PE . The correlation of seed-setting rate with stigma exsertion rate and panicle enclosure rate was in accord with the common understanding that enhancing stigma exsertion could facilitate outcrossing whereas panicle enclosure may interfere with outcrossing.Pearson’s product-moment correlation coefficients between the traits tested in 14DS is presented in Supplementary Table indica rice cultivars35. This indicates that our panel has well-captured the abundant genetic variation of the indica rice germplasm.The 217 CMS maintainer lines of rice, including 44 IRRI germplasm accessions and 173 breeding lines, were genotyped using a rice SNP chip. Of the 43,394 SNPs tested, 247 did not have information on physical position and 2,620 showed no polymorphism. Together with 127 SNPs having a proportion of missing data >0.2 and 15,065 SNPs with minor allele frequency <0.05, a total of 18,059 SNPs were excluded for being analyzed. The remaining 25,335 high-performing SNPs had an average of 17 kb per SNP, based on which further analysis was performed. In this rice panel, major allele frequencies varied from 0.4174 to 0.9495 with an average of 0.7230, gene diversity had an average of 0.3718 with a range from 0.0958 to 0.6472, and polymorphism information content (PIC) ranged from 0.0912 to 0.5722 with an average of 0.3052 (Supplementary Table P(K) appeared to be an increasing function of K as the putative K increased from 1 to 8, and no turning point was evident  on heading date, with the entries in group 2 delaying for an average of 5 days in 13WS and 14DS  varied within and among the chromosomes  values matched well with the expected distribution, indicating a low rate of false positive in the detection of significant trait-marker association. A total of 154 significant associations (P < 0.001) were detected, including 83 for SSR, 42 for TSE, 9 for DSE, and 20 for PE  and the dry season of 2014 (14DS). The maintainer lines were sown three days later than the sterile lines. They were grown in a randomized complete block design with three replications. Twenty-one day old seedlings were transplanted using a planting density of 20 cm × 20 cm. Each pair was grown in five rows with 10 plants per row, of which the middle three rows were for sterile lines and the rest for maintainers. Field management followed local recommendations for the two different cropping seasons. Heading date was scored for individual plants and averaged for each sterile line and maintainer line in each replication. Stigma exsertion rate, panicle enclosure rate, and seed-setting rate were measured for each sterile line, with stigma exsertion rate being evaluated in 14DS only while the two other traits were determined in both seasons. For measuring the stigma exsertion rate of a sterile line, five panicles from five plants in each replication were selected on the third day after flowering. After the removal of the spikelets that were not yet flowering, spikelets with no stigma exsertion, single stigma exsertion, and double stigma exsertion Fig.  were cou41, major allele frequency42, gene diversity42 and polymorphism information content (PIC)43 were calculated, and neighbor-joining trees was constructed based on the Cavalli-Sforza’s Chord genetic distance44 and viewed in MEGA version 545. Population stratification was identified using STRUCTURE version 2.3.446 with the parameter settings as described by Xie et al.35. The uppermost level of the assumed number of genetic groups (K) was determined by ΔK47. An individual was assigned to a specific group if its genome composition derived from the group  is above 80%; otherwise, it was assigned to the mixed group. LD was estimated using TASSEL version 548 with permutations of 1,000. By using the R package GAPIT49, kinship was calculated, and association scan was carried out with the compressed mixed linear model50 where Q-value and kinship were included as the fixed and random effects, respectively. The value of P < 0.001 was used to declare a SNP-trait association.By using the PowerMarker version 3.25http://dx.doi.org/10.5061/dryad.1tp63Data for the physical positions of the 43,394 SNPs used in this study are available from the Dryad Digital Repository: Supplementary figures and tables


