An orbital angular momentum acoustic metasurface for underwater defect detection

Fourier diffraction theorem can rapidly predict scattering characteristics of scatterers. This paper theoretically proposes Orbital Angular Momentum (OAM) to simplify the reference library in the underwater Fourier diffraction theorem. An acoustic metasurface is designed to replace the traditional phased array, meanwhile the underwater defect detection combining OAM and Fourier diffraction theorem is verified in the simulation. This acoustic metasurface has a high signal-to-noise ratio (SNR) when used for underwater defect detection. Compared with the traditional underwater defect detection method, the underwater defect detection method proposed in this paper has the advantages of simple structure and no reference pattern library.

Identifying and Translating Subjective Content Descriptions Among Texts

An agent pursuing a task may work with a corpus of documents as a reference library. Subjective content descriptions (SCDs) provide additional data that add value in the context of the agent’s task. In the pursuit of documents to add to the corpus, an agent may come across new documents where content text and SCDs from another agent are interleaved and no distinction can be made unless the agent knows the content from somewhere else. Therefore, this paper presents a hidden Markov model-based approach to identify SCDs in a new document where SCDs occur inline among content text. Additionally, we present a dictionary selection approach to identify suitable translations for content text and SCDs based on [Formula: see text]-grams. We end with a case study evaluating both approaches based on simulated and real-world data.

DNA barcode reference library construction and genetic diversity and structure analysis of Amomum villosum Lour. (Zingiberaceae) populations in Guangdong Province

Background Amomum villosum Lour. is the plant that produces the famous traditional Chinese medicine Amomi Fructus. Frequent habitat destruction seriously threatens A. villosum germplasm resources. Genetic diversity is very important to the optimization of germplasm resources and population protection, but the range of inherited traits within A. villosum is unclear. In this study, we analyzed the genetic diversity and genetic structures of A. villosum populations in Guangdong and constructed a local reference DNA barcode library as a resource for conservation efforts. Methods DNA barcoding and Inter-Simple Sequence Repeat (ISSR) markers were used to investigate the population genetics of A. villosum. Five universal DNA barcodes were amplified and used in the construction of a DNA barcode reference library. Parameters including percentage of polymorphic sites (PPB), number of alleles (Na), effective number of alleles (Ne), Nei’s gene diversity index (H), and Shannon’s polymorphism information index (I) were calculated for the assessment of genetic diversity. Genetic structure was revealed by measuring Nei’s gene differentiation coefficient (Gst), total population genetic diversity (Ht), intra-group genetic diversity (Hs), and gene flow (Nm). Analysis of molecular variance (AMOVA), Mantel tests, unweighted pair-group method with arithmetic mean (UPGMA) dendrogram, and principal co-ordinates (PCoA) analysis were used to elucidate the genetic differentiation and relationship among populations. Results A total of 531 sequences were obtained from the five DNA barcodes with no variable sites from any of the barcode sequences. A total of 66 ISSR bands were generated from A. villosum populations using the selected six ISSR primers; 56 bands, 84.85% for all the seven A. villosum populations were polymorphic. The A. villosum populations showed high genetic diversity (H = 0.3281, I = 0.4895), whereas the gene flow was weak (Nm = 0.6143). Gst (0.4487) and AMOVA analysis indicated that there is obvious genetic differentiation amongA. villosum populations and more genetic variations existed within each population. The genetic relationship of each population was relatively close as the genetic distances were between 0.0844 and 0.3347.

Characterizing Billbug (Sphenophorus spp.) Seasonal Biology Using DNA Barcodes and a Simple Morphometric Analysis

Billbugs (Sphenophorus spp.) are a complex of grass-feeding weevil species that reduce the aesthetic and functional qualities of turfgrass. Effective billbug monitoring and management programs rely on a clear understanding of their seasonal biology. However, our limited understanding of regional variation in the species compositions and seasonal biology of billbugs, stemming primarily from our inability to identify the damaging larval stage to species level, has hindered efforts to articulate efficient IPM strategies to growers. We used a combination of DNA barcoding methods and morphometric measures to begin filling critical gaps in our understanding of the seasonal biology of the billbug species complex across a broad geographic range. First, we developed a DNA barcoding reference library using cytochrome oxidase subunit 1 (COI) sequences from morphologically identified adult billbugs collected across Indiana, Missouri, Utah and Arizona. Next, we used our reference library for comparison and identification of unknown larval specimens collected across the growing season in Utah and Indiana. Finally, we combined our DNA barcoding approach with larval head capsule diameter, a proxy for developmental instar, to develop larval phenology charts. Adult COI sequences varied among billbug species, but variation was not influenced by geography, indicating that this locus alone was useful for resolving larval species identity. Overlaid with head capsule diameter data from specimens collected across the growing season, a better visualization of billbug species composition and seasonal biology emerged. This approach will provide researchers with the tools necessary to fill critical gaps in our understanding of billbug biology and facilitate the development of turfgrass pest management programs.

