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HEYL Localization Is Altered in Cancer Tissue-We have previously shown that in prostate cancer biopsies HEY1 expression is predominantly cytoplasmic rather than nuclear, and we hypothesized that this cellular localization provides a growth advantage in cancer cells, circumventing the repressive effects of nuclear HEY1 (25,26). To understand better the mechanism(s) of repression of AR by HEY proteins, we examined the subcellular distribution of HEYL protein by confocal microscopy with a monoclonal antibody. In normal and benign transformed prostate epithelial lines, RWPE-1 and BPH-1, respectively, endogenous HEYL was entirely nuclear (Fig. 7A). In our inducible LNCaP:HEYL cell line treated with doxycycline, the exogenously expressed HEYL was also nuclear, demonstrating that the transfected HEYL behaves similarly to endogenous protein, and furthermore, we observed a co-localization of HEYL and AR in the nucleus of these cells in the presence of androgen (Fig. 7A, bottom panel). In addition, we verified the specificity of the HEYL antibody by immunofluorescence and Western blotting of LNCaP:HEYL cells treated with or without doxycycline (Fig. 7A). To examine expression patterns of endogenous HEYL in human prostate tissue, we used prostate needle biopsies from patients with prostate cancer and stained them for HEYL. As shown in Fig. 7B, endogenous HEYL was expressed in the epithelial cell compartment of the prostate and showed nuclear localization in benign cells (Fig. 7B, left panels) with little expression detected in the cytoplasm. However, in all stages of cancer examined, HEYL nuclear intensity decreased, in most cases correlating with increasing cytoplasmic expression (Fig. 7, B and C). The expression score for each patient is plotted in Fig. 7C. This shows that nuclear expression decreased in all stages of the disease studied but remained high in adjacent benign tissue. Furthermore, expression of HEYL in the cytoplasm remained low in adjacent tissue but at the same level or higher in prostate cancer. Collectively, these **data** suggest that HEYL is excluded from the nuclei of cancer cells but not benign cells, indicating that nuclear exclusion of HEYL occurs in the early stages of prostate cancer formation or progression.

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The belief context consists of interaction processes, their interaction histories, and associated expectation **data** (discussed further at the end of this section), organized into object schemas. Each object schema's contents represent a set of beliefs that is probably about a real physical object. For instance, a visual tracker and its history **data** manages and contains a set of beliefs that include the location, color, and shape of an object. Similarly, an inactive action process for performing a grasp is associated with an expected success likelihood, which constitutes a belief about the graspability of the object.

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reports the performance of different estimators over 1000 replications of the **data** subsampling. Each replication results in a different observational dataset D O with different number of treated units n (1) O < N 1 and different number of control units n (0) O < N 0 . For evaluation we consider two criterion over the 1000 replications: the Mean Absolute Error (MAE) and the Median of Abolute Errors (MedAE).

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The most classical measures for functional connectivity are correlation and coherence, which reflects the similarity between signals in the time and frequency domain, respectively. Intuitively speaking, coherence is a correlation of two signals in the frequency domain (15). Other connectivity measures consider the phase of the oscillations in the electrophysiological signals, the so-called measures of synchronization. The phase indicates whether the oscillation is at a specific time point t at a peak, trough, or transitions between these two states (such as for instance, zero crossings). If two signals exhibit the same phases at the same point in time, they are said to oscillate synchronously. Determining the phase of two signals allows calculating the difference in phase, the phase lag, which in turn may inform us about propagation effects, if the one signal exhibits a later phase than the other signal. The phase lag is suggested to reflect signal propagation and can be studied to examine effective connectivity. In addition to bivariate measures that consider pairs of signals, multivariate measures are designed in order to remove shared properties between multiple signals, such as, for example, partial coherence (16). Most measures of effective connectivity are described under the umbrella term Granger causality (17). This concept considers two signals X and Y and examines whether the activity at time point t of signal X can be predicted (statistically) by the activity at the earlier time points t-k of signal Y. Among them, partial directed coherence (18) and directed transfer function (19) are commonly used to study epilepsy. Next to these **data** driven analysis approaches, effective connectivity can also be estimated based on underlying biophysical models with a priori assumptions about the organization of the network as in Dynamic Causal Modeling (DCM) and other neural mass models. DCM in EEG or MEG takes biologically plausibility of causal models into account, and thus yields an informed estimate of connectivity (20).

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Our **data** suggests that the expressed Cry toxins did not solely induce starvation (as cause of death) in the presence of antibiotics, i.e., presumed absence of microbiota, as hypothesized by Mason et al. [21]. For O. nubilalis, already on day 4, survival was reduced to less than 50% on both Bt maize varieties and for S. littoralis on the Spanish Bt maize to around 60%. In the study by Mason et al. [21], however, starvation did not affect survival rates before day 6. However, in our experiments, continuous presence of antibiotics or only a pretreatment with antibiotics until the onset of the bioassays did make some although small differences. This was possibly because even when stopping the administration of antibiotics at the beginning of the bioassay, a carry-over effect may last for the tested five-day period, meaning that the reestablishment of an effective gut microbiota probably takes longer than the testing period. Antibiotics alone did not affect larval survival on non-Bt control maize varieties, which is in agreement with all other studies listed in Table 1.