Bovine leukemia virus (BLV), a common virus of cattle globally, was believed for decades not to infect humans. More recent techniques (in situ PCR and DNA sequencing) enabled detection of BLV in human breast tissue, and determination of its significant association with breast cancer in a US population. Using similar techniques to study 96 Australian women, we report here detection of retrotranscribed BLV DNA in breast tissue of 40/50(80%) of women with breast cancer versus 19/46(41%) of women with no history of breast cancer, indicating an age-adjusted odds ratio and confidence interval of 4.72(1.71–13.05). These results corroborate the findings of the previous study of US women with an even higher odds ratio for the Australian population. For 48 of the subjects, paired breast tissue samples, removed 3–10 years apart in two unrelated procedures, were available. For 23/31 (74%) of these, in which the first specimen was diagnosed as nonmalignant  and the second as malignant, BLV was already present in benign breast tissue years 3–10 years before the malignancy was diagnosed. This is consistent with the supposition of a causative temporal relationship between BLV infection and subsequent development of cancer. Australia has a high female breast cancer incidence. Together with New Zealand, it ranks third in regional age-standardized per capita incidence globally . IncidenArchived formalin fixed paraffin embedded (FFPE) breast tissue specimens, surgically removed 1995–2010, were acquired from the archives of Douglass Hanly Moir-Pathology, a large pathology service in Macquerie Park, New South Wales, Australia. Humans subjects protocol was approved by the Human Research Ethics Committee of the University of New South Wales, Sydney, Australia and the Committee for the Protection of Human Subjects of the University of California, Berkeley. Pathology reports provided information for all subjects on date and type of surgery, age of patient at time of surgery, and the microscopic diagnosis of the specimen. No information was available on race/ethnicity of subjects. Subjects were selected chronologically, starting with the most recent patients undergoing breast cancer surgery and checking the computer base for previous breast surgeries on the same patient which resulted in a normal/benign diagnosis. Of 102 subjects selected, 6 were eliminated due to small tissue size and/or lack of mammary epithelial cells in the specimen. Among the remaining 96 subjects whose surgeries occurred 1996–2013, 64 had paired specimens with the first removed 3–10 years before the most recent surgery. Samples from 16 of these subjects could not be used in the paired specimen analysis because the earlier specimen was too small for analysis or the interval between surgeries was <3 years. However, the later specimen from these excluded pairs were included in the overall case-control analysis of the association of BLV with breast cancer. Tissue sections (5μ thick) were cut by Douglass Hanly Moir Pathology and shipped to the University of California, Berkeley for the PCR-in situ hybridization assay. The three pathology categories used in study analysis were based on microscopic diagnosis: malignant, pre-malignant , and benign  .2, 0.4 mmol/L dNTPs, 1 μmol/L primers , 0.06% bovine serum albumin (BSA) (free of BLV and antibodies to BLV), and 0.053 U/μL Amplitaq Gold DNA Polymerase . The positive control was the FLK  cell line, stably infected with and replicating BLV , and 0.2 μmol/L outer primers for each BLV gene. For the second (nested) round of amplification, 2 μL of the first-round PCR product was added to a new tube of the same reaction mix with inner primers shown above. Cycling conditions for outer primers were: 1 cycle at 95°C for 2 min; 35 cycles of 95°C for 30s, 57°C for 30s, 72°C for 24s, then 1 cycle of 72°C for 5 min. Cycling conditions for inner primers were: 1 cycle at 95°C for 2 min; 35 cycles of 95°C for 30s, 58°C for 30s, 72°C for 22s, then 1 cycle of 72°C for 5 min. Reaction mix and conditions, including MgCl2 concentrations, were previously optimized using the BLV-positive FLK cell line and BLV-negative Tb1Lu cell line [Using thin walled GeneMate PCR tubes , sample DNA (50ng) was added to 50 μL of PCR mix . The requested sample (100 ng/1000 bp length of DNA in 12 μL water and 1 μL of the 5 μmol/L sequencing primer stock solution of the inner primer pair for each genome region) was submitted to the University of California, Berkeley, DNA Sequencing Facility for direct sequencing. DNA for sequencing was obtained from 2–5 amplifications and sequenced at least once in forward (5′) and reverse (3′) directions. Sequences were checked against corresponding electropherograms. Variations from the reference sequence (GenBank accession no. EF600696) were considered valid only if they matched in both forward and reverse directions. Targeted sequences were relatively short because formalin causes DNA breaks, making it difficult to obtain sequences >300 bp .Statistical analysis of age differences between cases versus controls and virus-positive versus virus-negative subjects used two-sided Student t-tests. The STATA “cc” function for case-control data was used to compute unadjusted odds ratio and 95% confidence interval (using Cornfield’s approximation) for association between BLV status and breast cancer . For subAmong this population of Australian women, BLV was present in mammary epithelial cells of 59/96 (61.5%) subjects. Additional evidence that the virus detected was BLV was obtained for 14 specimens that had sufficient material for DNA extraction and sequencing. The sequences of the positive control cell line and 10 of the subjects’ sequences matched the reference sequence exactly for the span of the LTR examined. The sequences of 4 subjects contained at least one base substitution for this segment of the LTR examined. These are shown in A case-control study design was used to examine the association between exposure to BLV and the development of breast cancer. Detection of BLV DNA in mammary epithelial cells, the cell type from which most breast cancers arise, served as a biomarker of BLV infection (exposure) and a diagnosis of breast cancer or premalignant breast disease served as the measure of disease outcome. Statistical analysis indicated an initial unadjusted odds ratio (OR) of 5.68, but since the age-range of the subjects at the time of surgery was 16–83 years and the mean and median ages of cases and controls were significantly different , age-adjTo test whether temporal exposure to BLV preceded development of breast cancer, paired specimens from individual subjects were analyzed. For 48 subjects, an archived normal breast tissue was available from breast surgery performed 3–10 years previously (S2 Table). The time interval between specimens did not differ significantly between women who developed cancer versus women with no breast cancer history . The proFor the past ≈30 years, BLV has not been regarded as a threat to humans, e.g. “BLV is not transmissible to humans, and no human disease has ever been attributed to BLV.” . HoweverResults of this case-control study in Australian women from the greater Sydney area were consistent with results for US women , detectitax region [gag, pol, and env regions, coding for capsid protein, polymerase, and envelope protein, respectively, frequently become deleted as the malignancy progresses, but tax deletion occurs less frequently [One limitation of this study is the sample size, constrained by the scarcity of paired breast tissue specimens from individual donors. This resulted in a small sample size for each of the individual categories presented in x region  for the equently .tax region. Sequencing indicated a few base substitution polymorphisms, which differed from each other and from the positive control cell line, suggesting that cross-contamination was unlikely. Standard liquid phase PCR (L-PCR), which requires initial tissue digestion to extract DNA, is prone to molecular contamination. We reduced this probability by best practices described previously in detail [Primers used for DNA sequencing were from the BLV LTR promoter region, also rarely deleted during tumor progression, but having more sequence variation than the n detail . Also, tn detail .A concern occasionally raised in discussions of virally caused human cancers is that presence of any virus in the breast may not relate to causation of breast cancer, but instead could represent an opportunistic virus invasion of tissue already transformed to malignancy, with virus presence enhanced by the already accelerated division of the malignant cells. In this study, we were able to demonstrate, using paired breast tissues from the same donor removed at least 3 years apart, that in 23/31 (74.2%) subjects diagnosed with breast cancer, BLV infection of benign or premalignant breast tissue was present years before the breast cancer diagnosis (Fisher exact test result of p≤ .05). This argues against the idea of viral invasion of already malignant cells. While this does not prove BLV causation of breast cancer, it is consistent with a potential role of BLV in initiating the malignant process rather than infecting already malignant tissue.It is not known how humans acquire infection with BLV. It could be from other humans already infected with BLV or from consumption of unpasteurized dairy or undercooked beef products from BLV infected cattle. Only about 5% of BLV infected cattle develop lymphosarcoma, and in most countries their products cannot be marketed. Although the remaining 95% of infected cattle may be marketed, they have lower milk production and incur increased costs of diagnostic testing. These economic issues, and the potential loss of international markets due to the success of the BLV eradication program in the European Union, provided an incentive for Australian farmers to voluntarily progress toward a statewide and eventually nationwide program for the eradication of BLV in dairy herds . The proBovine leukemia virus is significantly associated with breast cancer in a population of Australian women, and was present in some breast tissues 3–10 years before the cancer was diagnosed.S1 File.(XLSX)Click here for additional data file.S2 File.(XLSX)Click here for additional data file.