On the determination of 3D position and orientation of spheroidal particles using defocusing and deep learning

Tracking the 3D position of tracer particles or small objects like cells or unicellular organisms in miniaturized lab-on-a-chip or biomedical devices is complicated since it is often not possible in these setups to use multi-camera approaches. Most successful single-camera approaches for these applications are based on holography or defocusing. Holographic methods have been used to track complex objects such has bacteria (Bianchi et al. (2019)) and even to estimate their orientation (Wang et al. (2016)). However, these methods require a complex and expensive experimental setup which is not always available in research laboratories. On the other hand, defocusing methods work with conventional microscopic optics, are easy to implement, and have shown excellent results in 3D PTV experiments (Qiu et al. (2019)). One main drawback is that they normally work only with spherical and mono-dispersed tracer particles. A defocusing method that has potential to measure non-spherical particles is the General Defocusing Particle Tracking (Barnkob and Rossi (2020)) which is based on pattern recognition and can be conceptually extended to more complex tasks by extending the reference library of particle images, including not only spherical particles at different depth positions, but also non-spherical particles at different orientations. However, whether this approach could work in practice is still unknown. First, is the information contained in simple defocused images sufficient to reconstruct depth and orientation of non-spherical particles, and eventually under which circumstances? Second, how to practically collect the labelled reference images?

Saint Peter and Saint Paul Archipelago barcoded: fish diversity in the remoteness and DNA barcodes reference library for metabarcoding monitoring

Anthropogenic pressures have been depleting the global biodiversity. In order to monitor the changes in ecosystems, molecular techniques can be used to characterize species composition. Among molecular markers capable of identifying species, the COI is the most used, and its sequencing is the standard procedure of how taxonomic information can be surveyed. Despite this, new possibilities of biodiversity profiling have become possible through the assessment of highly fragmented DNA molecules in environmental samples. Now, medium- and short-length markers are used in metabarcoding studies. Here, a survey of marine fish from the Saint Peter and Saint Paul Archipelago was barcoded, in which the COI barcode procedure identified 21 species of 11 families of fish. Then, the first extensive COI library of these islands located in isolation was constructed; from these sequences, the most appropriate primer pair for future metabarcoding studies was identified. The new Saint Peter and Saint Paul sequence database has 9,183 sequences from 165 species and 62 families of fish. The overall mean distance among all sequences was 0.4. This distance reveals that the archipelago is a reservoir of biodiversity as this attribute is higher than other islands around the world. Due to this, the protection of the archipelago should be enhanced and well monitored with science-based approaches such as DNA metabarcoding. In this case, the primer pair specifically designed from this library should be considered.

High resolution ancient sedimentary DNA shows that alpine plant biodiversity is a result of human land use

Alpine areas are well known biodiversity hotspots, but their future may be threatened by expanding forest and changing human land use. Here, we reconstructed past vegetation, climate, and livestock over the past ~ 12,000 years from Lake Sulsseewli (European Alps), based on sedimentary ancient DNA, pollen, spores, chironomids, and microcharcoal. We assembled a highly-complete local DNA reference library (PhyloAlps, 3,923 plant species), and used this to obtain an exceptionally rich sedaDNA record of 366 plant taxa. The vegetation mainly responded to temperature during the first half of the Holocene, while human activity drove changes from 6 ka onwards. Land-use shifted from episodic grazing (Neolithic, Bronze Age) to agropastoral intensification (Medieval Age). This prompted a coexistence of species typically found at different elevational belts, thereby increasing plant richness to levels that characterise present-day alpine diversity. Our results indicate that traditional agropastoral activities should be maintained to prevent reforestation and preserve alpine plant biodiversity.

Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species

Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.