RESUMEN
Gracilacus wuae n. sp. from soil associated with cow parsnip in Ontario, Canada is described and illustrated. Morphologically, females have a long stylet ranging from 80 to 93 µm long, the lip region not offset from the body contour, without lateral lips but with large and flat submedian lobes, the mouth opening slit-like elongated laterally and surrounded by lateral flaps, the excretory pore is anterior to the knobs of the stylet; males without stylet and the pharynx degenerated. The fourth-stage juveniles lack a stylet, the pharynx degenerated, and can be differentiated into preadult females and males based on the position of the genital primordia. The third-stage juveniles are similar to females but smaller. Phylogenetic studies using the rDNA small subunit 18S, large subunit 28S D2/D3, and internal transcribed spacer (ITS) sequences collectively provide evidence of a grouping with other Gracilacus and some species of Paratylenchus with stylet length of females longer than 41 µm deposited in GenBank.
RESUMEN
This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.
RESUMEN
Nematodes are considered among the most difficult animals to identify. DNA-based diagnostic methods have already gained acceptance in applications ranging from quarantine determinations to assessments of biodiversity. Researchers are currently in an information-gathering mode, with intensive efforts applied to accumulating nucleotide sequence of 18S and 28S ribosomal genes, internally transcribed spacer regions, and mitochondrial genes. Important linkages with collateral data such as digitized images, video clips and specimen voucher web pages are being established on GenBank and NemATOL, the nematode-specific Tree of Life database. The growing DNA taxonomy of nematodes has lead to their use in testing specific short sequences of DNA as a "barcode" for the identification of all nematode species.