Antarctic ice-covered lakes are exceptional sites for studying the ecology of aquatic fungi under conditions of minimal human disturbance. In this study, we explored the diversity and community composition of fungi in five permanently covered lake basins located in the Taylor and Miers Valleys of Antarctica. Based on analysis of the 18S rRNA sequences, we showed that fungal taxa represented between 0.93% and 60.32% of the eukaryotic sequences. Cryptomycota and Chytridiomycota dominated the fungal communities in all lakes; however, members of Ascomycota, Basidiomycota, Zygomycota, and Blastocladiomycota were also present. Of the 1313 fungal OTUs identified, the two most abundant, belonging to LKM11 and Chytridiaceae, comprised 74% of the sequences. Significant differences in the community structure were determined among lakes, water depths, habitat features , and nucleic acids (DNA vs. RNA), suggesting niche differentiation. Network analysis suggested the existence of strong relationships among specific fungal phylotypes as well as between fungi and other eukaryotes. This study sheds light on the biology and ecology of basal fungi in aquatic systems. To our knowledge, this is the first report showing the predominance of early diverging lineages of fungi in pristine limnetic ecosystems, particularly of the enigmatic phylum Cryptomycota. However, there is increasing evidence that questions this limited fungal diversity in aquatic ecosystems. First, sampling efforts of fungi in aquatic habitats are low in comparison to terrestrial environments, and second, molecular analyses of environmental DNA samples using next-generation sequencing have revealed a large diversity of fungi whose sequences are not associated with known cultivated representatives in the reference databases4. In fact, an undetermined number of aquatic fungi are still missing in the taxonomy of the Fungal Kingdom, especially members of the early diverging lineages within Cryptomycota and Chytridiomycota6.The diversity of fungi in freshwater, brackish, and marine environments has been suggested to be low in comparison to terrestrial habitats, with estimates of 3,000–4,000 species in total, composed mainly of members of Ascomycota, Chytridiomycota and to a lesser extent Basidiomycota8. Recent studies showed a high degree of fungal diversity in European freshwater lakes, where members of the basal phyla Chytridiomycota and Cryptomycota dominated the fungal community composition11. Similar results highlighting the importance of these groups have also been obtained in marine environments15. In particular, the early diverging fungal lineages have some physiological advantages for inhabiting aquatic ecosystems, including their mobility and capacity to parasitize numerous phytoplankton species such as diatoms, green algae, dinoflagellates and cyanobacteria17.Aquatic fungi perform crucial roles in nutrient cycling, such as contributing to the flow of carbon through aquatic food webs and overall ecosystem functioning. Hence, their biodiversity and ecological role must be evaluated in detail for a comprehensive understanding of aquatic ecosystemsIn this work, we studied the diversity and community composition of fungi in five perennial ice-covered lake basins of the McMurdo Dry Valleys, Southern Victoria Land, Antarctica. Particularly, we analyzed samples from Lake Miers, Lake Fryxell, Lake Hoare, and the West and East lobes of Lake Bonney Fig. . These l19. The landscape is composed of glaciers, mountain ranges, ice-covered lakes, meltwater streams, arid patterned soils and permafrost, sand dunes, and interconnected watershed systems20. The landscape harbors numerous lakes with permanent ice caps of >3.5 m thickness that prevent wind mixing and significant nutrient inputs21.The region of the McMurdo Dry Valleys is a polar desert with annual precipitation of 50 mm water equivalents and an average air temperature near −20 °C; this region constitutes the largest ice-free area in Antarctica with approximately 30% of the ground surface free of snow and ice23. The planktonic and benthic communities within these lakes are dominated by “microbial loop organisms” whose photosynthetic activity is a primary source of dissolved organic carbon. Previously, some studies examined the diversity of bacteria, archaea, protists, and ciliates, showing that communities are strongly stratified by depth27. In relation to fungal diversity, some works described fungal communities in surrounding soils30 or reported on the isolation of fungi using only cultivation-dependent methods32. Since only a minor fraction of the fungi in this aquatic ecosystem is cultivable, the community assembly and the role of fungi in these pristine lakes remain largely unknown.The selected lakes form a hydrologically closed-basin system characterized by a strong and physically stable chemical stratification down the water column (meromictic) with oxygen-rich surface layers and sub-oxic and saline deep waterIn this work, we used Illumina sequencing of the 18S rRNA gene to characterize the fungal populations inhabiting five ice-covered lake basins in geographically isolated areas of the McMurdo Dry Valleys, Antarctica. We also explored their relationships with other detected eukaryotes as the effect of variables, such as lake type, depth, and habitat type , on the community composition. We hypothesized that water column stratification would structure fungal community diversity and expected the fungal community structure to be composed of many undescribed taxa dominated by early diverging lineages of the Fungal Kingdom.20. The East lobe of Lake Bonney is characterized by fresh to brine waters that reach 114 mS cm−1 below the chemocline and water temperatures between 4.8 °C and −1.3 °C. The West lobe of Lake Bonney is influenced by the inflow of seasonal surface freshwater melt and cold (~−10 °C) subglacial brines from the Taylor Glacier33, producing a very sharp chemocline that reaches 80 mS cm−1 in the deeper layer and a vertical temperature range between 2.5 and −4.4 °C. Chloride is the dominant ion in Lake Bonney, leading to strong vertical stratification; pronounced variations also occurred for Br+, Ca2+, Cl−, Mg2+, Na+, and SO42−  with homogeneous temperatures (range 0. 6 to 0.2 °C). Lake Hoare is fed primarily by seasonal melt streams emanating from the Suess and Canada Glaciers22. Lake Fryxell (depth ~20 m) ranges from fresh surface waters to deep waters that reach 8.5 mS cm−1, whereby water temperatures range between 2.8 and 0.9 °C. This lake is fed by numerous glacially fed streams and has the highest volumetric phytoplankton biomass (20 µg Chl l−1) of all the lakes due to the inorganic nutrient and organic matter input from these streams together with the diffusive flux of these constituents from deeper waters35. The water below 10 m in Lake Fryxell is highly anoxic and contains sulfide levels in excess of 1.2 mM36. Lake Miers (depth ~19 m) is also a freshwater lake with maximum conductivities reaching 1.8 mS cm−1 near the bottom and relatively higher water temperatures (range 1.4 to 5.4 °C). This lake is located at a higher altitude in the Miers Valley, approximately 50 km south of Taylor Valley, and is fed by seasonal melt streams fed by the Miers and Adams Glaciers37. Lake Miers is the only lake in our study that has an outflow to the sea; all other lakes are hydrologically terminal and lose water only to ablation from the surface of the permanent ice cover. Biological production in Lake Bonney has been shown to be highly phosphorus deficient, whereas the other lakes are deficient in both nitrogen and phosphorus38.Vertical profiles of temperature, conductivity, chlorophyll and dissolved oxygen for the five chemically-stratified and permanently ice-covered basins studied are shown in Fig. P = 7.86 e-07), depth layers (P = 0.011), and habitats . Concerning depth, deep waters that form the monimolimnia had a higher proportion of fungi than the overlying mixolimnia and chemocline regions. In relation to salinity, freshwater contained a significantly higher proportion of fungi than brackish waters. Conversely, we found no significant differences  between the size fractions (particle associated > 5.0 µm and free living: 0.2–5.0 µm), nor between the proportions of fungi obtained from DNA or RNA samples .From the 4.99 million 18S rRNA sequences analyzed, 787,937 were classified as fungi (Table Glaciozyma sp. and Mrakia sp. (Basidiomycota) were more abundant, and extensive cyanobacterial mats were observed.Cryptomycota and Chytridiomycota were the most abundant fungal taxa in the McMurdo Dry Valley lakes. Cryptomycota represented ca. 72% of the fungal reads and ca. 44% of the fungal OTUs, while Chytridiomycota represented ca. 26% of the fungal reads and ca. 40% of the OTUs , depth layers (P = 0.0009), habitat , DNA vs. RNA derived samples (P = 0.006), and between particle size fractions (P = 0.018). However, pairwise comparisons revealed no significant differences between the composition of the East and West lobes of Lake Bonney (P = 0.460) or between chemoclines and the deep layers , with Lake Miers presenting the highest values , only between each lake and Miers. A similar situation occurred for estimates of the Shannon index and Pielou’s evenness. In relation to the habitat, Kruskal-Wallis tests showed that freshwaters presented a significantly higher species richness than brackish waters (P = 0.0001) but a lower evenness (P = 0.0016). Furthermore, we observed that with an increase in depth, there was a significant difference (P = 0.0079) between the richness values of both habitats.We determined significant differences in fungal richness among the five lakes studied , the most abundant primary producer in this ecosystem. Other associations include chytrids  and a heterotrophic