The goal of this study was to explore conceptual benefits of characterizing delineated target volumes based on surface area and to utilize the concept for assessing risk of therapeutic toxicity in radiosurgery.12Gy, V14Gy and V16Gy corresponding to received dose of 12 Gy, 14 Gy and 16 Gy, respectively, were computed and analyzed. Additionally, V12Gy and V14Gy volumes and values for seven prospective toxicity variables were recorded for 100 meningioma patients after Gamma Knife radiosurgery. Multivariable stepwise linear regression and best subset linear regression analyses were performed in two statistical software packages, SAS/STAT and R, respectively.Four computer-generated targets, a sphere, a cylinder, an ellipsoid and a box, were designed for two distinct scenarios. In the first scenario, all targets had identical volumes, and in the second one, all targets had identical surface areas. High quality stereotactic radiosurgery plans with at least 95% target coverage and selectivity were created for each target in both scenarios. Normal brain volumes V12Gy, V14Gy and V16Gy also increase. In the 100-meningioma patient cohort, the best univariate model featured tumor surface area as the most significantly associated variable with both V12Gy and V14Gy volumes, corresponding to the adjusted R2 values of 0.82 and 0.77, respectively. Two statistical methods converged to matching multivariable models.In a phantom study, for the constant volume targets, the volumes of 12 Gy, 14 Gy and 16 Gy isodose clouds were the lowest for the spherical target as an expected corollary of the isoperimetric inequality. For the constant surface area targets, a conventional wisdom is confirmed, as the target volume increases the corresponding volumes VIn a univariate model, target surface area is a better predictor of spilled dose to normal tissue than target largest dimension or target volume itself. In complex multivariate models, target surface area is an independent variable for modeling radiosurgical normal tissue toxicity risk. Surface area is an essential building block for numerous theoretical and technological concepts in mathematics, physics, chemistry, biology, cosmology, and other natural sciences. The motive for this work was to employ the concept in radiation therapy setting and to evaluate risk of radiosurgical toxicity from an unexplored vantage point of target surface area.In radiation oncology, the ICRU standards for therapeutic use of radiation–7 necessThus far, the mathematical concept of surface area has not been utilized in modern radiotherapy treatment planning as target surface area is neither measured nor considered for plan quality evaluations and toxicity assessments. This manuscript is an effort in exploring conceptual benefits of representing delineated target volumes based on surface area. The concept was validated in a stereotactic radiosurgery setting utilizing Leksell GammaPlan (LGP) as the treatment planning platform.Vrind of thickness d surrounding spherical target of radius r is equal to the volume of a larger sphere V(r+d) minus the internal sphere volume V(r):d is much smaller than r, i.e., d/r is small, the above equation in linear approximation is equal to 4πr2∙d, which is the surface area of the original sphere multiplied by the thickness d. Intuitively, the layers around tumor are associated with possible radiotherapy treatment complications. Thus, the working hypothesis is that target surface area is a toxicity risk predictor in radiosurgery.The volume of normal tissue around a tumor, analogous to onion layers, can be imagined as rinds of tissue where each rind corresponds to a specific isodose line increment. The volume of the rind of tissue DICOManTX [A radiotherapy treatment planning feature crucial for this study is the ability to measure surface area of delineated targets and critical structures. This requirement is quite basic yet the feature is nonexistent in contemporary commercial treatment planning systems. For that reason, an in-house multifunction software ICOManTX , 18 was DICOManTX to Leksell GammaPlan (LGP) version 11.03. The phantom was delineated utilizing the LGP image-based segmentation tool which provided automatic skull definition. Every object was centrally placed inside of the spherical phantom and designated a target structure. The largest cardinal axis for every target was oriented along the z-axis, i.e., along Gamma Knife in-and-out direction. Such target placement takes advantage of the machine design and source arrangement which results in the sharpest beam penumbra in z-plane.The phantom and the objects were exported from The aim was to develop eight individual radiosurgery plans of equivalent plan quality. The treatment planning goals were set at a minimum 95% target coverage accompanied with at least 95% selectivity as defined by Paddick. However12Gy, V14Gy and V16Gy corresponding to received dose of 12 Gy, 14 Gy and 16 Gy, respectively, represented a measure of treatment toxicity risks in this study. In particular, the V12Gy, V14Gy and V16Gy brain volumes for a spherical target were used as reference or normalization values for toxicity risk comparisons between all phantom plans.Ideally, clinically recorded long term toxicity data would provide benchmark metrics in any analysis identifying significant outcome variables. In reality, published studies differ in many aspects including prescription dose, definition of volume, completeness of follow up, definition and evidence of radiation toxicity. This information is not available for a phantom study. However, treatment plan toxicity risks can be evaluated as a volume of surrounding normal tissue receiving undesired spilled dose from the neighboring treatment site(s). A normal brain volume, uninvolved by tumor, receiving a dose of 12 Gy or higher is commonly considered as a benchmark complication predictor for a large number of conditions. In addiDICOManTX software and for statistical evaluations with SAS/STAT and R software. The selection criteria were simple and included only single fraction meningioma patients treated with a dose of 15 Gy prescribed to 50% isodose line at the periphery of the target. The 15 Gy was selected as this prescription dose was used most frequently within institutional database. These treatments involved de novo patients as well as patients who had residual disease or recurrence after surgical resection. The rationale for including just one prescription dose was to achieve evaluation of equivalent clinical conditions. Otherwise, the prescribed dose differences would need to be accounted by scaling or renormalizing the plans, which would be different from the actual delivered plans. One hundred patients met the inclusion criteria out of 175 Gamma Knife patients treated from November 2011 to July 2018. The 100-patient cohort comprised of 77% female and 23% male patients with a median age of 60.1 years (range 21 to 92 years). Based on the World Health Organization (WHO) classification scheme for meningiomas, 87% of patients were Grade 1, 11% Grade 2 and 2% Grade 3. Radiological features of contrast enhanced MRI images were used for diagnosis determination in 63 patients, while the remaining 37 patients had both histopathological and imaging evaluations. The retrospective statistical studies did not include any interaction or intervention with human subjects and the anonymized data did not include any identifiable patient information. As the data had already been obtained through a standard clinical practice, the research ethics committee or institutional review board approval was not required.Ultimately, an a priori sample size of 100 anonymized clinical patients were selected for tumor surface area measurements utilizing SAS/STAT version 9.4 [12Gy and V14Gy volumes. Next, a statistical computing platform R version 3.5.1 aka Feather Spray [12Gy and V14Gy volumes by utilizing the adjusted coefficient of determination R2. An adjusted coefficient of determination, in contrast to an unadjusted value, penalizes the model for further variable insertion. Comparable models were ranked based on increasing value of adjusted R2 with a maximum value of 1 indicating perfect correlation between linear fit and data. The simplest model is one variable model, following is adding one more variable to the assortment and so forth. The analysis included all variables, however, for simplicity and clarity, the presented results were limited to the best two models for a given number of variables.Two different software packages and seven variables were used for statistical investigations. The studied variables included target surface area, target volume, target largest linear dimension, coverage, selectivity, gradient index as well as number of shots or isocenters. Of particular interest was inclusion of target surface area as novel and uncharted variable. Univariate and multivariable linear regression evaluations were performed with Cary, NC). Forwardston, MA) was usedDICOManTX software was used to create DICOM objects in this study. The phantom and two sets of targets, one set of identical volumes and another set of identical surface areas were imported to Leksell GammaPlan. On average, LGP measured target volumes in DICOManTX values, with the greatest outlier of 2.6% for the smallest target volume. These differences are expected since there is no universally adopted or recommended method of determining volumes from contours, consequently the proprietary algorithm differences yield slightly different values.60Co sources distributed in a semispherical halo.Consistent high-grade plan quality was imposed to support meaningful comparisons between the plans. As listed in 12Gy)Object/(V12Gy)Sphere, (V14Gy)Object/(V14Gy)Sphere and (V16Gy)Object/(V16Gy)Sphere showed increased ratios for all isodose levels observed, as indicated in Treatment plans were compared in terms of the volume of undesired spilled dose to the normal brain. For the constant volume targets in 12Gy)Object/(V12Gy)Sphere, (V14Gy)Object/(V14Gy)Sphere and (V16Gy)Object/(V16Gy)Sphere displayed decreased ratios for all isodose levels observed, as shown in For the constant surface area targets, the volumes of 12 Gy, 14 Gy and 16 Gy isodose clouds were the highest for the spherical target, see 3 (range 0.4 to 20.3 cm3) and the corresponding median tumor surface area was 19.0 cm2 (range 2.8 to 96.6 cm2).Univariate and multivariate linear regression was performed on 100 meningioma patients as described in the methods section. Each patient received a prescribed therapeutic dose of 15 Gy to 50% isodose line. The median tumor volume was 4.0 cm12Gy and V14Gy volumes. The univariate model results are presented in 12Gy and V14Gy volumes. This is in accord with the highest corresponding adjusted R2 values of 0.82 for V12Gy and 0.77 for V14Gy. The second-best single variable model was tumor largest dimension, with the adjusted R2 values of 0.75 and 0.73, corresponding to V12Gy and V14Gy volumes, respectively. Note that the tumor largest dimension denotes measurements in any of three cardinal views, i.e., axial, coronal or sagittal plane, not the true diagonal largest dimension in a volume.The simplest models, derived by univariate linear regression analysis demonstrated that tumor surface area, tumor largest dimension and tumor volume are significantly associated with both VSAS/STAT forward and backward stepwise linear regression analysis are summarized in p<0.0001), volume (p<0.0001), selectivity (p<0.0001), gradient index (p = 0.0055) and number of shots (p = 0.0003) were significantly associated with V12Gy volume. Tumor surface area, volume, and gradient index and number of shots were positively associated with V12Gy volume while selectivity was negatively associated with V12Gy volume. Tumor largest dimension and coverage were not significantly associated with V12Gy volume. In the same way, tumor surface area (p = 0.0002), volume (p<0.0001), selectivity (p<0.0001), and tumor largest dimension (p<0.0001) were significantly associated with V14Gy volume. Tumor surface area, volume, and largest dimension were positively associated with V14Gy volume while selectivity was negatively associated with V14Gy volume. Gradient index, number of shots and coverage were not significantly associated with V14Gy volume.The results of R best subset linear regression analysis limited to the best two models for a given number variables are presented in Figs R2 values indicating better but more complex models relative to a single variable model. For instance, in 12Gy volume with five variables and an equal adjusted R2 value of 0.93. These two models can account for 93% variance in V12Gy volume with four shared variables: tumor surface area, volume, selectivity and gradient index, while the fifth variable is either number of shots or tumor largest dimension. This is in excellent agreement with the stepwise linear regression result for V12Gy in R2 value of 0.89 was associated with two models of V14Gy volume involving four variables. In this case three mutual variables are tumor surface area, volume and selectivity, while the fourth variable is either tumor largest dimension or number of shots. Compared to the V14Gy result in The findings of 3 treated with identical prescription dose of 15 Gy prescribed to 50% isodose line at the periphery of the target. However, the normal tissue toxicity risk is likely to be very different for these two patients due to differences in spilled dose volumes. The surface area of cavernous sinus meningioma was approximately two times larger relative to the tentorial meningioma, 34.9 cm2 versus 16.9 cm2, respectively. Consequently, the undesired dose spillage to surrounding normal tissue was also very different. For example, V14Gy was 5.4 cm3 for the cavernous sinus target while it was 1.1 cm3 for the tentorial target, i.e., approximately 5 times larger normal tissue volume was irradiated. Toxicity increases rapidly once the volume of the brain exposed to >12 Gy is >5–10 cm3[Intrinsic properties of surface area emerged as fundamental features in numerous scientific settings. In chemistry, the rate of a chemical reaction increases as the surface area of a substance increases. In biolSAS/STAT was used to identify statistically significant predictors of normal brain dose. R was used to study relative contribution of variables for a given model in an effort to identify clinically applicable set of parameters as a compromise of simplicity versus greatest predictive value.Another instance can be added now to the aforementioned examples. In radiotherapy, surface area of a target determines how much normal tissue is exposed to radiation. Target surface area is a fundamental way of characterizing the “size” of a tumor and thus the risk of toxicity to the surrounding brain. The most common metrics currently in use, e.g., target largest dimension and volume, have never been prospectively validated as predictors of toxicity, but are routinely used to make clinical decisions regarding prescribed dose. A univariate model demonstrated that target surface area is a better predictor of spilled dose to normal tissue than target largest dimension or target volume itself. Furthermore, in more complex multivariate models, target surface area maintains the status of a statistically significant variable although it may not necessarily be as a leading term. This reveals that target surface area could be a useful supplement to currently used metrics to further stratify risks of toxicity. The utilization of two different statistical software packages for clinical data analysis provided essential insight for modeling decisions and consequent interpretations. A phantom study was designed and used as proof of concept and to provide unique perspective on possible treatment toxicities from surface area vantage point. Note that the plan for spherical target is in fact the same plan belonging to both constant volume and constant surface area sets. This dual affiliation for spherical target was purposely constructed to link two different categories of plans. It is important to point out that the values presented in 12Gy, which was used in this study as a benchmark complication predictor[The limitations of the study included selection bias inherent to retrospective approach, single-institution experience and lack of quantifiable toxicity data. The large majority of the selected cohort were low grade meningiomas. In general, the literature focusing on Gamma Knife meningioma radiosurgery are in accord that treatments are safe, effective and well tolerated. Moreovepredictor. UltimatThe phantom study and the linear regression analysis of clinical dataset offer several take home messages.12Gy, V14Gy and V16Gy also increase. This is a direct consequence of the isoperimetric inequality which ascertains that a spherical target has the minimal surface area for a given volume.First, for the same target volumes, as the target surface area increases, the corresponding irradiated normal brain volumes VSecond, the constant target volume scenario provides a valuable insight why two patients with equal tumor volumes, and all other treatment parameters identical, can still exhibit different normal tissue complication probabilities. Diverse and complex patient specific biological circumstances present an attractive possible explanation. However, the simplest explanation for an adverse effect, or the lack of it, could be the variance in surface area of two targets.12Gy, V14Gy and V16Gy also increase.Third, for the same target surface areas, a conventional wisdom is confirmed, as the target volume increases, the corresponding irradiated normal brain volumes VForth, the constant target surface area scenario offers remarkably different view on toxicity which would supplement current clinical knowledge and experience. In an imaginable novel approach, radiotherapy targets would be characterized and stratified by surface area. Since target surface area is independent of plan quality an a priori maximum treatment toxicity could be estimated purely based on surface area measurements, before an actual treatment plan is even made.As a final remark, considerations of target surface area could introduce a new avenue for further development of treatment planning dose optimization algorithms.S1 File(PDF)Click here for additional data file.


BRCA1/2 negative patients with eoBC and negative family history, and 60 healthy controls. Hypermethylation was determined, within each promoter, by comparing the patient’s mean methylation value with thresholds based on one-sided 95% bootstrap confidence interval of the controls’ mean. Three patients had hypermethylated results, two at BRCA1 and one at RAD51C. Analyses on tumor tissue from the patient exceeding the highest threshold at BRCA1 revealed a mean methylation >60% and loss of heterozygosity at chromosome 17q. The patient hypermethylated at RAD51C showed low methylation in the tumor sample, ruling out a role for methylation-induced silencing in tumor development. In isolated eoBC patients, BRCA1 constitutive promoter methylation may be a predisposing event. Further studies are required to define the impact of methylation changes occurring at BC-predisposing genes and their role in tumorigenesis.Early age at onset of breast cancer (eoBC) is suggestive of an increased genetic risk. Although genetic testing is offered to all eoBC-affected women, in isolated cases the detection rate of pathogenic variants is <10%. This study aimed at assessing the role of constitutive promoter methylation at BC-associated loci as an underlying predisposing event in women with eoBC and negative family history. Promoter methylation at 12 loci was assessed by the MassARRAY technology in blood from 154  Breast cancer (BC) is the most common malignancy and the main cause of cancer-related deaths in women worldwide . While mThe risk of developing BC is influenced by multiple factors, including genetic, lifestyle and environmental factors. Although only 5–10% of all cases present a strong genetic predisposition, genetic factors are expected to play a role in up to 30% of BCs ,5. EarlyBRCA1 and BRCA2 account for 20–30% of all suspected hereditary BC (HBC) and harbor most of the currently identifiable high-risk variants. The detection rate (DR) of BRCA1/BRCA2 pathogenic variants depends on the characteristics of probands/families. Although a young age at diagnosis is commonly associated with a higher probability of carrying pathogenic variants, several studies highlighted that the DR might be even lower than 10% in patients with early onset BC (eoBC) and negative FH [TP53, PTEN, STK11, CDH1 and PALB2 genes. Moderate-penetrance variants have been described in about 5% of cases, although their contribution to HBC appears to be limited [Among known genetic susceptibility factors, the major genes ative FH ,7,8,9,10 limited ,13,14. I limited ,16. HoweIn order to identify other putative causes of increased susceptibility to BC, which might also underlie a fraction of both familial and sporadic eoBC cases, alternative pathomechanisms have been sought. An emerging phenomenon, which has also been associated with increased risk of BC and other types of cancer, consists of epigenetic dysregulation, such as hypermethylation at specific regions, resulting in the inactivation of tumor suppressor genes ,18. HypeBRCA1 and other loci. In particular, Hansmann et al. identified constitutive hypermethylation at BRCA1 and RAD51C in 1.4% (9/641) and 0.5% (3/641) of high risk patients respectively [Previous studies, which analyzed peripheral blood of BC-affected individuals, documented constitutive hypermethylation at ectively . FurtherBRCA1, BRCA2, ATM, CDH1, FANCM, STK11, NBN, PALB2, PTEN, RAD51C, RECQL and TP53), on a large cohort of 154 unrelated women affected with eoBC and selected for negative FH.Since young BC-affected women with negative FH in our population show a very low DR (<8%) at routine genetic testing , a possiIn the experiment implemented on 8 healthy donors, a total of 232 CpGs from the 12 investigated promoters were analyzed. Among those, 81 CpGs were excluded according to the EpyTYPER warning remarks  and 32 CpGs were excluded based on the quality-filtering criteria. As a result, 119 CpGs were selected for the subsequent analysis. All these CpGs had a success rate > 80% across all the samples (154 cases + 60 controls) and standard deviation <0.1 in control samples.CDH1, ATM and STK11, which showed a median methylation value in the range 0.1–0.15, and two CpGs in RAD51C, which were consistently hypermethylated in all samples  of most CpGs showed <0.1 as expected, with the exception of a single CpG in Pathological features of the analyzed cohort of patients are reported in BRCA1 promoter, with mean methylation value of 0.25. The analysis of the corresponding patient’s tumor tissue revealed a mean methylation >0.60 . However, the mean RAD51C methylation, observed in the patient’s tumor (0.13), was lower than that observed in blood and within the normal range with respect to normal breast tissues (0.11–0.16)  . PatientBRCA1 promoter methylation above 0.50 detected in the tumor tissue, the loss of heterozygosity (LOH) at chromosome 17 was investigated.In order to assess whether a complete loss of BRCA1 underpinned tumor development in patient 1 and explained the Five Short Tandem Repeats  at chromosome 17 were analyzed in tumor samples and normal tissue from patient 1. Three STRs showed heterozygous results in DNA from normal tissue and thus were informative for the analysis . The presence of LOH was assessed by comparing the signal from the two alleles of the three STRs in blood and tumor DNA. BRCA1 (17q12–17q21.31). The calculated allele/peak ratio of the three STRs consistently confirmed LOH in the tumor, indicating the presence of a large deletion encompassing In order to determine whether oncological treatments influenced promoter methylation at the selected loci, we compared for each promoter the mean methylation of the selected CpGs between patients who underwent blood withdrawal during or following chemotherapy and those who did not undergo chemotherapy before blood withdrawal. Age at blood withdrawal, as an additional factor influencing DNA methylation was also investigated. For all promoters, no statistically significant association was observed either between mean methylation level and age at blood withdrawal in both controls and cases , panel CBRCA1 mRNA expression in patient 1, Real-time PCR was performed. BRCA1 mRNA levels of normal breast