Composition and distribution of fish environmental DNA in an Adirondack watershed

Background Environmental DNA (eDNA) surveys are appealing options for monitoring aquatic biodiversity. While factors affecting eDNA persistence, capture and amplification have been heavily studied, watershed-scale surveys of fish communities and our confidence in such need further exploration. Methods We characterized fish eDNA compositions using rapid, low-volume filtering with replicate and control samples scaled for a single Illumina MiSeq flow cell, using the mitochondrial 12S ribosomal RNA locus for taxonomic profiling. Our goals were to determine: (1) spatiotemporal variation in eDNA abundance, (2) the filtrate needed to achieve strong sequencing libraries, (3) the taxonomic resolution of 12S ribosomal sequences in the study environment, (4) the portion of the expected fish community detectable by 12S sequencing, (5) biases in species recovery, (6) correlations between eDNA compositions and catch per unit effort (CPUE) and (7) the extent that eDNA profiles reflect major watershed features. Our bioinformatic approach included (1) estimation of sequencing error from unambiguous mappings and simulation of taxonomic assignment error under various mapping criteria; (2) binning of species based on inferred assignment error rather than by taxonomic rank; and (3) visualization of mismatch distributions to facilitate discovery of distinct haplotypes attributed to the same reference. Our approach was implemented within the St. Regis River, NY, USA, which supports tribal and recreational fisheries and has been a target of restoration activities. We used a large record of St. Regis-specific observations to validate our assignments. Results We found that 300 mL drawn through 25-mm cellulose nitrate filters yielded greater than 5 ng/µL DNA at most sites in summer, which was an approximate threshold for generating strong sequencing libraries in our hands. Using inferred sequence error rates, we binned 12S references for 110 species on a state checklist into 85 single-species bins and seven multispecies bins. Of 48 bins observed by capture survey in the St. Regis, we detected eDNA consistent with 40, with an additional four detections flagged as potential contaminants. Sixteen unobserved species detected by eDNA ranged from plausible to implausible based on distributional data, whereas six observed species had no 12S reference sequence. Summed log-ratio compositions of eDNA-detected taxa correlated with log(CPUE) (Pearson’s R = 0.655, P < 0.001). Shifts in eDNA composition of several taxa and a genotypic shift in channel catfish (Ictalurus punctatus) coincided with the Hogansburg Dam, NY, USA. In summary, a simple filtering apparatus operated by field crews without prior expertise gave useful summaries of eDNA composition with minimal evidence of field contamination. 12S sequencing achieved useful taxonomic resolution despite the short marker length, and data exploration with standard bioinformatic tools clarified taxonomic uncertainty and sources of error.

Clustering with varying risks of false assignments in discrete latent variable model

In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases.

Error Analysis on Unindra Student’s Sentence Tenses Assignment

<p class="abstractcontent">Knowing the pattern of tenses is very important to the students, because it can help them to do a good sentence. If the students unknown the pattern of using tenses in sentences, the students are confused to make a good sentence. The teacher should try to correct their error by error analysis. Error analysis is really very important to students, because teachers know the mistaken students done in making a sentence by using in each tenses. In this study, the researcher used descriptive method which is the data was taken by student’s an assignment at the first students of UNINDRA. First, teachers asked students to make sentences based on eight tenses. They are Present Tense; Present Continuous Tense; Present Perfect Tense; Simple Future Tense; Past Tense; Past Continuous Tense; Past Continuous Tense; Past Perfect Tense; Future Perfect Tense. Second, After doing sentences in each tenses, the research done identify based on the pattern of sentences; Third, the last steps researcher analysis the assignment in make the table consist of table 1 the amount of error done; table 2 the error sentences students and correction; table 3 the reasons why sentences are difficulties to the students and easier. The results of these study 60 percentages students UNINDRA made good sentences in eight tenses; 40 percentages did not remember the pattern of tenses; 30 percentages made the error of changed verb; 30 percentages used time action to make sentence in each tenses.</p><p class="abstractcontent">Key words; Tenses, Assignment, Error, Analysis, Pattern</p>

Structural revisions of small molecules reported to cross-link G-quadruplex DNA in vivo reveal a repetitive assignment error in the literature

Two molecules of mistaken identity are addressed. Uncovering these assignment errors led us to formulate more general guidelines about additional misassignments in cases of published bis-imines derived from 1,2-phenylenediamine and hydroxybenzaldehydes having no substituent in ortho-positions. The main purpose of this article is to highlight this repetitive assignment error in the literature and thus increase the likelihood of correct assignments in future papers.

Characterizing AMV Height-Assignment Error by Comparing Best-Fit Pressure Statistics from the Met Office and ECMWF Data Assimilation Systems

To ensure realistic use of atmospheric motion vector (AMV) observations in data assimilation, the error characteristics of the observation type need to be known and carefully taken into account. Assigning a height to the tracked feature is one of the most significant error sources for AMV observations. In this article, the characteristics of the AMV height-assignment error are studied by comparing model best-fit pressure statistics between the Met Office and ECMWF data assimilation systems. The aim is to provide detailed uncertainty estimates for the assigned pressure and to demonstrate that the best-fit pressure enables reliable estimation of the uncertainties in the AMV height assignment. Typical values for the standard deviation of the difference between the assigned pressure and the best-fit pressure are 50–80 hPa at high levels, 115–165 hPa at midlevels, and 60–125 hPa at low levels, depending on satellite, channel, and height-assignment method. Observed minus best-fit pressure biases are mostly within the range of ±50 hPa. The results are very similar for the Met Office and ECMWF systems, suggesting that the pressure differences are not strongly dependent on the data assimilation system. Furthermore, the findings are in good agreement with the expected characteristics of the height-assignment methods and quality of the observations. Thus, best-fit pressure statistics give reliable information about the uncertainties in the AMV height assignment.