Much of today’s molecular science revolves around next-generation sequencing. Frequently, the first step in analyzing such data is aligning sequencing reads to a reference genome. This step is often ...taken for granted, but any analysis downstream of the alignment will be affected by the aligner’s ability to correctly map sequences. In most cases, for research into chromatin structure and nucleosome positioning, ATAC-seq, ChIP-seq, and MNase-seq experiments use short read lengths. How well aligners manage these reads is critical. Most aligner programs will output mapped reads and unmapped reads. However, from a biological point of view, reads will fall into one of three categories: correctly mapped, incorrectly mapped, and unmapped. While increased sequencing depth can often compensate for unmapped reads, incorrectly and correctly mapped reads appear algorithmically identical but can produce biologically significant alterations in the results. For this reason, we are benchmarking various alignment programs to determine their propensity to incorrectly map short reads. As short-read alignment is an important step in ATAC-seq, ChIP-seq, and MNase-seq experiments, caution should be taken in mapping reads to ensure that the most accurate conclusions can be made from the data generated. Our analysis is intended to help investigators new to the field pick the alignment program best suited for their experimental conditions. In general, the aligners we tested performed well. BWA, Bowtie2, and Chromap were all exceptionally accurate, and we recommend using them. Furthermore, we show that longer read lengths do in fact lead to more accurate mappings.
This study investigated how factors and barriers to flu vaccination among college students has changed over the past 16 years. Data were collected from 440 students using a survey and compared to ...previous data from the same university. Respondents were also asked about their experiences with Covid-19 and its effect on their intent to vaccinate. We found that vaccination rates had increased from 12.4 to 30.5%. Among the unvaccinated, expense, fear of getting influenza from vaccination, fear of side effects, and lack of information have decreased by 28%, 20%, 17%, and 15% respectively. Time, convenience, and perceived risk are still significant barriers to vaccination. Students are getting more encouragement to vaccinate from their health care providers and parents, but it is becoming less effective. The Covid-19 pandemic has changed vaccine attitudes and vaccine fatigue has been a large contributor. Additionally, political affiliation has become a predictor of flu vaccine uptake with conservatives being less likely to vaccinate. There has also been a shift in motivation from concern for personal safety to concern for public safety.
Phosphines are extremely important ligands in organometallic chemistry, and their donor or acceptor ability can be measured through the Tolman electron parameter (TEP). Here, we describe the ...development of a TEP machine learning model (called TEPid) that provides nearly instantaneous calculation of experimentally calibrated CO vibrational stretch frequencies for (R)3P–Ni0(CO)3 complexes. This machine learning model with an error of less than 1 cm–1 (compared to density functional theory (DFT) calculated values) was developed using >4,000 DFT calculated (R)3P–Ni0(CO)3 TEP values and 19 connectivity-based descriptors associated with SMILES strings. We also built a web-based interface to run the machine learning model where phosphine SMILES strings can be entered and TEP values returned. We applied this TEPid model to examine the donor and acceptor capabilities of phosphines in the large Kraken phosphine database. This showed that the Kraken database is skewed toward donor phosphines. In the same spirit of the Kraken database, we generated tens of thousands of new experimentally based phosphines that, when combined with Kraken phosphines, provide an electronically balanced ligand library.
Three-dimensional vector fields are common datasets throughout the sciences. They often represent physical phenomena that are largely invisible to us in the real world, like wind patterns and ocean ...currents. Computer-aided visualization is a powerful tool that can represent data in any way we choose through digital graphics. Visualizing 3D vector fields is inherently difficult due to issues such as visual clutter, self-occlusion, and the difficulty of providing depth cues that adequately support the perception of flow direction in 3D space. Cutting planes are often used to overcome these issues by presenting slices of data that are more cognitively manageable. The existing literature provides many techniques for visualizing the flow through these cutting planes; however, there is a lack of empirical studies focused on the underlying perceptual cues that make popular techniques successful. The most valuable depth cue for the perception of other kinds of 3D data, notably 3D networks and 3D point clouds, is structure-from-motion (also called the Kinetic Depth Effect); another powerful depth cue is stereoscopic viewing, but none of these cues have been fully examined in the context of flow visualization. This dissertation presents a series of quantitative human factors studies that evaluate depth and direction cues in the context of cutting plane glyph designs for exploring and analyzing 3D flow fields. The results of the studies are distilled into a set of design guidelines to improve the effectiveness of 3D flow field visualizations, and those guidelines are implemented as an immersive, interactive 3D flow visualization proof-of-concept application.
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