and tumor tissue from patient 1. Compared with normal breast tissue from two healthy women, BRCA1 expression levels in patient 1 were very low in both normal breast and tumor samples, with a reduction of 82.6% and 87.6% respectively . The analysis of isolated eoBC cases was prompted both by the very low detection rate of n et al. . In prinBRCA1 promoter (exceeding the threshold by +0.15 and +0.10 respectively) and one at the RAD51C promoter (exceeding the threshold by +0.05). The patient with the highest methylation at BRCA1, who developed a triple negative high-grade invasive ductal carcinoma at the age of 31 years, showed a mean blood methylation level of 0.25, possibly due to the presence of a constitutively hypermethylated allele. An alternative theory would be that the level of methylation detected in blood from our patient reflected the presence of tumor DNA. However, the size of cell-free DNA fragments is about 150 bp, considerably shorter than our PCR amplicons and thus not detectable by the assays used for the analysis. As for concerns circulating tumor cells (CTCs), the density of which is as low as 1 cell per 1 × 109 blood cells in patients with metastatic cancer, their putative contribution would not justify the high methylation level of patient 1 [By analyzing 154 cases, representing the largest cohort reported to date of isolated eoBC cases investigated for promoter methylation at multiple loci, we identified three cases with higher methylation levels compared with healthy individuals, two at the atient 1 . MoreoveLOH along with mean promoter methylation >0.60. Since previous studies showed that promoter methylation usually occurs as a first step in tumor development [BRCA1 mRNA revealed decreased expression levels in both normal breast and tumor tissue from patient 1. Moreover, as expected the expression in the tumor sample was lower than that in normal breast, likely due to the presence of LOH. Subsequent analyses on tumor tissue revealed elopment ,33, thescis-acting effect on promoter methylation and gene silencing, we performed Sanger sequencing of the BRCA1 5′-UTR, which confirmed the absence of variants in the 500 bp upstream of the translation starting site. This phenomenon, usually underlying heritable constitutional epimutations rather than sporadic methylation events, is well characterized for mismatch repair genes [BRCA1 by Evans et al., who reported a co-segregation of the c.-107A>T variant with BRCA1 promoter methylation and gene silencing in multiple affected individuals of two families from North West England [In order to rule out the presence of regulatory sequence variants that might determine a ir genes , and has England .RAD51C, was also evaluated for promoter methylation in the tumor tissue. The analysis revealed a methylation level within the normal range, thus ruling out a possible role of epigenetic dysregulation in tumor development.Patient 2, who developed an invasive ductal carcinoma  at the age of 31 years and showed hypermethylation exceeding the threshold by +0.05 at BRCA1, developed an invasive ductal carcinoma  at the age of 28 years. Further analyses on patient 3 could not be carried out, since tumor tissue was not available.Patient 3, exceeding the threshold by +0.10 at RAD51C) showed a normal methylation pattern in cancer tissue, we cannot exclude that slighter methylation changes could affect BC risk in other patients. Since hypermethylation in our cohort is assumed to be a somatic mosaic event, methylated alleles could be present at low level in blood DNA, and possibly not detectable by MassARRAY or other available techniques. Consistent with this, a recent study by Böck et al. showed that slight increase in promoter methylation affecting tumor suppressor genes could still determine transcriptional dysregulation in blood, although a direct effect on tumor development could not be ascertained [There are a few limitations to our study. First, although the MassARRAY system compared with most other techniques is able to analyze sequences encompassing a larger number of contiguous CpG sites, for some of the investigated promoters the selected CpGs, due to the limited knowledge of the promoters’ structure, might still not be representative of the global promoter methylation. If that were the case, the frequency of hypermethylation events in our cohort would be underestimated. Moreover, the detection rate could have also been influenced by the thresholds used to identify hypermethylated cases. A value of +0.05 with respect to the one-sided 95% bootstrap confidence interval of the controls’ mean might be too conservative and, although patient 2 . Patients, whose reported FH was not considered reliable due to uncertain or insufficient information were excluded from the study.Patients were selected among individuals and families who underwent genetic counselling and testing at our Institution. Selection criteria included: women affected with BC, either invasive or ductal carcinoma in situ (DCIS); age at onset before the age of 36 years or before the age of 45 years, in case of documented triple negative phenotype; available and updated information on FH and no additional cases of BC or OC up to the 3rd degree of kinship; no pathogenic variants (including large deletions/duplications) or variants of uncertain significance (VUS) identified by the A total of 154 patients were selected for the analysis on peripheral blood. The median age at onset of BC was 33 years (range 19–44 years); 21 developed a second breast cancer at a median age of 42 years (range 29–68 years). Histopathological features of BC are summarized in Blood samples were consecutively collected also from a control population of 60 women, selected by using age as a matching criterion, among healthy blood donors at the Immunohematology and Transfusion Medicine Service of our Institution. The age at blood withdrawal ranged from 19 to 66 years (median 35) for patients and 21 to 59 years (median 41) for healthy controls.Furthermore, four Formalin-fixed Paraffin-Embedded (FFPE) samples collected from healthy women, who underwent breast reduction surgery, were used as controls for the methylation and expression analyses.All participants provided a signed informed consent for the use of their biological samples and data for research purposes. The investigations were conducted in accordance with the Declaration of Helsinki and the study was approved by the Ethics Committee of Fondazione IRCCS Istituto Nazionale dei Tumori .® DNA Mini and Blood Mini Kit  following protocol instructions. FFPE tumor tissue samples, from patients with abnormal methylation levels detected in blood cells, were also retrieved for DNA extraction. In order to reduce the contamination of the tumor component by non-neoplastic cells, manual microdissection was performed on a 7 µm FFPE tissue section previously stained with hematoxylin-eosin. DNA from FFPE tumor and normal samples was extracted with the QIAamp® DNA FFPE Tissue Kit (QIAGEN), according to the protocol instructions.Whole blood DNA was extracted through the QIAampBisulfite conversion of genomic DNA from whole blood and FFPE tissues was performed with the EZ DNA Methylation Direct Kit , using 500 ng of DNA for each reaction.BRCA1, BRCA2, TP53, PTEN, CDH1, PALB2, STK11, ATM, NBN, RAD51C, RECQL and FANCM. These genes have been either found hypermethylated at promoters in BC patients or their sequence variants have been associated with an increased risk of BC. Promoter regions were selected based on previously published data and in silico prediction of promoter sequences . For most of the considered genes , promoter regions to be investigated were selected based on previous studies, which demonstrated a correlation between hypermethylation and either reduced gene expression or increased risk of BC or both. The other analyzed regions  are within sequences with strong promoter activity, but for which fewer functional information related to methylation were available [https://www.epidesigner.com, Agena Bioscience, San Diego, CA, USA) in order to amplify bisulfite-converted 300–500 bp-long genomic sequences including multiple CpGs . We selected 12 loci for the study: vailable ,50,51,52BRCA1 and RAD51C promoters, were also designed in order to assess the methylation level in FFPE tumor tissues from patients with blood methylation significantly higher compared with control samples , PCR products were treated with shrimp alkaline phosphatase enzyme (Agena Bioscience) to dephosphorylate unincorporated deoxynucleotide triphosphate. The in vitro transcription and T-cleavage reaction were carried out as described in the instruction manual. Mass spectra were acquired through a MassARRAY mass spectrometer (Agena Bioscience) and analyzed using the EpiTYPER® MassARRAY® software, which provided a quantification of methylated/unmethylated CpGs, with values ranging from 0 to 1.The procedure was recently described by Fontana et al. . To avoi samples . In the An ad hoc experiment with samples from 8 healthy donors (controls) was performed in order to select CpG sites according to quality criteria such as reliability and reproducibility. In this experiment, two independent bisulfite modifications, in duplicate for each sample, were processed for methylation analysis of the 12 investigated promoters. Within and between variability and success rates were considered in order to select reliable CpG sites for the subsequent analysis. Specifically, CpGs with less than 80% of successful measurements or with a standard deviation of 0.1 (10% methylation) and larger were removed. In addition, CpG sites with warning remarks by the EpiTYPER software indicating unreliable data were also excluded.To identify hypermethylated cases, the following procedure was adopted: for each subject (cases and controls) the mean methylation levels of the selected CpGs within each promoter was computed. Starting from the distribution of mean methylation levels of the 60 controls, 1000 bootstrap samples were generated and the one-sided 95% percentile bootstrap confidence interval (bCI) of the controls’ mean was derived . SubsequBRCA1 locus on chromosome 17q. Genomic locations of the STRs and primer sequences used for PCRs are provided in PCRs were performed using 100 ng of DNA from both tumor and normal breast tissue samples, and the products were analyzed by capillary gel electrophoresis using Gene Mapper software on an ABI 3130XL system . Five STRs  were used to investigate the presence of deletions involving the BRCA1, were analyzed by Sanger sequencing to check the presence of sequence variants that might determine a cis-acting effect on promoter methylation and gene silencing. Primers used for sequencing cover the whole region (c.-516 to c.-24) which was also investigated for methylation profiling: forward primer 5′-AACTGGAGACCTCCATTAGG-3′ and reverse primer 5′-ACCCAGAGCAGAGGGTGAA-3′. PCR was performed with the AmpliTaq Gold DNA polymerase  and sequencing with the Big Dye Terminator v3.1 Cycle sequencing Kit (Thermo Fisher Scientific) on an ABIPRISM 3130XL Genetic Analyzer .The two patients, exceeding the methylation threshold at BRCA1 mRNA expression levels in normal breast and tumor samples from patient 1 and normal breast tissue from two healthy women, which were also analyzed for methylation levels. Total RNA was extracted from FFPE tissues through the RNeasy FFPE Kit (QIAGEN) following the manufacturer’s instructions and quantified by Nanodrop instrument (Thermo Fisher Scientific) with standard protocol.Real-time PCR experiments were performed in order to quantify BRCA1—assay ID: Hs01556193_m1) and housekeeping (18S rRNA—assay ID: Hs03928990_g1) genes, respectively. Relative gene expression was determined using the DDCt method. Each sample was run in triplicate and the expression value represents the mean among the three replicates.An amount of 500 ng of RNA were reverse transcribed by two independent experiments using the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) with random hexamers. Real-time PCR experiments were conducted on TaqMan 9700 Realtime PCR system (Applied Biosystems). TaqMan probes were used to detect target  and better define a threshold which could be used to identify hypermethylation with potential functional effects.


