Thorough Statistical Analyses of Breast Cancer Co-Methylation Patterns

Background: Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data). Results: Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal have more than 93% of positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites; the top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns. Conclusions: Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.

The Spatial Autocorrelation between Precipitation and Vegetation Indices in the Bandar Abbas Basin

This study aimed to investigate the spatial autocorrelation between precipitation and vegetation indices in the Bandar Abbas basin. For this purpose, the vegetation indices of DVI, EVI, IPVI, NDVI, NDWI, RVI, SAVI, TCI, VCI, and VHI were derived from Landsat satellite images over 20 years were studied. Precipitation data corresponding to rain gauge stations was extracted. The Pearson correlation coefficient and the GI * and I indices were used to investigate the relationship between precipitation and spatial autocorrelation. Moreover, the Pearson correlation coefficient was used to investigate the relationship between precipitation and vegetation indices, and the GI * and I indices was used to correlate spatial autocorrelation patterns. The results showed that SAVI, VHI, VCI, and NDWI were most correlated with precipitation among the Bandar Abbas basin's vegetation indices, with the SAVI index being more closely correlated than the others. However, precipitation had the least impact on the TCI index. The spatial autocorrelation of rainfall with the vegetation indices, except for the IPVI index, had a scattered pattern in the study area’s southern and eastern parts. Of the indices studied in terms of spatial pattern, the IPVI and NDWI indices formed a positive spatial correlation pattern with precipitation over a greater spatial range.

Correlations Between the DMN and the Smoking Cessation Outcome of a Real-Time fMRI Neurofeedback Supported Exploratory Therapy Approach: Descriptive Statistics on Tobacco-Dependent Patients

The aim of this study was to explore the potential of default mode network (DMN) functional connectivity for predicting the success of smoking cessation in patients with tobacco dependence in the context of a real-time function al MRI (RT-fMRI) neurofeedback (NF) supported therapy. Fifty-four tobacco-dependent patients underwent three RT-fMRI-NF sessions including resting-state functional connectivity (RSFC) runs over a period of 4 weeks during professionally assisted smoking cessation. Patients were randomized into two groups that performed either active NF of an addiction-related brain region or sham NF. After preprocessing, the RSFC baseline data were statistically evaluated using seed-based ROI (SBA) approaches taking into account the smoking status of patients after 3 months (abstinence/relapse). The results of the real study group showed a widespread functional connectivity in the relapse subgroup (n = 10) exceeding the DMN template and mainly low correlations and anticorrelations in the within-seed analysis. In contrast, the connectivity pattern of the abstinence subgroup (n = 8) primarily contained the core DMN in the seed-to-whole-brain analysis and a left lateralized correlation pattern in the within-seed analysis. Calculated Multi-Subject Dictionary Learning (MSDL) matrices showed anticorrelations between DMN regions and salience regions in the abstinence group. Concerning the sham group, results of the relapse subgroup (n = 4) and the abstinence subgroup (n = 6) showed similar trends only in the within-seed analysis. In the setting of a RT-fMRI-NF-assisted therapy, a widespread intrinsic DMN connectivity and a low negative coupling between the DMN and the salience network (SN) in patients with tobacco dependency during early withdrawal may be useful as an early indicator of later therapy nonresponse.

Measurement Invariance of Test Anxiety Across Four School Subjects

School-subject-specific test anxieties have been widely examined, but there is a lack of analyses of measurement invariance of test anxiety across subjects. In order to preclude a mixture of test anxiety construct differences across school subjects with actual test anxiety differences and to ensure meaningful comparisons of test anxiety across school subjects, we examined such measurement invariance. Two test anxiety factors (worry and emotionality) were inspected across four school subjects (mathematics, physics, German, and English) in a sample of secondary school students ( N = 1,280). Strict measurement invariance was ascertained (i.e., comparable factor loadings, intercepts, and residual variances of the items of worry and emotionality factors across school subjects). The correlations of subject-specific test anxiety factors with subject-specific academic self-concepts and grades showed a convergent/divergent correlation pattern, thereby supporting criterion-related validity. The results of this study provide insights into the comparability of test anxiety assessments across school subjects.

Pair Bond-Induced Affiliation and Aggression in Male Prairie Voles Elicit Distinct Functional Connectivity in the Social Decision-Making Network

Complex social behaviors are governed by a neural network theorized to be the social decision-making network (SDMN). However, this theoretical network is not tested on functional grounds. Here, we assess the organization of regions in the SDMN using c-Fos, to generate functional connectivity models during specific social interactions in a socially monogamous rodent, the prairie voles (Microtus ochrogaster). Male voles displayed robust selective affiliation toward a female partner, while exhibiting increased threatening, vigilant, and physically aggressive behaviors toward novel males and females. These social interactions increased c-Fos levels in eight of the thirteen brain regions of the SDMN. Each social encounter generated a distinct correlation pattern between individual brain regions. Thus, hierarchical clustering was used to characterize interrelated regions with similar c-Fos activity resulting in discrete network modules. Functional connectivity maps were constructed to emulate the network dynamics resulting from each social encounter. Our partner functional connectivity network presents similarities to the theoretical SDMN model, along with connections in the network that have been implicated in partner-directed affiliation. However, both stranger female and male networks exhibited distinct architecture from one another and the SDMN. Further, the stranger-evoked networks demonstrated connections associated with threat, physical aggression, and other aversive behaviors. Together, this indicates that distinct patterns of functional connectivity in the SDMN can be detected during select social encounters.

