Low-Cost Clamp-On Photometers (ClampOD) and Tube Photometers (TubeOD) for Online Cell Density Determination

Optical density (OD) measurement is the gold standard to estimate microbial cell density in aqueous systems. Recording microbial growth curves is essential to assess substrate utilization, gauge sensitivity to inhibitors or toxins, or determine the perfect sampling point. Manual sampling for cuvette-photometer-based measurements can cause disturbances and impact growth, especially for strictly anaerobic or thermophilic microbes. For slow growing microbes, manual sampling can cause data gaps that complicate analysis. Online OD measurement systems provide a solution, but are often expensive and ill-suited for applications such as monitoring microbial growth in custom or larger anaerobic vessels. Furthermore, growth measurements of thermophilic cultures are limited by the heat sensitivity of complex electronics. Here, we present two simple, low-cost, self-assembled photometers—a “TubeOD” for online measurement of anaerobic and thermophilic cultures in Hungate tubes and a “ClampOD” that can be attached to virtually any transparent growth vessel. Both OD-meters can be calibrated in minutes. We detail the manufacturing and calibration procedure and demonstrate continuous acquisition of high quality cell density data of a variety of microbes, including strict anaerobes, a thermophile, and gas-utilizing strains in various glassware. When calibrated and operated within their detection limits (ca. 0.3–90% of the photosensor voltage range), these self-build OD-meters can be used for continuous measurement of microbial growth in a variety of applications, thereby, simplifying and enhancing everyday lab operations.

Megalopodidae (Insecta, Coleoptera): new occurrence and host plant records for Brazil

The Brazilian Megalopodidae fauna consists of 147 species from 11 genera, of which Mastostethus Lacordaire is the most diverse, representing about half of the species. Nevertheless, knowledge about species distributions and their relationships with host plants is still scarce. Since this group has endophytic immatures and bore into branches, they are economically important as they mainly damage Solanaceae species. Thus, this study sought to determine the Megalopodidae species that occur in forest fragments of southwestern and western Paraná and associate them with their host plants. For this purpose, we used material collected with Malaise traps and active manual sampling to determine associations with host plants. A total of 170 specimens of 11 species were sampled, of which Pseudhomalopterus carinatus Pic, 1920 was the most abundant and collected in all fragments. Four species were sampled exclusively through manual collections and associated with their host plants, all of which were new association records for Brazilian megalopodides. Mastostethus pantherinus Lacordaire, 1845 was associated with an Asteraceae for the first time. Additionally, Mastostethus minutus Monrós, 1947 is a new record for Brazil.

UAV Data as an Alternative to Field Sampling to Monitor Vineyards Using Machine Learning Based on UAV/Sentinel-2 Data Fusion

Pests and diseases affect the yield and quality of grapes directly and engender noteworthy economic losses. Diagnosing “lesions” on vines as soon as possible and dynamically monitoring symptoms caused by pests and diseases at a larger scale are essential to pest control. This study has appraised the capabilities of high-resolution unmanned aerial vehicle (UAV) data as an alternative to manual field sampling to obtain sampling canopy sets and to supplement satellite-based monitoring using machine learning models including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme learning regression (ELR) with a new activation function. UAV data were acquired from two flights in Turpan to determine disease severity (DS) and disease incidence (DI) and compared with field visual assessments. The UAV-derived canopy structure including canopy height (CH) and vegetation fraction cover (VFC), as well as satellite-based spectral features calculated from Sentinel-2A/B data were analyzed to evaluate the potential of UAV data to replace manual sampling data and predict DI. It was found that SVR slightly outperformed the other methods with a root mean square error (RMSE) of 1.89%. Moreover, the combination of canopy structure (CS) and vegetation index (VIs) improved prediction accuracy compared with single-type features (RMSEcs of 2.86% and RMSEVIs of 1.93%). This study tested the ability of UAV sampling to replace manual sampling on a large scale and introduced opportunities and challenges of fusing different features to monitor vineyards using machine learning. Within this framework, disease incidence can be estimated efficiently and accurately for larger area monitoring operation.

Manual Sampling and Video Observations: An Integrated Approach to Studying Flower-Visiting Arthropods in High-Mountain Environments

Despite the rising interest in biotic interactions in mountain ecosystems, little is known about high-altitude flower-visiting arthropods. In particular, since the research in these environment can be limited or undermined by harsh conditions and logistical difficulties, it is mandatory to develop effective approaches that maximize possibilities to gather high-quality data. Here we compared two different methods, manual sampling and video observations, to investigate the interactions between the high-mountain arthropod community and flowers of Androsace brevis (Primulaceae), a vulnerable endemic alpine species with a short flowering period occurring in early season. We manually sampled flower-visiting arthropods according to the timed-observations method and recorded their activity on video. We assessed differences and effectiveness of the two approaches to estimate flower-visiting arthropod diversity and to identify potential taxa involved in A. brevis pollination. Both methods proved to be effective and comparable in describing the diversity of flower visitors at a high taxonomic level. However, with manual sampling we were able to obtain a fine taxonomic resolution for sampled arthropods and to evaluate which taxa actually carry A. brevis pollen, while video observations were less invasive and allowed us to assess arthropod behavior and to spot rare taxa. By combining the data obtained with these two approaches we could accurately identify flower-visiting arthropods, characterize their behavior, and hypothesize a role of Hymenoptera Apoidea and Diptera Brachycera in A. brevis pollination. Therefore, we propose integrating the two approaches as a powerful instrument to unravel interactions between flowering plants and associated fauna that can provide crucial information for the conservation of vulnerable environments such as high-mountain ecosystems.

Validation of Additional Approaches and Applications for Using the Continuous and Manual Sampling Devices for Raw Beef Trim

In this work, the goal was to determine efficacy of MicroTally™-based sampling in scenarios commonly encountered in the commercial beef processing industry, but outside of the parameters evaluated during the initial proof-of-concept work. The data were derived from 1,650 matched samples collected from 540 individual combo bins at six commercial beef processing plants comparing MicroTally-based sampling (Continuous and Manual Sampling Devices [CSD and MSD]) to N60 Excision and/or N60 Plus methods. Mounting a 61 cm CSD cartridge to a 30 cm wide conveyor provided sampling that is equivalent to N60 excision and N60+ methods. Mounting a CSD to a chute instead of a conveyor was equivalent to the N60 Plus sampling method. The CSD was shown to be effective for sampling when used in conjunction with a “swinging arm trim diverter” and receiving product in batch mode as opposed to continuous flow. MSD sampling of oval combo bins with trim surface area (≈ 0.93 m 2 , ≈ 1,439 in 2 ) less than 1 m 2 (1,600 in 2 ) was shown to be equivalent to the N60 Plus sample collection method. PAA applied at the end of the trim conveyor did not negatively impact pathogen index target detection of the CSD even if the samples were shipped overnight before analysis. Pathogen index targets were demonstrated to be useful tools for validating methods designed to measure pathogen prevalence. The data presented herein support equivalency criteria of within 0.5 log CFU/sample for indicator organism counts. These data collectively support various alternative applications of MicroTally-based trim sampling and the application and interpretation of alternative methods for pathogen detection.