KIT gene has been showed to affect pleiotropic effects, blood parameters, and coat colour phenotypes, especially the white colour phenotype formation in European commercial breeds. Here, we described the use of SNPs to identify different selection patterns on the porcine KIT gene and the phylogenetic relationships of the inferred haplotypes. The phylogenetic tree revealed four clades in European and Asian wild and domestic pigs: two major clades with European and Asian origins and one minor clade with Iberian origins as well as the other minor clade in Asia, consistent with the major introgression of domestic Asian pigs in Europe around 18th -19th century. The domestication history of pigs, which occurred in the domestication centers (Europe and Asia), has also been demonstrated by mtDNA analysis. Furthermore, both Asian and European domestic pigs evolved under purifying selection. This study indicated that domestic pigs in Europe and Asia have different lineage origins but the porcine KIT gene was undergoing a purifying selection during their evolutional histories.The recent geographic expansion of wild boars and the even more recent development of numerous domestic pigs have spurred exploration on pig domestic origins. The porcine The KIT gene is located in the Dominant White locus and is confirmed to be responsible for several common colour variants in pigs, such as Dominant White, patch, spotting, belt, and roan [Coat colour variation in animals has been a considerable research hotspot in animal genetics and breeding. Documentation on coat colour can be traced back over 5,000 years ago to Mesopotamia . Numerouion loci , which eectively , 4. Howeand roan . KIT gene encodes the mast/stem cell growth factor receptor, which is a cytokine receptor expressed on the surface of haematopoietic stem cells as well as other cell types [MGF, also known as the stem cell factor). It contains an extracellular domain composed of five immunoglobulin domains, a single transmembrane domain, a juxtamembrane domain, and an intracellular protein kinase domain that is interrupted by an insertion of approximately 80 amino acids [ KIT gene are associated with gastrointestinal stromal tumours, mast cell disease, and piebaldism in human [ KIT gene mutations [Thell types . This prno acids . The prono acids . Mutatioin human . In micein human . Variantutations , 19. KIT gene, the recessive i allele (wild-type) present in wild boar and coloured pigs , the IP allele for the patch phenotype (Pietrain), the IBe allele for the belt phenotype (Hampshire), the IRn allele for the roan/gray coat colour, and the major dominant I allele  for the fully dominant white coat colour especially in European commercial breeds such as Landrace and Large White. The origin, domestication, and distribution of the alleles in the porcine KIT gene likely present an independent geographic trend. Subsequently, the European pigs harboured a normal copy number of the KIT gene  as well as duplicated copy numbers , while only the normal copy number was identified in Asian pigs until now. Furthermore, none of the Dominant White alleles were identified in one Chinese white pig breed [ KIT gene were limited to only a few of pig breeds [Up to now, five alleles have been identified in theig breed . Howeverg breeds , 20, 21. KIT gene in 17 Asian wild and domestic breeds, as well as several European wild and domestic breeds. We also compared the divergent evolutional mode of the KIT gene and addressed which kinds of selection occurred in Asian and European domestic pigs during the process of domestication.In this study, we aimed to investigate the DNA polymorphisms and haplotype distribution of the porcineA total of 44 unrelated pigs from 22 domestic breeds were analyzed in this study  with the working graft sequence of Sus scrofa (AC141857.2). The information on primers is listed in μL reaction volume containing 40–50 ng of template DNA, 10 × PCR buffer, 100 μM of dNTP, 10 pmol of each forward and reverse primers, and 1 unit of Taq DNA polymerase . The PCR program included an initial denaturation step of 95°C for 5 min followed by 40 cycles of 94°C for 30 s, 50-66°C for 30 s, and 72°C for 40 s, and a final extension step of 72°C for 5 min in a PTC-200 Programmable Thermal Controller . PCR products were purified with GELaseTM Agarose Gel-Digesting Preparation kit  and then were directly sequenced using the Big Dye terminator chemistry on the ABI 3130XL DNA Analyzers . Ambiguous positions were verified by resequencing. To determine if any contamination was present, blank PCR controls were performed throughout this study.A total of 17 PCR primer sets were designed using Primer Premier 5 to amplify the 21 exon regions of theAccording to the highly conserved tRNA-Pro and tRNA-Phe regions within the porcine mtDNA, one primer set was designed to amplify the D-Loop region as described in . Purifieπ), haplotype diversity (Hd), and Watterson's theta estimator were calculated by the DNASP v. 4.0 [∗, and F∗ were also estimated by the DNASP. Confidence intervals for Tajima's D and Fu and Li's D∗ and F∗ values were obtained by generating 1000 independent coalescent simulations assuming no recombination. The synonymous (silent) and nonsynonymous (nonsilent) nucleotide substitution rates of gene sequences were estimated by the modified Nei-Gojobori method descried by [Nucleotide diversity (P v. 4.0 . To esticried by . The MEGcried by . KIT haplotypes were conducted by MrBayes 3.1 [http://tree.bio.ed.ac.uk/software/figtree/). A neighbour-joining (NJ) tree was constructed with MEGA under Kimura's two-parameter model. Standard errors were obtained from 1000 bootstrap replicates.Phylogenetic analyses of theayes 3.1 , 27 and ayes 3.1 . A consehttp://www.fluxus-engineering.com). Nucleotide weighting (ω) was adjusted to reflect the difference in mutational frequencies between transversions (ω = 20) and transitions (ω = 10).The relationships of haplotypes were estimated by the parsimonious median-joining method and visualized using Network 4.5.1.6 and Network Publisher 1.2.0.0 . Subsequently, the variable sites were checked in the other individuals. A total of 28 polymorphisms including one indel event (intron18: g.29_32delAGTT) were obtained by a multiple alignment of the assembled 66 individual sequences using Clustal W [ KIT gene in wild and domestic pigs from Asia and Europe. It was noted that none of the 27 identified SNPs had given any effect of a camouflaged coat in either Asian or European wild boars. Even 5 of 27 were missense mutations, but Asian and European wild boars did not display any difference in phenotypic or physiological characters. Furthermore, no fixed mutation has been identified as the distinguishing SNP, which could separate the wild boar from domestic breeds.The polymorphism sites in the whole coding region of thelustal W . The pre KIT gene in the identified four subgroups is presented in  KIT gene . It could be explained by the unique breeding history of the European pigs, the majority of which were commercial breeds, except the Iberian pig, which was selected and developed strongly under the effects of human selection. Interestingly, Asian pigs displayed distinct mutations compared with those in European pigs. However, no more novel mutations were detected between Asian wild and domestic pigs, and the polymorphisms displayed a slight decrease in Asian domestic pigs. Aspects of selective pressures acting on the KIT gene were estimated by an analysis of the rates of synonymous, silent (dS), and nonsynonymous or nonsilent (dN) nucleotide substitutions among wild and domestic pigs from Asia and Europe . Two major and two minor clades were identified in the consensus tree using the mutations of the porcine KIT gene .A consensus tree was generated based on the estimated haplotypes of the investigated samples, as well as the one out group sequence , while the European wild boars contained one major haplotype and a small portion of other haplotypes (East European type). Since these patterns were revealed within a few wild boars, they may not lead to a scientific conclusion. We confirmed these patterns by a large sample set containing 37 European wild boars and 42 Korean wild boars . The hapi, I, IP, IBe, and IRn) [ KIT gene [ KIT gene duplication mutation associates with a partially dominant phenotype (patch), and one exon skipping leads to the roan phenotype [Up to now, 5 alleles have been identified in the Dominant White locus (and IRn) , 33, 34.and IRn) . None ofhenotype , while ihenotype .KIT) indicated four geographic clusters, which supported the multiorigins of modern pigs. Furthermore, both Asian and European domestic pigs evolved under purifying selection during their evolutionary histories.In conclusion, we found that the phylogenetic analysis of the Dominant White locus (


Hordeum vulgare) is cultivated in many arid areas of the world. In the present study, we evaluate the tolerance to water stress (drought) in nine accessions of “Ardhaoui” barley landraces from different regions of Tunisia. The genetic diversity of the accessions is evaluated with six SSR markers. Seedlings from the nine accessions are subjected to water stress by completely stopping irrigation for three weeks. A high genetic diversity is detected among the nine accessions, with no relationships between genetic distance and geographical or ecogeographical zone. The analysis of growth parameters and biochemical markers in the water stress-treated plants in comparison to their respective controls indicated great variability among the studied accessions. Accession 2, from El May Island, displayed high tolerance to drought. Increased amounts of proline in water-stressed plants could not be correlated with a better response to drought, as the most tolerant accessions contained lower levels of this osmolyte. A good correlation was established between the reduction of growth and degradation of chlorophylls and increased levels of malondialdehyde and total phenolics. These biochemical markers may be useful for identifying drought tolerant materials in barley.Due to its high tolerance to abiotic stress, barley ( Hordeum vulgare L.) is outstanding for its tolerance to various stresses. Munns et al. [n = 14) and a huge range of genetic variability that accounts for different levels of stress tolerance, barley is a model species among crops for deciphering stress tolerance mechanisms, and hence has been subjected to many genetic studies in relation to stress tolerance, ranging from genetic variability analysis [Among cereals, barley  of nine accessions of the “Ardhaoui” barley collected in different locations from four different regions of southern Tunisia with varying climatic conditions . An analysis of genetic diversity with genomic SSRs was used to evaluate genetic diversity among the nine accessions. These materials were subjected to a controlled water stress tolerance treatment and compared to a control with the aim of identifying materials tolerant to drought and investigate the biochemical mechanisms involved in the tolerance to this stress. These results will be important for evaluating the variation for tolerance to drought, potential for selection and mechanisms of tolerance to drought of this resilient barley landrace.aN) ranged between 2 (GBM1256 and GBM1459) and 5 (GBM1461), and the frequency of the major allele was always ≥0.5 (eN) ranged between 1.52 (GBM1256) and 2.84 (GBM1221), while PIC values ranged between 0.286 (GBM1256) and 0.610 (GBM1461). The SSR genetic fingerprints revealed eight different genetic fingerprints among the nine accessions, resulting in eight branches in the phenogram of genetic relationships , with relatively very high increases over the control (Significant differences were observed among accessions for proline (Pro) and total soluble sugars (TSS) both for control and water stress treatments A,B. No a control A, indica control A,B.Regarding the oxidative stress marker malondialdehyde (MDA), there were no significant differences among accessions in the control plants A. HoweveSignificant differences were observed among accessions for total phenolic compounds (TPC) and total anti-oxidative flavonoids (TF) in control plants, although the levels observed were not associated to the levels of tolerance B,C. For Drought is the most adverse abiotic factor affecting crops, and even the most optimistic scenarios forecast that in many regions of the world it will get worse in the future, due to the global warming . DroughtScreening of reliable drought tolerance indicators is an important tool in the selection of tolerant genotypes . Plant rAlthough all growth parameters analyzed in our study showed an inhibitory effect of water stress in all nine accessions, the most discriminant proved to be the total fresh weight (FW), whose reduction was almost double in the more sensitive accessions in comparison with the tolerant accession 2. Dry matter content of the aerial part (leaves and shoots) also significantly decreased in the water stress treatment in all accessions, although in a lower proportion than fresh weight, indicating a lower water content in the aerial tissues. In cereals, maintaining cell water potential is a main strategy of drought avoidance . AltogetDecreasing chlorophyll levels is considered as a symptom of oxidative stress and may be the result of pigment photo-oxidation and chlorophyll degradation . ChloropDrought and salinity have an osmotic effect on plants due to the lowered soil water potential in the root zone which interferes with water absorption and maintenance of turgor. To counterbalance the high external osmotic pressure, plants synthesize and accumulate in their cytosol compatible solutes, commonly known as osmolytes . Apart fCarbohydrates have also been reported as major osmolytes in barley , especiaIn conclusion, our study reveals that wide variation exists for tolerance to drought in the Tunisian “Ardhaoui” barley and that highly tolerant genotypes can be selected that may be of interest for adaptation to climate change and yield stability under marginal conditions. In this respect, selection for drought tolerance among variable landrace accessions can be a rapid way to select adapted materials with increased drought tolerance. Our study also casts doubt on the utility of proline as indicator of drought tolerance in barley genotypes. Instead, it suggests that reduction of chlorophylls or MDA contents, or increases in total phenolics could be used as parameters for selecting drought-tolerant varieties of barley. The results obtained provide relevant information on the diversity and mechanisms of drought tolerance in the “Ardhaoui” barley, which is of interest for developing new barley materials with better tolerance to drought.Seeds of landrace accessions of the “Ardhaoui” barley from nine different locations in Southern Tunisia, which could be grouped into four different ecogeographical zones , were usBarley seeds were germinated, grown and treated in a greenhouse of the Institute of Plant Molecular and Cell Biology (IBMCP) at the Universitat Politècnica de València, Spain. A controlled growth environment was maintained, with regulated temperatures ranging between 17 and 23 °C, under a long-day photoperiod (16 h light/8 h dark); humidity ranged between 50% and 80%. The seeds were sown in seed trays containing a mixture of commercial peat and vermiculite (1:1). Immediately after emergence, young seedlings were transferred to individual polyethylene pots  and were grown for three weeks. During this growth period, plants were watered with Hoagland’s nutrient solution . Followi2, 0.05 µM of forward primer, 0.25 µM of reverse primer, 0.2 µM of fluorescent M-13 primer, 0.2 mM dNTPs, and 0.20 µL Taq DNA polymerase , at a concentration of 5 U/µL. Amplifications were carried out in an Eppendorf Mastercycler ep gradient S  thermocycler. Amplification procedure via the thermocycler consisted of an initial step at 94 °C for 5 min; 35 cycles of 94 °C for 30 s, 30 s at 62 °C, and 72 °C for 45 s; and final 10 min extension at 72 °C.For each accession, genomic DNA was extracted from nearly 100 mg of young leaf tissue using the CTAB method . DNA conPCR products were diluted in formamide and analyzed on an automated DNA sequencer ABI PRISM 3100-Avant with a GeneScan 600LIZ  size standard. The data were analyzed using the GeneScan software (Applied Biosystems) to obtain the electropherograms and polymorphisms were analyzed with Genotyper DNA Fragment Analysis software (Applied Biosystems).Stem length was measured every three days after the initiation of treatments. In addition, the number of leaves was monitored throughout the treatments period. At the end of the treatments (three weeks), all plants were harvested, and the vegetative parts were weighed individually on a precision balance. Part of the fresh material was weighed  before being dried at 65 °C, until it reached constant weight and then weighed again . The dry weight percentage  was calculated as (DW/FW) × 100.−1 DW. Proline (Pro), total soluble sugars (TSS), malondialdehyde (MDA), total phenolics content (TPC), and total flavonoids (TF) contents were determined in dry leaf material. Pro was measured according to the method described by Bates et al. [−1 DW. TSS was measured following the method devised by Dubois et al. [−1 DW). TF was quantified following the method described by Zhishen et al. [−1 DW).Chlorophyll a (Chl a), chlorophyll b (Chl b), and total carotenoids (Caro), were quantified following the protocol described by Lichtenthaler and Welburn  using 10s et al.  using gls et al.  using drs et al. , was dets et al. . MDA cons et al. . TPC wass et al.  using thn et al. . The amoaN), frequency of the predominant allele (f), effective number of alleles (eN), and polymorphic information content (PIC) was determined for each SSR locus using the PowerMaker software [For genetic characterization of the nine accessions of “Ardhaoui” barley landraces, the number of polymorphic alleles .Morphological and biochemical data were analyzed using the program Statgraphics Centurion v.16 . The significance of the differences among treatments was tested by ANOVA tests, and post hoc comparisons were made using the Tukey HSD test at


Tv) is explored as a standardized iDEP signature for each phage species. The present study includes mathematical modeling with COMSOL Multiphysics and extensive experimentation. Three related, but genetically and structurally distinct, phages were studied: Salmonella enterica phage SPN3US, Pseudomonas aeruginosa phage ϕKZ and P. chlororaphis phage 201ϕ2-1. This is the first iDEP study on bacteriophages with large and complex virions and the results illustrate their virions can be successfully enriched with iDEP systems and still retain infectivity. In addition, our results indicate that characterization of the negative dielectrophoretic response of a phage in terms of Tv could be used for predicting individual virus behavior in iDEP systems. The findings reported here can contribute to the establishment of protocols to analyze, purify and/or enrich samples of known and unknown phages. Bacterial viruses or phages have great potential in the medical and agricultural fields as alternatives to antibiotics to control nuisance populations of pathogenic bacteria. However, current analysis and purification protocols for phages tend to be resource intensive and have numbers of limitations, such as impacting phage viability. The present study explores the potential of employing the electrokinetic technique of insulator-based dielectrophoresis (iDEP) for virus assessment, separation and enrichment. In particular, the application of the parameter “trapping value” ( Increasing numbers of phages that share a core set of genes with SPN3US have recently been isolated, most for the goal of using them for phage therapy purposes. Despite their obvious potential for biocontrol applications, in reality little is known about the biology of these phages. For instance, the virions of giant phages related to SPN3US are comprised of many (>70) different proteins ranging in copy numbers from just a few to 1560 copies per virion, a large proportion of which (~80%) have no known specific function. These characteristics have led us to conduct genetic studies on SPN3US as a model for related “giant” phages.Bacteriophages, estimated to have a total population of 10on earth . Over thon earth . To emplBacillus virus, phageG to completely disintegrate, and those of B. thuringiensis phage 0305ϕ8-36 to lose infectivity by several orders of magnitude [To conduct characterization studies on SPN3US, or any phage, requires the use of techniques to purify and enrich particles from a non-homogenous sample containing bacterial cells, cellular debris, and virions, as illustrated in agnitude . Similaragnitude . Given tagnitude ,8.Microfluidics has revolutionized the manner in which many bioanalytical assessments are performed. It has opened the doors to perform high resolution and sensitivity purification assays . ElectroMicroorganisms have been extensively studied in dielectrophoretic-based systems, including both electrode-based DEP (eDEP) and iDEP systems . A challIn contrast to human and plant viruses, there have been few studies on the suitability of DEP for phage enrichment. Sonnenberg et al developed an eDEP system for the isolation detection of T7 bacteriophage from whole blood , illustrSalmonella phage SPN3US, and for comparison purposes, two related giant Pseudomonas phages: ϕKZ and 201ϕ2-1, in two distinct iDEP systems. In particular, the application of the parameter “trapping value” (Tv) is explored as a standardized iDEP signature for each virus species. This work includes mathematical modeling with COMSOL Multiphysics®  and experimentation with iDEP devices containing an array of circular or oval-shaped insulating posts. For model information, please see the Tv, Equation (3)) for each type of phage [The contributions mentioned above are excellent examples of some of the latest advancements in the dielectrophoretic manipulation of viral particles. However, it is evident that systems capable of purifying newer “environmental” phages, including giant phages, are still an unexplored area. Similarly, unexplored are systems capable of handling a larger throughput containing several viral species which would be of potential value in phage therapy as typically cocktails or mixtures of different types of phages are employed. In this contribution, we present the first report on the assessment and enrichment of of phage ,36. ThisParticles can exhibit either positive or negative DEP, depending on their relative polarizability with respect to the suspending media . PositivSeparating the above expression into system-dependent and particle-dependent parameters:ls-Terré  and Hayels-Terré  groups, 10–1012 pfu (plaque-forming units)/mL) of three related viruses: Salmonella Typhimurium phage SPN3US [Pseudomonas aeruginosa phage ϕKZ [P. chlororaphis phage 201ϕ2-1 [Experiments were conducted in two distinct microchannel designs made from PDMS employing standard soft lithography techniques; microfabrication information is included here . The mice SPN3US , Pseudomhage ϕKZ  and P. c 201ϕ2-1 . These pPhage samples were assayed for viability via plaque assays in triplicate using the standard double overlay technique using LB agar bottom plates and overlays made from LB broth and 0.34% agar. Briefly, each phage sample underwent a 10-fold dilution series in SM buffer and these were spotted onto overlays made containing 100 μL of a fresh overnight culture of the appropriate bacterial strain. Plaques were enumerated after overnight incubation at 30 °C.Tv, Equation (3)). Each experiment started with a clean channel to which a 5–10 μL sample of the corresponding labeled virus was added, followed by the application of DC electric potentials. For the purpose of this study, a “sufficient” trapping voltage was determined as the required voltage to obtain a visually observable band or cluster of trapped viral particles. Phage response was observed and recorded as videos with a Leica DMi8 inverted microscope . Direct current (DC) electric potentials were applied with a high voltage supply . COMSOL Multiphysics® 4.4 was used to predict the magnitude of the trapping value , which characterizes the conditions required to trap a specific type of particle [The trapping value  in a given iDEP device. Consequently, these findings will be relevant for future studies on mixtures of phages, even those including related phages, as found in phage therapy cocktails.As defined by Casals-Terré  and Hayemensions . NotablyPseudomonas phages, ϕKZ and 201ϕ2-1, had titers in the high range after trapping  for each phage type. This parameter, as identified by other research groups, normalizes the required conditions, in terms of electric field and electric field gradient, for trapping a specific type of particle. The results demonstrated that the Tv for a specific species is reasonably constant within the two distinct designs studied here, opening the exciting possibility of using Tv for the designing of iDEP devices targeting specific viral species, and also for predicting the required voltage for trapping a specific type of particle, including viruses, in distinct iDEP devices. In addition, these findings suggest that iDEP has potential for analyses of mixtures of phages, even those including related phages, such as found in phage therapy cocktails.Presented here is the assessment of three phages: SPN3US, ϕKZ and 201ϕ2-1 in two distinct iDEP devices, one with circle-shaped and one with oval-shaped insulator posts. Experimental work demonstrated the successful trapping of all three phage species, where the voltage requirement to achieve trapping of virions was lower in devices with circular insulating posts, since these produce lower dielectrophoretic forces than the oval-shaped posts. A mathematical model created with COMSOL was then employed to estimate the trapping value (


The results showed that: (1) a subset of reentries  conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EPavg cases; (2) emergent VTs could be induced sometimes after ablation in EPavg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EPvar parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EP Ventricular tachycardia (VT), a life-threatening fast heart rhythm, occurs frequently in patients with myocardial infarction, and leading to sudden cardiac death. Although increased utilization of pre-procedural imaging , high-deavg) to represent non-infarcted tissue and remodeled gray zone (GZ). Clearly, an ideal option would be to use personalized EP in each model, however acquisition of such information is invasive, in contrast to the VAAT approach, which offers a non-invasive prediction of the ablation targets. Thus, the ablation targets that are predicted to terminate VT in post-MI patients using the VAAT approach might have a level of uncertainty associated with the fact that pre-determined EP is used.Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders and their treatment ,b; it haSimilar issues had previously arisen in using patient-specific models of persistent atrial fibrillation (AF) in patients with fibrotic remodeling . As the While persistent AF in the fibrotic substrate and VT post-MI have very different arrhythmia dynamics, they both result from a combination of remodeled structural substrate interacting with a remodeled EP. Thus, in this study, we aimed to systematically assess the effect of changes in the electrophysiological parameter set on the induced VTs and predicted ablation targets in personalized models of post-MI patient hearts.For this retrospective study, we used data from 5 patients who were referred for catheter ablation of VT; their MRI data are used for model reconstruction. Details regarding patient information and model construction can be found in our previous publication . BrieflyElectrophysiology properties were assigned in the model as previously described . Brieflyavg. We then ran simulations in ventricular models with the same geometric structure and infarcted tissue distribution to assess the effects of APD and CV variability. For simulations with APD variability, specific ionic currents were modified to achieve ±10% APD  in each case were used in this study (±10% and CV±10% conditions), parameter changes required to achieve the desired emergent property were different for non-infarcted and GZ tissues. Complete details about these changes and the resulting variability in APD and CV are presented in To establish baseline reentry dynamics in the absence of variability in the electrophysiological component of the infarcted substrate, we first conducted simulations under average human VT EP, as in previous studies . For bre±10% APD , while mve slope . To simpis study . For sim±20% and CV±25%, and in one model, with a combination of APD±20% and CV±25%. The parameter changes were listed in ±20% instead of APD±25% because APD+25% resulted in longer APD than the upper limit of APD in experimental measurements (364 ms vs. 340 ms)  .1 on a parallel computing platform  were delivered; then S2 was given at 250 ms. If excitation was elicited, then we decreased S2 to 200 ms. If the excitation could propagate again, then we decreased S2 in 10 ms intervals until excitation could not be elicited. The S2 coupling interval was the last one that elicited excitation propagation. The same protocol was applied to determine the smallest S2-S3 and S3-S4 intervals. If reentry was induced after the S2 stimulus, then no addition stimulus was given.avg through analysis of the 3D reentrant wave propagation. In silico ablation was performed by rendering the tissue in the narrowest region of the reentrant circuit [typically a channel in the scar as in . For VTs induced in each of these models, if the reentry morphology was the same or similar to VT induced in the corresponding EPavg model, the ablation lesion from the EPavg model was applied to the same location in the EPvar model. By similar reentry morphology we refer to reentries that have the same wavefront conduction pathway, with the same critical conduction channels (or conduction isthmus), but could have different CL, and even different reentry direction. If VT in the EPvar model was terminated, the ablation lesions in EPvar and EPavg models were considered “overlapping.” If VT persisted in the EPvar model despite the ablation, then these two VTs were considered non-overlapping. Then ablation in the EPavg model proceeded as described above. Finally, the number of ablation lesions was compared between EPavg and EPvar models.All simulations were then repeated in the family of models with EPavg conditions, rapid pacing induced sustained VTs in all 5 patient-specific ventricular models. avg models. A total of 16 distinct reentries were induced in five models under the EPavg condition, and after ablation, only one new distinct reentry was induced. The average amount of ablated ventricular tissue under EPavg condition was 0.92%. Before ablation, there were 2–4 different sustained VTs (3.2 ± 0.8) induced in each model, and the organizing centers of all 16 VTs perpetuated in distinct locations. Ablation, as described in Section “Materials and Methods,” did not always render the remodeled substrate non-inducible for VT; in some cases, new VTs could arise. Specifically, after ablation, 4 patient models did not have emergent VTs, while 1 patient model had one emergent VT at a location different from those before ablation. The volume of the simulated ablation lesions that terminated the VTs, both original and emergent, in each model was 0.92 ± 0.83% of the total ventricular volume .For simulations conducted under EP±10% conditions each  are provided in +10% parameters. In terms of the total number of VTs, 22 unique VTs were induced in the 24 inducible models. Eight of the VTs were induced under all five electrophysiological conditions; these were for patients 1, 3, 4, and 5. The activation maps of these 8 VTs are shown in +10) is shown in ±10% and CV±10%), APD±10%, and CV+10% resulted in less VTs, while CV−10% resulted in more VTs compared with the EPavg conditions.Comprehensive summary data for the VTs induced in simulations conducted in all 5 ventricular models under the 5 different APD/CV+10% parameter set before ablation, and all of them corresponded to VTs under EPavg conditions , another one at the LV lateral wall (avg), and the third one located at the posterior wall (morphology not shown). For the APD±10% and CV±10% parameter sets, both resulted in two reentries being induced, which were located at the septum and the LV lateral wall. Under CV−10% conditions, there were 5 distinct VTs induced, and 3 of them had matching reentries in EPavg models (ablation lesions in Panel B of The results for one example model (patient 5) under all 5 EP parameter sets are shown in avg to the other 4 conditions  resulted in changes in reentry location only when CV was at the boundaries of that range. A Venn diagram of these results is provided in Next, we present results of simulations with the additional EP parameter sets (from the extended parameter range) in the limited number of models, as described in Section “Materials and Methods.” Results from simulations with model 2 are presented in +20% & CV+25% and APD−20% & CV+25% conditions did not result in reentry. There were two reentries induced under the APD+20% & CV−25% condition, one of which corresponded to the reentry morphology in EPavg  EPavg conditions resulted in more VTs (5–8) than in APD±10% and CV+10%, and in less reentries than in CV−10%; (3) Emergent VTs were induced sometimes post-ablation, and the emergent reentries often matched the pre-ablation VTs in models with other parameter sets. (4) Where the VTs were robust to APD/CV variability, there were differences, as expected, in macroscopic arrhythmia features such as CL and total activation time.In this study, we used computational models reconstructed from LGE-MRI scans of the infarcted ventricle of individuals with VT to assess the sensitivity of VT localization to variability in cell- and tissue-scale electrophysiological parameters. We showed that: (1) in simulations conducted with ±10% APD/CV, a subset of VTs  were observed in approximately the same locations as those in the EPavg were also induced in all other four parameter set models (±10% APD/CV), and the remainder of reentries were induced in at least one of the four parameter set models. Interestingly, all the reentries induced in APD+10% and CV+10% models had matching reentries in the EPavg models. The models with EPavg were more inducible than the APD+10% and CV+10% models, as the wavelength in the former case was smaller, allowing for more space for the reentry to propagate and be sustained. While there were new VTs induced in models with APD−10% and CV−10%, more than 76% of the VTs occurred also in models with EPavg. The models with APD−10% had a smaller number of induced VTs than the corresponding EPavg models. A possible explanation for this finding is that APD in the EPavg parameter set falls in the arrhythmogenesis susceptibility window of APD (About 53% (9/17) of the reentries that occurred in EPw of APD , where t±20% and CV±25% cases had similar outcomes to those with APD±10% and CV±10%. Thus, simulating only ±10% APD or ±10% CV changes may be sufficient in representing the results from simulations with larger electrophysiological parameters variations.In the additional simulations with the extended range of APD and CV and their combination, the results with the combined reduction/increase of CV and APD were very similar, in terms of reentry location, and pathway to those when varying APD or CV independently. Furthermore, APDavg models identified most of the VT ablation targets that were consistently observed under different EP conditions. Nonetheless, there was a small subset of reentries that were observed only under EPavg simulations. Furthermore, in some models, VTs only emerged upon APD and/or CV change, suggesting that there could be a small number of potential ablation targets that may not be revealed using the EPavg parameter set. Furthermore, for reentries with the same morphology and location of the critical conduction isthmus (and thus the same ablation target) we observed differences in CL, total activation time and in rare cases, even direction of propagation. Finally, we demonstrated that majority of ablation targets that were not predicted under EPavg conditions manifested themselves upon repeating the pacing protocol post-ablation, similar to the findings in modeling of ablation for persistent AF in patients with fibrotic remodeling  might play a dominant role in determining the location of infarct-related VTs. Our results suggest that EPmodeling .The results shown in this paper differ from our atrial sensitivity analysis study  because avg conditions identified most of VTs that were consistently observed under multiple different EP conditions. The uncertainty in the post-MI VT ablation targets under EPavg is further mitigated by the design of the pipeline for determining the optimal set of targets [as in the VAAT approach . Considering the relatively low CV and long APD in GZ used in our model, further changes in these parameters will make these values fall out of the experimental range. Furthermore, we conducted a large number of simulations with 25 different 3D LV models. The sheer amount of computational time makes exploration of additional ranges of parameter values technically difficult. The second limitation in this study is that in order to achieve different APD values, we only changed IDD, AP, and PN performed the LGE-MRI scan segmentation and model creation. DD performed the simulations of VT in all models. DD, AP, and NT analyzed the data. DD, JS, and NT wrote the manuscript. All authors discussed the results and commented on the manuscript.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.


The streamflow of major global rivers changes under the influences of climate change and human activities and varies greatly in different regions. The Yellow River has undergone a dramatic shift during the last six decades. Its streamflow gradually dwindled away and even dried-up severely in the late 20th century, but in recent years it has recovered and remains stable. Comprehensive understanding of the river streamflow change and its driving forces promotes effective water resource management within this complex human-natural system. Here, we develop a runoff identity attribution approach to analyze 61 years of streamflow observations from the Yellow River. We find that between the 1950s and the 1980s, human water consumption contributed more than 90% to streamflow reduction, but from the 1970s onwards, land cover change became the major factor to decrease streamflow. Since 2000, government management schemes have prevented streamflow from declining further and guarantee its stability. Based on the analysis framework we propose, persistent droughts, which are related to abrupt streamflow abatement, may be the most uncontrollable factor in the future. A more resilient management system should be therefore built to grapple with the expected increased frequency of such extreme climate events in the future. Most continental streamflow generally increased during the 20th century, as the global hydrological cycle intensified, driven by atmospheric variation and land use change5, but there is uncertainty regarding streamflow trends on the regional scale7. At these scales, spatial heterogeneity and social-ecological interactions make the process of streamflow change more complex9. In general, precipitation and its intensity relating to large-scale atmospheric circulation, and temperature are the major climatic factors causing streamflow change16, while physical features of landscape and human activities such as urbanization, agricultural development, and water-soil conservation measures also play important roles in altering streamflow23. Although there are many researches focusing on regional streamflow change drivers, previous studies pay more attention to one or two aspects. Therefore, holistic analysis of regional streamflow features and mechanisms is required to promote a better understanding of the basin-scale water budget, which is critical for sustainable water resource management24.Streamflow is a major source of water for human consumption, and also the most concise indicator of a basin’s response to climate change and human activities25, faces an enormous challenge of water shortage, especially in the period of great changes in social-ecological environment27. With the increase of population and development of social economy, there has been significant growth in water consumption, including agricultural, industrial, and domestic water use, along with water facilities during the last six decades29. Serious soil erosion in the Loess Plateau, mainly located in the middle reaches of the YR basin, and high sediment load in the YR have been a major issue for the local population for many years31, the Chinese Government implements a series of large ecological engineering and invests more than US$8.7 billion32 to solve this problem. After a long-term water-soil loss control, the Loess Plateau has become greener and the sediment load of the YR has significantly decreased in recent decades, but this decrease is associated with streamflow reduction36. Climate factors, such as increasing temperature, decreasing precipitation and changing extreme climate events, also aggravate water scarcity in the YR39. The streamflow has decreased severely; the lower YR even dried-up completely in the late 20th century41.The Yellow River (YR) Fig. , flowing45. However, with great efforts under the government management, water use regime of the Yellow River (YR) was altered after acute dried-up events, and the declining streamflow trend has been reversed immediately49. In spite of these major changes, there have been few systematic studies focusing on long-term streamflow change in the YR basin and its response to various driving factors in different periods.Lots of studies have taken notice of this phenomenon and there is an agreement on the significant roles of precipitation, human abstraction and water-soil conservation measures such as afforestation in streamflow decreaseThe objective of this study is therefore to understand the temporal change and spatial distribution of streamflow in the Yellow River basin, and to quantify the influences of precipitation, potential evapotranspiration, land cover and direct human activities on streamflow change during the last six decades.−2, p < 0.001) since 1956, and, in recent years, there has been no significant difference (p = 0.28) between the streamflow of the YR estuary and the source regions  since 1956, while at the Lijin gauging station (LJ), the control station of the YR estuary, the streamflow fell from 501.15 × 108 m3 yr−1 in the 1960s to 140.75 × 108 m3 yr−1 in the 1990s, and finally increased to 175.20 × 108 m3 yr−1 in the 2010s. This latter value is approximately equal to the value of 195.94 × 108 m3 yr−1 measured at TNH during the 2010s, even though there are 51 major tributaries, each with a catchment area greater than 1,000 km2, flowing into the YR between TNH and LJ.Streamflow in the Yellow River (YR) estuary has significantly decreased  during the period 1956–2016. This decline was the result of both increasing human water consumption , and decreasing natural water yield . Based on 7-year moving-average annual regional streamflow between TNH and LJ, we divided the past 61 years into four periods covering 1956–1968 (P1), 1969–1985 (P2), 1986–2002 (P3), and 2003–2016 (P4)  during P1. Their gap narrowed but was still significant (p < 0.001) in P2, but disappeared, and even reversed for P3 and P4, because water consumption was close to or above water yield between these two stations.Analysis of the water budget of the region between Tangnaihai gauging station (TNH) and Lijin gauging station (LJ) (see Methods) revealed that its regional streamflow had a significant decline (slope −6.07 × 10p = 0.45) during 1986–1996 in P3, because decreasing natural water yield equalled to increasing human consumption. As these trends continued, the balance was lost in 1997, but then recovered (p = 0.28) in P4 owing to a halt to the trends and a slight increase of natural water yield. An interesting difference between P3 and P4 was that the drying-up of the lower YR occurred frequently and for increasing number of days in the former period, but no drying-up occurred in the latter period at all , 37.54% being attributed to the source regions (SR), 22.72% to the upper reaches (UR), and only 0.80% to the lower reaches (LR). This difference comes from the various physical features of these sub-regions  basin Fig. . During 8 m3 yr−1, in the whole basin. This mismatch between water yield and consumption is particularly serious in SR and LR, with the available water supply in LR being almost wholly reliant on flow from upstream (95%).However, spatial distribution of human water consumption doesn’t match water yield pattern well. Locating in arid and semi-arid regions mostly, agricultural water in the Yellow River (YR) basin accounts for more than 70% of the total human water consumption. Irrigation is the most important mode of agricultural development, especially in upper reaches (UR). Besides farmland within the basin, the lower YR also provides water for irrigation areas in the North China Plain  made the largest contribution to the streamflow decline of the YR (46.75%), with a proportional change rate of −2.02% yr−1. RC accounted for 90.34% of this change, while HI made up 9.23% and PET only 0.43%. Together the lower reaches (LR) and upper reaches (UR) contributed to more than half of the YR streamflow reduction  and in both cases decreasing RC was again the main driver. The remaining 3.20% of the reduction in the YR was attributed to the source regions (SR) where streamflow dropped at a proportional rate of only −0.10% yr−1. The proportional rate of decreasing RC in the SR was −0.24% yr−1, more than twice that of the streamflow, while PET and HI both increased, with proportional rates of 0.09% yr−1 and 0.05% yr−1 respectively.We attributed regional streamflow change to changes of regional potential evapotranspiration (PET), hydrothermal index (HI), and runoff coefficient (RC) based on runoff identity  and direct human disturbance (HD), and then specified the major contributing factors among different periods  and hydrothermal index (HI). For P1 and P2, the rate of decline of the streamflow was −0.61 mm·yr−2, in which HD made the largest contribution (96.80%). Changes in PET and HI contributed 9.53% to this decline, with the 6.33% reduction surplus being offset by increasing RCn. The largest decline in YR streamflow, −1.15 mm·yr−2, occurred between P2 and P3, and was partitioned as follows: RCn (70.88%), HD (16.41%), HI (9.27%) and PET (3.44%). For P3 and P4, there was little reduction in streamflow, in comparison to the earlier periods, with a rate of only −0.03 mm·yr−2. In this case, RCn was the only contributory factor that reduced streamflow, with the contributions of other factors all mitigating the reduction.These results highlighted the dominant role of runoff coefficient (RC) in controlling the Yellow River (YR) streamflow reduction. To gain further insight, we divided the effect of RC into two parts, natural runoff coefficient (RCn), direct human disturbance (HD) and potential evapotranspiration (PET) accounted for 40.38%, 28.79%, 23.70% and 7.13% respectively, of the inter-annual streamflow variation. A significant reduction over time was found for the contributions of HI and PET , but the HD and RCn contributions showed an increasing trend .On the annual scale, both hydrothermal index (HI) and runoff coefficient (RC) had strong effects on streamflow variation in the Yellow River Fig. . The lonn). Human water consumption is the major component of HD, it reduced streamflow significantly  over the last six decades was attributed to runoff coefficient (RC) reduction. The dominant processes behind this reduction have changed from direct human disturbance (HD) to natural runoff coefficient (RCn) related to dramatic land cover change taking place in the Yellow River (YR) basin, largely accounted for the reduction in streamflow. The YR once carried more fluvial sediment than any other rivers in the world, which led to the launch of large-scale ecological projects in the YR basin, such as the Conservation of Water and Soil Ecological Engineering (CWSEE) project and the Grain-for-Green Programme (GGP), to prevent soil erosion51. After the implementation of GGP in 1999, difference of the normalized differential vegetation index (NDVI) has increased in more than 61% the YR basin . During the late 20th century, human water consumption had reached the maximum level that the Yellow River (YR) could bear60, and drying-up episodes in the lower YR were becoming more frequent and of longer duration  will continue executing the water allocation scheme to get human water consumption under control and sustain a healthy fluvial system25. Net primary productivity in the Loess Plateau, a major part of the YR basin, is approaching its sustainable water resource limits32, and ecological projects have imposed measures to alleviate water consumption. However, in spite of the progress made in YR management in the last few years, extreme climate events caused by global change are likely to be more frequent in the future65, and abrupt streamflow changes associated with uncontrollable climate factors will be a major concern over the coming decades. The management system must be perfected and made resilient to cope with these future climate challenges.In this study, we have provided a unique perspective on the Yellow River (YR) water budget and its change, and also presented a simple and efficient method for attribution analysis to capture the main streamflow characteristics in response to the key events and factors which dominate the available water resource. According to our analysis framework Fig. , it is a68 and from the Yellow River Water Resources Bulletin (http://www.yellowriver.gov.cn/other/hhgb/). Daily meteorological data from 1956 to 2016, including precipitation, maximum and minimum temperatures, wind speed, atmospheric pressure, relative humidity and duration of sunshine, were provided by the China Meteorological Data Service Center (http://data.cma.cn/). Data from 319 meteorological stations were involved in this research. Daily potential evapotranspiration at each station was estimated with the Penman-Monteith equation, and yearly potential evapotranspiration and precipitation were accumulated day by day. The Kriging interpolation was used to calculate regional average precipitation and potential evapotranspiration. Records of soil and water conservation measures, including areas of terraces, dams and vegetation, were collected from the Yearbook of the Yellow River (1990–2012), the China Water Conservancy Yearbook and other publications53. Satellite-based land use images  and normalized differential vegetation index images  of the YR basin were downloaded from Resource and Environmental Data Cloud Platform (http://www.resdc.cn/)70.Yearly and monthly streamflow data from the main gauging stations along the Yellow River (YR) for the period 1956 to 2016 were obtained from the Yellow River Conservancy Commission (YRCC). Yearly human water consumption and storage capacity of the YR basin, and partial large reservoirs water storage data for the 1956 to 2016 period were collected from papersoutlet − streamflowentrance). Human water consumption and reservoir water storage change were then added to the regional streamflow to get the regional natural water yield . Natural water yield represents the initial streamflow of a region before human abstraction and regulation through water facilities.To depict the water budget, we used regional streamflow that was calculated by subtracting the gauged streamflow at the entrance to a region from that at the outlet  basin into four regions based on the topographic features and human activity; the source regions (SR); upper reaches (UR); middle reaches (MR); and lower reaches (LR). They cover areas of 12.40 × 10Tangnaihai gauging station (TNH), Toudaoguai gauging station (TDG), Huayuankou gauging station (HYK) and Lijin gauging station (LJ) are the outlets of SR, UR, MR and LR respectively, while TNH, TDG and HYK are also the entrances to UR, MR and LR respectively Fig. . We calc72, the runoff (R) can be considered as the product of the three variables:Analogous to the Kaya and Sediment IdentityWe then defined the proportional change rate of a variable X(t) as r(X) = (dX/dt)/X. Accordingly, r(R) = ((dR/dt)/R) = ((d(PET·HI·RC)/dt)/(PET·HI·RC)) = ((dPET/dt)/PET) + ((dHI/dt)/HI) + ((dRC/dt)/RC), the counterpart of the runoff identity for proportional change rates can be rewritten asTherefore, we attributed regional runoff change to the changes of potential evapotranspiration , hydrothermal index , and runoff coefficient  by using this attribution approach.73:xj and xi are the jth and ith of X observations respectively and X observations. The theoretical proportional change rate, the sum of the proportional change rates of PET, HI and RC, closely approximates the actually calculated proportional change rate of R  is proportional to runoff (mm) and their trends are essentially identical, so the relative contribution of each identity factor to the change rate of runoff is equivalent to its contribution to streamflow. Here we focused on the influence of each identity factor on streamflow.Streamflow  such as vegetation interception caused by land cover change, and direct human disturbance (HD) including water abstraction and regulation. To separate HD from RC, we reconstructed the natural streamflow through yearly gauged data  and calculated its change rate. The difference in change rates between gauged and natural streamflow was attributed to HD, and the remaining part of the partial change rate of streamflow caused by RC was interpreted as the influence of RCn.Runoff coefficient (RC), namely the ratio of runoff to rainfall, used to describe the water yield ability of a region, can be altered by natural processes . A nonparametric test, the Kruskal-Wallis test, was used to test the differences between the four study periods.The Mann-Kendall test was used to analyze the change trends of yearly streamflow, precipitation, potential evapotranspiration and other hydrometeorological dataYellow River water rebalanced by human regulation