Functional elements demarcated by histone modifications in breast cancer cells

AbstractHistone modifications are regarded as one of markers to identify regulatory elements which are DNA segments modulating gene transcription. Aberrant changes of histone modification levels are frequently observed in cancer. We have employed ChIP-Seq to identify regulatory elements in human breast cancer cell line, MCF-7 by comparing histone modification patterns of H3K4me1, H3K4me3, and H3K9/14ac to those in normal mammary epithelial cell line, MCF-10A. The genome-wide analysis shows that H3K4me3 and H3K9/14ac are highly enriched at promoter regions and H3K4me1 has a relatively broad distribution over proximity of TSSs as well as other genomic regions. We identified that many differentially expressed genes in MCF-7 have divergent histone modification patterns. To understand the functional roles of distinctively histone-modified regions, we selected 35 genomic regions marked by at least one histone modification and located from 3 to 10kb upstream of TSS in both MCF-7 and MCF-10A and assessed their transcriptional activities. About 66% and 60% of selected regions in MCF-7 and MCF-10A, respectively, enhanced the transcriptional activity. Interestingly, most regions marked by H3K4me1 exhibited an enhancer activity. Regions with two or more kinds of histone modifications did show varying activities. In conclusion, our data reflects that comprehensive analysis of histone modification profiles under cell type-specific chromatin environment should provide a better chance for defining functional regulatory elements in the genome

Subclinical vascular inflammation in subjects with normal weight obesity and its association with body Fat: an 18 F-FDG-PET/CT study

BACKGROUND: Although body mass index (BMI) is the most widely accepted parameter for defining obesity, recent studies have indicated a unique set of patients who exhibit normal BMI and excess body fat (BF), which is termed as normal weight obesity (NWO). Increased BF is an established risk factor for atherosclerosis. However, it is unclear whether NWO subjects already have a higher degree of vascular inflammation compared to normal weight lean (NWL) subjects; moreover, the association of BF with vascular inflammation in normal weight subjects is largely unknown. METHODS: NWO and NWL subjects (n = 82 in each group) without any history of significant vascular disease were identified from a 3-year database of consecutively recruited patients undergoing (18) F-fluorodeoxyglucose positron emission tomography/computed tomography ((18) F-FDG-PET/CT) at a self-referred Healthcare Promotion Program. The degree of subclinical vascular inflammation was evaluated using the mean and maximum target-to-background ratios (TBRmean and TBRmax) of the carotid artery, which were measured by (18) F-FDG-PET/CT (a noninvasive tool for assessing vascular inflammation). RESULTS: We found that metabolically dysregulation was greater in NWO subjects than in NWL subjects, with a significantly higher blood pressure, higher fasting glucose level, and worse lipid profile. Moreover, NWO subjects exhibited higher TBR than NWL subjects (TBRmean: 1.33 ± 0.16 versus 1.45 ± 0.19, p < 0.001; TBRmax: 1.52 ± 0.23 versus 1.67 ± 0.25, p < 0.001). TBR was significantly associated with total BF (TBRmean: r = 0.267, p = 0.001; TBRmax: r = 0.289, p < 0.001), age (TBRmean: r = 0.170, p = 0.029; TBRmax: r = 0.165, p = 0.035), BMI (TBRmean: r = 0.184, p = 0.018; TBRmax: r = 0.206, p = 0.008), and fasting glucose level (TBRmean: r = 0.157, p = 0.044; TBRmax: r = 0.182, p = 0.020). In multiple linear regression analysis, BF was an independent determinant of TBRmean and TBRmax, after adjusting for age, BMI, and fasting glucose level (TBRmean: regression coefficient = 0.020, p = 0.008; TBRmax: regression coefficient = 0.028, p = 0.005). Compared to NWL, NWO was also independently associated with elevated TBRmax values, after adjusting for confounding factors (odds ratio = 2.887, 95% confidence interval 1.206–6.914, p = 0.017). CONCLUSIONS: NWO is associated with a higher degree of subclinical vascular inflammation, of which BF is a major contributing factor. These results warrant investigations for subclinical atherosclerosis in NWO patients

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows.open

Renal infarction resulting from traumatic renal artery dissection

Renal artery dissection may be caused by iatrogenic injury, trauma, underlying arterial diseases such as fibromuscular disease, atherosclerotic disease, or connective tissue disease. Radiological imaging may be helpful in detecting renal artery pathology, such as renal artery dissection. For patients with acute, isolated renal artery dissection, surgical treatment, endovascular management, or medical treatment have been considered effective measures to preserve renal function. We report a case of renal infarction that came about as a consequence of renal artery dissection

High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value &gt;0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R-2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area