stramenopile (Parahysomonas); LKM15 (Cryptomycota) and unicellular green algae (Prasinophytae); Glaciozyma (Basidiomycota) and the flagellate heterotroph Heteromita (Rhizaria); Blastocladiomycota and green algae Choricystis; Harpellales (Zygomycota), Paratrimastix (Chloropastida) and Chlorophyta. Because of this, and based on the nature of the organisms with which the fungi were interacting, we may infer that they can display roles as parasites, preys or saprophytic commensals. In addition, we detected that some fungi presented a high degree of connections with other organisms in the ecosystem, including Hyaloraphidium, LKM15, Chytridium and Harpellales.We used network analysis to explore the relationships between the eukaryotic microorganisms co-habiting the lake basins Fig. . This te15. Hence, this finding led us to hypothesize that early diverging fungi might dominate fungal communities in the water column of undisturbed aquatic ecosystems, while members of Dikarya can more frequently dominate terrestrial habitats or aquatic environments affected by terrestrial input and anthropogenic activities.This is the first report highlighting the predominance of Cryptomycota among fungal communities in an aquatic ecosystem. This predominance occurred in lake basins located in the Taylor and Miers Valleys of Antarctica, which are perennially ice-covered lakes presenting a minimum level of human disturbance and are characterized by strong physical and biochemical vertical gradients. The dominance of other early diverging lineages of fungi belonging to the phylum Chytridiomycota, has also been reported previously in communities of some specific freshwater and marine systems37. Other variables, such as light, have an effect on the high rates observed for thymidine uptake, which is considered a proxy of the phytoplankton primary productivity and might have also played a role, but this still requires further investigation39. We also observed an increased richness of fungi in the deeper layer of Lake Fryxell. In this case, despite the anoxic conditions, the higher phytoplankton biomass, DOC, and nutrient inputs from surrounding streams may have contributed to the higher values of richness and diversity observed35.Lake Miers contained the highest proportion of fungi, the highest species richness in all the depth layers, but the lowest values of evenness. This suggests that despite the many taxa inhabiting this lake, some species, mainly assigned to Cryptomycota, presented an unequal abundance distribution. The high richness in this lake could be explained by a combination of factors, including the freshwater habitat, the relatively warmer temperatures, and the location in a different valley with a higher altitude25. In addition, the effect of salinity on the mycoplankton was reported in other aquatic systems, showing higher diversity during periods of reduced salinity40. Salinity, however, might also provide additional niches where fungal groups can specialize and coexist42. In this regard, lakes Bonney and Fryxell provide ideal conditions to further investigate the mechanisms used by fungi to adapt to gradients of osmotic pressure , high ion concentrations, and temperatures below 0 °C.Aside from the lake ecosystem (site), the interaction of two factors shaped fungal richness and diversity in the McMurdo Dry Valley lakes: salinity and depth. Accordingly, higher values of richness are expected in freshwaters with respect to brackish waters or in the monimolimnion with respect to the overlying mixolimnion and chemocline regions. However, it is difficult to separate the specific effect of salinity from the effect of depth because these factors were closely related, since salinity varied with depth. A previous work also showed the effect of depth stratification on the bacterial communities of Lakes Fryxell and Bonney10. The comparison of the fungal composition of the samples derived from RNA or DNA, obtained specifically from Lake Bonney, suggests that the fungal communities in this lake were effectively active, but they can vary with respect to the passive communities over time.The fungal communities of the Antarctic ice-covered lakes studied were characterized by the dominance of a few highly abundant members of Cryptomycota and Chytridiomycota coexisting with a large number of low-represented organisms. In addition, we determined significant differences in the community composition among lakes, depth layers, and habitat. This suggests the development of certain niche preferences in some fungal taxa. The partitioning of the fungal communities according to different environmental parameters also has been observed in other temperate lakesGeminigera, the prominent cryptomonad primary producer in the lakes. In this case, we hypothesized that this is a parasitic relationship, since many members of Chytridiacea are obligate or facultative parasites44. Although co-occurrence does not necessarily imply a relationship, previous studies also showed Chytridiomycota as common phytoplankton parasites in both freshwater and marine ecosystems5. Furthermore, we were able to confirm a chytrid infection on algae by visualization with the fluorescence microscope . A summary of the chemical and biological parameters used in this study are presented in supplementary information  was measured on acidified samples with a Shimadzu TOC-V analyzer; phytoplankton primary production (PPR) was determined on light and dark bottles amended with 14C-bicarbonate and incubated in situ for 24 h47; bacterial production (TDR) was measured as 3H Thymidine and 3H Leucine incorporation49, and chlorophyll concentrations (CHL) were obtained with a submersible FluoroProbe  spectrofluorometer.All background environmental data and methods are available on the MCM LTER website . The RNA was converted to cDNA with a One-Step RT-PCR Kit  according to the manufacturer’s instructions. We amplified the V7 and V8 regions of the 18S rRNA gene using primers FF390 (5′-CGATAACGAACGAGACCT-3′) and FR1 (5′-AICCATTCAATCGGTAIT-3′), which showed good coverage of all fungal lineages51. For the 25 µl PCR reaction, we used a proof reading enzyme  and 40 ng DNA (or cDNA) as a template with the following conditions; 95 °C for 3 min initial denaturation followed by 35 cycles at 95 °C for 45 s, 52 °C for 1 min, 72 °C for 1 min, and a final extension at 72 °C for 5 min. The 96 resulting amplicons (~350 bp) went into the library preparation for Illumina MiSeq sequencing according to the protocol presented by the Illumina customer letter for 16 S sequencing with custom primers  and the Nextera index kit . The samples were sequenced on a MiSeq sequencer  with v3 2 × 300 nt chemistry. Sequences were demultiplexed with flexbar resulting in 6.4 M sequences. The sequence data were deposited into the NCBI Sequence Read Archive .DNA from the microorganisms in the filters was extracted using a phenol-chloroform protocol53. Subsequent processing included alignment of reads against the eukaryote subset (n = 16209) of the SILVA 123 data set54, pre-clustering (1 mismatch threshold), chimera removal with UCHIME55, and classified with SILVA 123, using the k-nearest neighbor algorithm with a minimum cutoff of 60%. We assigned sequences to OTUs using a split method based on taxonomy56. For this, sequences were clustered at the genus level in an initial step, and then all sequences within each taxa were assigned to OTUs according to the Vsearch method using a 0.03 distance cut-off57. After the first taxonomic classification obtained with the Mothur pipeline, we performed a second manual curation of the assignments. For this, a representative sequence of each OTU was further compared against the SINA Alignment and Classify service58. Notably, we found that many OTUs previously assigned as “Porifera”, “Unclassified Opisthokonta” or “Unclassified Nucletmycea” that are normally omitted in further analyses belonged to the Fungal Kingdom and contained large numbers of sequences. Other unclassified sequences at higher taxonomic levels were also compared against those in GenBank using the BLAST tool. The addition of this second step was fundamental for obtaining a proper and accurate taxonomic assignation of the eukaryotic organisms.The 18S rRNA gene sequence reads were paired and quality filtered using Mothur 1.33.3 following the SOP tutorial for MiSeq sequencing data59. To limit experimental biases due to sequencing depth, we removed samples whose total sequencing depth fell below the first quintile (1393 sequence reads), allowing us to keep an adequate number of sequences per sample but avoiding the loss of valuable samples of the lakes. This process resulted in the selection of 70 samples with nearly 4.99 million eukaryotic sequences. To estimate differences in the relative proportion of fungi and in the diversity indices, we used the non-parametric Kruskal-Wallis test, while pairwise comparisons were performed using Wilcox and Dunn’s tests (with Bonferroni adjustment). For further statistical tests, we normalized the data by converting the OTU counts (without singletons) into relative abundances. We used the Vegan package60 to perform non-metric multidimensional scaling (NMDS) analyses, permutational analysis of variance , the Analysis of Multivariate Homogeneity of group dispersions and to calculate alpha diversity values. For the network analysis, we selected the 62 most abundant eukaryotic genera (17 classified as fungi), which corresponded to 98.2% of the total number of eukaryotic sequences. The analysis was performed with the SpiecEasi pipeline using the Meinshausen-Bühlmann neighborhood selection method61 and further visualized with igraph package using a Fruchterman-Reingold layout62.Data processing, visualizations, and statistical analysis were performed in RSupplementary Information