Read A Book Of Dreams

So, I'm pretty new to lucid dreaming, and earlier i was researching some ways you can become lucid using DILD. And it said that you can become lucid by reading text in a dream, but the text would be a bunch of random letters and crap. This confused me because in SO many of my dreams I've read things perfectly. I'll wake up and be able to remember exactly what it said, without anything wrong with it. I'm a little confused.

This was done in three-hour blocks, and repeated between seven and ten times, on different days, for each participant. During each block, participants were woken up ten times per hour. Each volunteer reported having visual dreams six or seven times every hour, giving the researchers a total of around 200 dream reports.

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Kimberly Drake is a freelance columnist, feature writer, reporter, and copywriter focusing on all aspects of health and wellness. Her weekly health column in the Lakeland Times, an award-winning newspaper in Wisconsin, informs her loyal readers on the latest health news and trends with a humorous journalistic flair. In her spare time, she serves as vice-chairperson on the governing board for Lakeland STAR School/Academy, a charter school specializing in the needs of students with autism. When not researching, writing, and editing, she can be found enjoying the benefits of gardening and weight training, or immersed in DIY projects.

People who appear in your dreams may reflect parts of your own personality. Analyzing these dreams may help to think about what the person was doing in the dream and what part of yourself you think may be shown to you through the other person.

Interpreting your dreams takes patience, practice, and an open mind. It may be a good idea to remember that you give meaning to your own dreams by identifying symbols and thinking about what they mean to you personally.

Talking about your dreams with a psychotherapist may be particularly beneficial when trying to analyze them. They may be able to give you feedback about specific words you used or facial expressions you made, which could also add to the dream interpretation.

Dream analysis may be better achieved by working with a psychotherapist. They might be able to link different aspects of your dreams and ask you specific questions that could lead to more profound insight.

Part of the reason this is the case is because writers and poets think about language more than most people, and holding these thoughts in the mind immediately before sleep can influence the content of their dreams, she explains. But poets, in particular, may find the language contained in their dreams more useful than others.

Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.

Error generation in next-generation sequencing data. Normal cells (gray) and cancer cells (blue) shed DNA into the bloodstream. The cancer DNA (blue) contains a mutation (yellow star). The circulating free DNA in the blood is damaged both in vivo and in vitro (green triangle). Errors can be introduced at each PCR duplication during amplification (red circle). Further errors are accumulated during sequencing and mapping (purple square). The final data contains mapped reads, where some mismatches are errors, and others are mutations from tumor cells

Mutect2 [15], Strelka2 [16], and Shearwater [17] are examples of general somatic variant callers applicable for most NGS data. Mutect2 realigns reads in regions with mutational signal and then calculates a log-odds for the existence of the alternative allele using a statistical model in which the error rates are derived from the PHRED scores. Similar to Mutect2, Strelka2 realigns the reads and then uses a statistical model to determine the likelihood of a variation being real by analyzing base quality, read mapping quality, and depth at each position. Shearwater is developed specifically for low-frequency somatic variant detection for sub-clonal tumor mutations. It builds a position-specific error model based on the observed rate of read alignment mismatches across a set of training samples. A mutation is called if the observed signal exceeds what is expected from the error model. Additionally, this method can incorporate prior knowledge about the probability of the mutations of interest.

Other methods, including MRDetect [18] and INVAR [19], have been specifically tailored to detect ctDNA in NGS data. These methods build on the idea of aggregating the signal across multiple mutations to classify a sample as ctDNA positive or negative, as opposed to calling each individual mutation. For this purpose, a patient-specific catalog of mutations is generated from a matched tumor sample. However, the enhanced performance of these methods comes at the expense of general applicability as they assume the presence of curated data from known ctDNA fragments or specialized lab protocols. Another approach, iDES [12], finds mutations in paired reads by combining a specialized stranded barcoding scheme and a polisher that aims to filter out an erroneous mutational signal based on a number of criteria, including an error model. Mutations are then called based on the remaining variant signal.

Here we develop a generally applicable ctDNA detection method based on a detailed background error model of individual read positions. This approach aims to capture general read-level error behavior and thus be applicable even for genomic regions where training data is not available. Data from reads known to come from ctDNA is not needed, and all data outside known mutated positions, or from independent normal samples can be used as training data. However, training data that was obtained similarly to the test data will provide the most precise model. Thus, severe changes in laboratory protocols should optimally be accompanied by re-training the model. Some features such as the read position [20], proximity to fragment ends [14], UMI group size [12], GC-content [21], and trinucleotide context [22] have been shown to affect the probability of errors at individual read positions. By modeling their combined effect, the error rate of individual read positions may be predicted. Thereby, a read alignment mismatch, i.e., a non-reference base, with a low predicted error rate can provide more mutational evidence than a mismatch with a high error rate. This allows for improved cfDNA error modeling, which is key to develop accurate ctDNA applications.

The read-level features capture the structural composition of the read, UMI characteristics, and sequencing information. The structural composition includes the strand a read aligns to (forward or reverse), the number of insertions and deletions in the read, and the total size of the underlying fragment. In the read pre-processing, UMIs were used to generate consensus reads with lowered error rates (Additional file 1: Section S2). For each consensus read, we extracted the UMI-group size, the number of reads disagreeing with the consensus at the position, and the overall number of mismatches outside the position of interest. As sequencing-related features, we included the base position in the read (read position), the length of the read sequence after overlap trimming, and whether the read is the first to be sequenced from the read-pair. The read quality (PHRED score) was not included, as it had the same high value for all positions in the UMI-collapsed consensus reads.

Since fragment lengths of cfDNA are influenced by nucleosome binding patterns, the fragment length distribution has peaks at around 162 bp (mono-nucleosomal) and 340 bp (di-nucleosomal) [23]. The error rate tended to be minimized in fragments of these lengths (Fig. 3b). As expected, we observed a lower error rate in consensus reads formed by larger UMI groups [12] (Fig. 3c).

Overall, we saw variation in the error rate for all the presented features (the remaining are shown in Additional file 1: Section S3). Thus, for a given genomic position, different reads may have different error rates due to differences in read-level features. In the following, we present how this variation can be captured and used to potentially improve detection of ctDNA.

We found the most and second most informative feature for modeling the error rate to be respectively the read position and the strand (Fig. 4a). The third feature was the trinucleotide context, indicating that there is a difference in error rate for different contexts, as found by others [19]. The fourth feature was whether the read was the first in the read pair to be sequenced, indicating that systematic errors might be induced by the lab protocol. The fragment length, sequence length, and UMI group size also contribute significantly to the model. The remaining features showed little to no effect on the model performance. This showed that read-level features do contribute to accurate modeling of the error rate, and that they might be at least as important as features derived from the local sequence context.

The explorative feature analysis indicated an increased error rate at the beginning and end of reads (Fig. 3d). DREAMS takes the read position into account and can thereby compensate for the increased error rate at these positions. Other methods such as Mutect2 and Shearwater are not aware of read ends and the performance of these can potentially improve by trimming these. To investigate the effect of trimming, we evaluated the performance of each method when trimming 0, 2, 6, or 12 of the bases in the beginning of reads or 0, 1, or 2 of the bases in the end of reads (Fig. 5b). We found that Shearwater can improve performance by trimming 2 bases from the beginning of reads and Mutect2 can improve by trimming the last base of each read. For DREAMS, the performance is only decreased when trimming, especially in the beginning of reads. be457b7860