Online Advanced Bacterial Identification Software, an Original Tool for Phenotypic Bacterial Identification

The objective of the study is to present and validate an original online Advanced Bacterial Identification Software, ABIS, by comparison to a commercially available, standardized identification system, API strips and apiweb™ bioMerieux software. Methods and results: presentation of ABIS online software, phenotypic bacterial identification of 16 reference strains and 123 wild isolates by ABIS and apiweb TM bioMerieux software and comparative analysis of results. Closed results were obtained (same taxa) for reference and wild strains of Enterobacteriaceae, Pasteurellaceae, Bacillaceae, Lactobacillaceae, Staphylococcaceae, Streptococcaceae, and other. Conclusions: Apiweb™ confirmed the results of ABIS, overall, average identification percent for ABIS being 91.8% and 90.4% for apiweb TM. ABIS online is a powerful tool for microbiology lab and the Encyclopedia connection provides essential information about the ecological significance, pathology and other features of the identified strains.

Features of peptide fragmentation spectra in single cell proteomics

The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single cell proteomics specializes in identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single cell spectra, including loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity and distinct fragmentation pattern compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.

The Need for Community Standards to Enable Accurate Comparison of Glycoproteomics Algorithm Performance

Protein glycosylation that mediates interactions among viral proteins, host receptors, and immune molecules is an important consideration for predicting viral antigenicity. Viral spike proteins, the proteins responsible for host cell invasion, are especially important to be examined. However, there is a lack of consensus within the field of glycoproteomics regarding identification strategy and false discovery rate (FDR) calculation that impedes our examinations. As a case study in the overlap between software, here as a case study, we examine recently published SARS-CoV-2 glycoprotein datasets with four glycoproteomics identification software with their recommended protocols: GlycReSoft, Byonic, pGlyco2, and MSFragger-Glyco. These software use different Target-Decoy Analysis (TDA) forms to estimate FDR and have different database-oriented search methods with varying degrees of quantification capabilities. Instead of an ideal overlap between software, we observed different sets of identifications with the intersection. When clustering by glycopeptide identifications, we see higher degrees of relatedness within software than within glycosites. Taking the consensus between results yields a conservative and non-informative conclusion as we lose identifications in the desire for caution; these non-consensus identifications are often lower abundance and, therefore, more susceptible to nuanced changes. We conclude that present glycoproteomics softwares are not directly comparable, and that methods are needed to assess their overall results and FDR estimation performance. Once such tools are developed, it will be possible to improve FDR methods and quantify complex glycoproteomes with acceptable confidence, rather than potentially misleading broad strokes.

Development and individual identification of captive Ziegler’s crocodile newt Tylototriton ziegleri

Tylototriton ziegleri is a newt native to Vietnam with a very limited range and assessed as Vulnerable by the IUCN. It is rarely found in captivity. Larval husbandry, based on field conditions, had mixed success with a high proportion of egg hatch and relatively rapid larval growth rates but also substantial larval mortality, probably due to high stocking density. Larvae started to hatch after 23 days and after 77-79 days had metamorphosed at a mean mass of 1.6 g; juveniles grew at an average of about 0.3 g/month. The primary animal carer was able to use wart patterns to distinguish between four individuals but scope for use on a larger scale was not supported when tested using computer-assisted individual identification software (WildID) or expert observers.

Automated classification of bat echolocation call recordings with artificial intelligence

Acoustic recorders are commonly used to remotely monitor and collect data on bats (Order Chiroptera). These efforts result in many acoustic recordings that must be classified by a bat biologist with expertise in call classification in order to obtain useful information. The rarity of this expertise and time constraints have prompted efforts to automatically classify bat species in acoustic recordings using a variety of learning methods. There are several software programs available for this purpose, but they are imperfect and the United States Fish and Wildlife Service often recommends that a qualified acoustic analyst review bat call identifications even if using these software programs. We sought to build a model to classify bat species using modern computer vision techniques. We used images of bat echolocation calls (i.e., plots of the pulses) to train deep learning computer vision models that automatically classify bat calls to species. Our model classifies 10 species, five of which are protected under the Endangered Species Act. We evaluated our models using standard model validation procedures, but we also performed two out-of-distribution tests. For the out-of-distribution tests, an entire dataset was withheld from the procedure before splitting the data into training and validation sets. We found that our validation accuracy (93%) and out-of-distribution accuracy (90%) were higher than when we used Kaleidoscope Pro and BCID software (65% and 61% accuracy, respectively) to evaluate the same calls. Our results suggest that our approach is effective at classifying bat species from acoustic recordings, and our trained model will be incorporated into new bat call identification software: WEST-EchoVision.

PCirc: random forest-based plant circRNA identification software

Background Circular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an important role in gene regulation. Therefore, identifying circRNAs from increasing amounts of RNA-seq data is very important. However, traditional circRNA recognition methods have limitations. In recent years, emerging machine learning techniques have provided a good approach for the identification of circRNAs in animals. However, using these features to identify plant circRNAs is infeasible because the characteristics of plant circRNA sequences are different from those of animal circRNAs. For example, plants are extremely rich in splicing signals and transposable elements, and their sequence conservation in rice, for example is far less than that in mammals. To solve these problems and better identify circRNAs in plants, it is urgent to develop circRNA recognition software using machine learning based on the characteristics of plant circRNAs. Results In this study, we built a software program named PCirc using a machine learning method to predict plant circRNAs from RNA-seq data. First, we extracted different features, including open reading frames, numbers of k-mers, and splicing junction sequence coding, from rice circRNA and lncRNA data. Second, we trained a machine learning model by the random forest algorithm with tenfold cross-validation in the training set. Third, we evaluated our classification according to accuracy, precision, and F1 score, and all scores on the model test data were above 0.99. Fourth, we tested our model by other plant tests, and obtained good results, with accuracy scores above 0.8. Finally, we packaged the machine learning model built and the programming script used into a locally run circular RNA prediction software, Pcirc (https://github.com/Lilab-SNNU/Pcirc). Conclusion Based on rice circRNA and lncRNA data, a machine learning model for plant circRNA recognition was constructed in this study using random forest algorithm, and the model can also be applied to plant circRNA recognition such as Arabidopsis thaliana and maize. At the same time, after the completion of model construction, the machine learning model constructed and the programming scripts used in this study are packaged into a localized circRNA prediction software Pcirc, which is convenient for plant circRNA researchers to use.

Alteration Mineralogy of the Zhengguang Epithermal Au-Zn Deposit, Northeast China: Interpretation of Shortwave Infrared Analyses During Mineral Exploration and Assessment

Alteration mineralogy from shortwave infrared (SWIR) spectroscopy was compared with X-ray diffraction (XRD) analyses for samples from the Zhengguang intermediate sulfidation epithermal Au-Zn deposit, eastern Central Asian orogenic belt, northeast China. The SWIR and XRD analyses indicate that alteration minerals in the vein-adjacent halo mainly comprise quartz, illite, and locally pyrite (QIP) and chlorite, whereas samples from the pervasive propylitic alteration of host basaltic andesite lava contain epidote, chlorite, carbonate, montmorillonite, and locally illite. SWIR mineral identifications from automated mineral identification software may not always be accurate; thus, the results should be validated by the user. The wavelength position of the Al-OH (~2,200 nm; wAlOH) absorption feature can be used to approximate the composition of illite or white mica. However, caution is required when using the wAlOH value to assess paleotemperatures, as the composition of illite can be influenced by the composition of the host rocks or the hydrothermal fluid. In addition, values of the illite spectral maturity (ISM; ratio of the depth of the ~2,200 nm minima divided by the ~1,900 nm minima) can be affected by the presence of other hydrous minerals, quartz-sulfide veins, and absorption intensity (which can be a function of rock coloration). Despite these cautions, the spatial distribution and variation of the wAlOH and ISM values for illite suggest that the high paleotemperature hydrothermal upflow zones related to the Zhengguang Au-Zn deposit were located below ore zones I and IV, which are predicted to be proximal to the intrusive center of the system.