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Preliminary analysis excluded 11 (10.6%) patients because of suboptimal quality of transthoracic images. In TEE, interob- Figure 2. The scheme of the selection of index beat during AF. The STE results were estimated using the ratio of preceding (1) to pre-preceding (2) R-R′ interval. We selected the beat with the smallest difference between prevenient R-R′ intervals BPM -beats per minute; HR -heart rate; STE -speckle-tracking echocardiography server discrepancies regarding LAAT vs. "sludge" differentiation involved 6 (5.8%) patients. These cases were excluded from further analysis. Finally, we enrolled into the study 36 patients with LAAT and 51 patients without LAAT as controls according to previously established criteria. Demographic, clinical, and echocardiographic **data** for the study group are presented in Table 1.

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Using mutual information [14,15], the time delay of each variable of the time series can be solved. Based on information theory, the mutual information between time series and its delay time series can be derived: ( ) = ( , ) = ( ) + ( ) − ( , ) .(3) We compute the mutual information ( ) by the histogrambased statistic estimator [15], and , which makes ( ) to the first local minimum point, is regarded as the delay time of th variable. By calculating time series **data** of all subjects, reasonable reconstructing time lag can be obtained (the average time lag of 36 subjects is ankle = 17 ± 7.3, knee = 21 ± 5.8, and hip = 19 ± 8.3).

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To determine the effect of ground vortices on the performance of the propeller, the **data** at two height ratios are compared, namely, h=R ¼ 3:0 and h=R ¼ 1:46. The height ratio of h=R ¼ 3:0 is the maximum height ratio could be achieved in the setup. It is supposed that the strength of ground vortices generated at the height ratio of h=R ¼ 3:0 is much smaller than that at h=R ¼ 1:46. The height ratio of h=R ¼ 1:46 is the position closest to the ground during our test, and it induces ground vortices which have the strongest impact on the propeller inflow. The difference of the propeller performance between h=R ¼ 1:46 and h=R ¼ 3:0 is negligible, as shown in Fig. 17. This means that the time-averaged performance of the propeller is independent of the ground vortices. First, the effects of the vortices entering the propeller in the propeller axial direction are cancelled out by each other. This hypothesis is confirmed by the tangential velocity distribution, as shown in the top right of Fig. 12. Second, although the effect of vortices entering the propeller in the radial direction induces an axial velocity decrease in the propeller inflow (as shown in the top left of Fig. 12), this influenced region is small compared with the whole disk region of the propeller and its effect is negligible as well. As the majority of research on turbofans is conducted on suction tubes, the impact of ground vortices on the loadings of a turbofan is not available. Our tests on a propeller give such **data** for the first time.

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For statistical analysis the SPSS statistics package (version 19.0; IBM SPSS, Armonk, NY, USA) was used. The distribution of our **data** indicated that fGC **data** were normally distributed after Kolmogorov-Smirnov; therefore, we used untransformed fGC values for graphical representation of the data. In addition, means were tested for significant differences with the t-test for repeated and independent samples, and comparisons between groups for zoo location, seasons, social group and age class were analysed by one-way analysis of variance (ANOVA). In the case of overall significant effects, when appropriate, post hoc comparisons were conducted by Tukey, Wilcoxon signed-rank, and Kruskall-Wallis tests. For all statistical tests, the alpha value was set at P = 0.05. Assays results are expressed as mean ± standard error of mean ng metabolite concentrations of fGC per dry gram of faeces. To determine correlations between fGC levels and social rank, Spearman correlation analysis was used.

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showing that in this limit the numerics are under control . For the rescaled equation ( eq.14 ) we can study the lowest mass meson as a function of the quark mass . Figure 3 shows the value of the first meson mass as a function of the quark mass . The important point to note here is that there appears to be a mass gap in the m → 0 limit for l = 0 . The numerics make this calculation difficult , though at m = 10 −10 the value of M 1 is 0.28 . Note that in contrast to the equation of motion with l = 0 , the D7 - brane equation is perfectly well behaved in this limit and has discrete eigenvalues . This will be shown analytically in section 4.2 . The scale is set by the AdS radius R which can be tuned by hand to compare with lattice data .

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RNA isolation form MG63 cells in the control and experimental groups . Control cells were cultured under normal condition . Experimental cells were pretreated with { BIW8 } for 24 h. According to the manufacturer 's instructions , the total RNA of MG63 cells was extracted by using TRIzol ( Invitrogen , USA ) . RNA concentration and quality were determined with a NanoDrop Spectrophotometer . Microarray data were converted into recognizable format and annotated with software Genome Studio . The probes detected with p - value lower than 0.01 in at least one sample were accepted as significant and used for further analysis . The raw data were normalized using the quantile algorithm from the package limma of R. 1.13.1 Identification of differentially expressed genes ( DEGs ) . |Fold change| > 1.5 and adj . P < 0.05 were set as the cut - offs to screen out differentially expressed genes ( DEGs ) . 1.13.2 Heatmaps and volcano plots analysis . Clustered heatmaps and volcano plots of the control and experimental groups were generated using the package ggplot2 of R. 1.13.3 Enrichment analysis of DEGs . Functional enrichment analysis of DEGs was performed by DAVID ( The Database for Annotation , Visualization and Integrated Discovery ) to identify GO categories in by their biological processes ( BP ) , molecular functions ( MF ) , or cellular components ( CC ) . The DAVID database was also used to perform pathway enrichment analysis with reference from KEGG ( Kyoto Encyclopedia of Genes and Genomes ) pathways . False discovery rate ( FDR ) < 0.05 was used as the cut - off .

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We will use the most updated version of the regulatory label for the treatment options for type 2 diabetes. When **data** on outcomes is not available, we will use **data** from our Agency for Healthcare Research and Quality (AHRQ) comparative effectiveness review 15 .

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Mordred instructs GrimoireELK about when to poll based on its configuration, and GrimoireELK constructs the corresponding queries (to **data** sources, via Perceval, or to raw indexes) using metadata (in raw indexes or in enriched indexes, respectively). When Arthur is used, Mordred instructs it directly about polling frequencies.

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Author Contributions: Conceptualization, Z.K.; methodology, Z.K. and E.I.; software, N.A.; validation, Z.K., E.I. and N.A.; formal analysis, Z.K.; investigation, Z.K. and E.I.; resources, N.A.; **data** curation, Z.K. and N.A.; writing-original draft preparation, Z.K., E.I. and N.A.; writing-review and editing, N.A.; supervision, N.A.; project administration, N.A. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding.

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For MALDI IMS **data** analysis, **data** was log2 transformed before further statistical analysis and a linear model was fitted using both treatment and analyses run as variables. For multiple testing correction of the p-values the [30] was used. An adjusted pvalue ,0.05 was considered as significant. Statistical analysis was done using the statistical environment R [31] and the package Limma [32]. Linear regression was used for its representative value but Pearson correlation was used to ascertain the nature of the relationship.

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Neural networks trained on big **data** can now be used in a variety of healthcare-related domains, for instance to issue recommendations regarding resource management in hospitals (bed allocation, ventilators and PPE availability, etc.), transplant waitlists, and more [3]. Some surgeries and exploratory exams can now be performed by high-precision robots aided by machine learning ( [16,19], and AI systems that issue medical diagnoses and suggest treatments have been around for a while [11,18]. Some have explicitly argued that machine learning and computational AI more generally are transforming psychiatry by changing the conceptual categories the field relies on, and might soon transform the very definition of mental disorder [42]. Finally, machine learning is also used in architecture and building engineering to create safer, more affordable, and more sustainable housing (e.g., [20,22,23].

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Thus, we develop a Bayesian scaling estimation method with non-decimated wavelet transform (NDWT) motivated by real-life signals that are known to possess a certain theoretical degree of self-similarity. Bayesian approaches have been previously employed in this context. The Hurst exponent for Gaussian **data** was estimated with a Bayesian model in [11,2,4]. Holan et al. [7] developed a hierarchical Bayesian model to estimate the parameter of stationary long-memory processes. A Baysian model for the parameter estimation of auto-regressive fractionally integrated moving average (ARFIMA) processes [9] are discussed in [6,19,16]. These models are based on time domain data. However, the de-correlation property of wavelet transforms facilitates a simplified model construction, and multiple wavelet-based Bayesian techniques has been developed. Based on a Bayesian approach, Vannucci and Corradi [23] estimated parameters for long memory process with a recursive algorithm and Markov chain Monte Carlo (MCMC) sampling. A Baysian wavelet model for ARFIMA processes is illustrated in [10].

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After the relevant results at the regional and departmental scale , we also monitored smaller watershed by collecting samples in Parisian sewers , to allow for a more precise description of the viral dynamics and allow for the detection of clusters . Viral concentrations in samples collected from the sewer system of the city of Paris are plotted in Fig . 3 . Results showed that the median of data taken from the sewer visually correlated with the city trends , emphasizing the possibility to refine the virus spreading in the area . Monitoring of individual sampling points showed some specific dynamics before the start of the second wave , demonstrating the possibility to detect local cluster using sewer monitoring ( lower panel ) . For example , an important concentration was locally detected during week 22 ( October 14 , 2020 ) , at the beginning of the second wave , probably due to local clusters . Same trend could be easily observed before the second lockdown .

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The data that support the findings of this study are available from the corresponding author , [ author initials ] , upon reasonable request .

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yes