365 The Extent and Pattern of Correlation Among Blood Parameters Differed Between Pasture-raised Goats and Sheep

Various blood parameters are generally used to monitor the nutrient status and health condition of goats and sheep. Relationship of a parameter with others may be useful to depict a bigger picture when that parameter is known. However, information on how different blood parameters are related to each other in goats and sheep has not been documented well. The objective of this study was to determine the correlation among different blood parameters in goats and sheep, both within and between species. Kiko does (19; 15–16 m old; live wt. 34±1.4 kg) and Katahdin ewes (18; 21–22 m old; live wt. 40±1.4 kg) were rotationally stocked in fall pastures for 87 days. Blood samples were collected on Day 1, Day 47, and Day 87, and analyzed for 34 blood parameters. Data were analyzed for Pearson Correlation Coefficients (r) (ɑ= 0.05) in SAS 9.4. In does, concentration of red blood cells (RBC) was found positively correlated with hemoglobin (HGB) (r=0.68), mean corpuscular hemoglobin concentration (MCHC) (r=0.36), albumin (r=0.36), and cholesterol concentrations (r=0.34) (P < 0.05). However, RBC concentration was negatively correlated with mean corpuscular volume (MCV) (r=-0.58) and mean corpuscular hemoglobin (MCH) (r=-0.71) (P < 0.0001). Cholesterol was found positively correlated with hematocrit, hemoglobin, lymphocyte, basophil, calcium, albumin, and chlorine, and negatively correlated with neutrophil and amylase (P < 0.05). In ewes, RBC concentration was found positively correlated with hematocrit (r=0.81) and hemoglobin (r=0.84) (P < 0.0001), and negatively correlated with MCV (r=-0.29), MCH (r=-0.59), MCHC (r=-0.42) and mean platelet volume (r=-0.39) (P < 0.05). Cholesterol was found positively correlated with creatinine, calcium, gamma-glutamyl transferase, and potassium, and negatively correlated with glucose, blood urea nitrogen, and lipase (P < 0.05). Results show that blood parameters are correlated with one another, and the correlation pattern and extent among blood parameters occur differently in goats and sheep.

BOLD Coupling between Lesioned and Healthy Brain Is Associated with Glioma Patients’ Recovery

Predicting functional outcomes after surgery and early adjuvant treatment is difficult due to the complex, extended, interlocking brain networks that underpin cognition. The aim of this study was to test glioma functional interactions with the rest of the brain, thereby identifying the risk factors of cognitive recovery or deterioration. Seventeen patients with diffuse non-enhancing glioma (aged 22–56 years) were longitudinally MRI scanned and cognitively assessed before and after surgery and during a 12-month recovery period (55 MRI scans in total after exclusions). We initially found, and then replicated in an independent dataset, that the spatial correlation pattern between regional and global BOLD signals (also known as global signal topography) was associated with tumour occurrence. We then estimated the coupling between the BOLD signal from within the tumour and the signal extracted from different brain tissues. We observed that the normative global signal topography is reorganised in glioma patients during the recovery period. Moreover, we found that the BOLD signal within the tumour and lesioned brain was coupled with the global signal and that this coupling was associated with cognitive recovery. Nevertheless, patients did not show any apparent disruption of functional connectivity within canonical functional networks. Understanding how tumour infiltration and coupling are related to patients’ recovery represents a major step forward in prognostic development.

Spatiotemporal Difference Characteristics and Influencing Factors of Tourism Urbanization in China’s Major Tourist Cities

Tourism is crucial for promoting industrial development and is an important driver of China’s new type of urbanization. A tourism urbanization index system was constructed in three dimensions: the tourism industry, urbanization, and the ecological environment. The spatial–temporal differentiation characteristics and influencing factors of tourism urbanization in 35 major tourist cities in China from 2009 to 2018 were analyzed using the state space method, standard deviation ellipse, and spatial autocorrelation analysis. The results show the following. (1) Over time, the tourism industry index displays an upward trend, the urbanization index exhibits a more obvious upward trend, and the ecological environment index fluctuates strongly. Under the influence of all three factors, the tourism urbanization index shows a fluctuating rising trend. (2) Regarding the spatial distribution pattern, the development center of tourism urbanization shifts to the southeast, and the distribution direction is northeast-southwest. There is a significant agglomeration in global spatial autocorrelation. The local spatial correlation pattern is dominated by correlation characteristics and supplemented by different characteristics. (3) In terms of influencing factors, policy and regional development strategy, tourism resource endowment, economic development level, and traffic conditions are listed in descending order of influencing degree. Finally, we put forward some suggestions.

PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure.