Sort by
Evaluation of a Formulation of Bacillus subtilis for Control of Phytophthora Blight of Bell Pepper.

Phytophthora blight, caused by Phytophthora capsici, is one of the most economically significant diseases of bell pepper in the United States. Over the past several decades, isolates of P. capsici exhibiting resistance to mefenoxam and other fungicides have been reported. Fungicide resistance coupled with an increased market for organically grown crops has led to interest in biological control as a disease management option. In this work, an isolate of Bacillus subtilis (AFS032321) was evaluated for control of Phytophthora blight of bell pepper in the greenhouse and field. A 28% active ingredient wettable powder formulation of the strain was applied as a soil drench at transplanting prior to inoculation. Treatment with this formulation of B. subtilis significantly reduced the area under the disease progress curve (AUDPC) by up to 52% compared to untreated control plants in greenhouse tests. Comparisons between applying the biocontrol weekly after seeding for 5 weeks versus a single application at transplanting (5 weeks) indicated no significant benefits of additional applications. The formulation of B. subtilis reduced disease caused by a mefenoxam-resistant isolate of P. capsici, while mefenoxam failed. The biocontrol efficacy of formulated strains was not affected in different soil types or potting media. However, disease was more severe in sandy soils. In field experiments that were conducted with a mefenoxam-sensitive isolate, disease incidence and severity of Phytophthora blight were significantly reduced at all rates of B. subtilis in 2019 except the 16.8 kg ha-1 rate. In both years, mefenoxam was more effective than B. subtilis in controlling disease in the field. B. subtilis did not affect the spatial dynamics of pathogen spread within rows. While the precise mechanism(s) of action is unclear, in vitro dual-culture tests suggest direct antagonism, as B. subtilis significantly inhibited colony growth of P. capsici. AgBiome has recently released a new formulation of the AFS032321 strain named Theia, with higher active ingredients for commercial applications and biocontrol of P. capsici.

Just Published
Relevant
Seasonal Variation in Grapevine Red Blotch Virus Titer in Relation to Disease Symptom Expression in Vineyards

Grapevine red blotch virus (GRBV) is the causative agent of red blotch disease. Limited information is available on the seasonal variation of GRBV titer in relation to disease symptom expression in vineyards across the United States. In this study, no statistically significant difference in GRBV titer was found among asymptomatic infected vines in June ( P = 0.451) and among symptomatic infected vines in October ( P = 0.068) in a diseased ‘Cabernet franc’ vineyard in California, regardless of the years symptomatic, one to seven, as shown by qPCR. Similarly, there were no statistically significant differences in GRBV titer as it related to isolates of the two phylogenetic clades in asymptomatic infected ‘Cabernet franc’ and ‘Cabernet Sauvignon’ vines in June ( P = 0.138 and P = 0.778, respectively) and in symptomatic infected vines in October ( P = 0.806 and P = 0.490, respectively). GRBV titer differed among cultivars in diseased California vineyards ( P < 0.001) and increased over the course of the growing season in infected ‘Merlot’ and ‘Cabernet franc’ vines, but not in ‘Cabernet Sauvignon’ vines. Patterns observed in California were consistent in New York and Georgia vineyards. In a Geneva double curtain-trellised ‘Cabernet Sauvignon’ vineyard in Georgia, GRBV distribution was uneven between cordons, and virus titer was variable within the vine canopy in June ( P = 0.017) but not in October ( P = 0.107). This work revealed consistent patterns of GRBV titer during a growing season in different vineyards across the United States. It also highlighted relatively high virus titer in symptomless grapevines in June, when Spissistilus festinus-mediated GRBV transmission is documented in northern California.

Open Access Just Published
Relevant
Representation Learning for Stack Overflow Posts: How Far Are We?

The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on the selection of representation models for Stack Overflow posts. As the volume of literature on Stack Overflow continues to burgeon, it highlights the need for a powerful Stack Overflow post representation model and drives researchers’ interest in developing specialized representation models that can adeptly capture the intricacies of Stack Overflow posts. The state-of-the-art (SOTA) Stack Overflow post representation models are Post2Vec and BERTOverflow, which are built upon neural networks such as convolutional neural network and transformer architecture (e.g., BERT). Despite their promising results, these representation methods have not been evaluated in the same experimental setting. To fill the research gap, we first empirically compare the performance of the representation models designed specifically for Stack Overflow posts (Post2Vec and BERTOverflow) in a wide range of related tasks (i.e., tag recommendation, relatedness prediction, and API recommendation). The results show that Post2Vec cannot further improve the SOTA techniques of the considered downstream tasks, and BERTOverflow shows surprisingly poor performance. To find more suitable representation models for the posts, we further explore a diverse set of transformer-based models, including (1) general domain language models (RoBERTa, Longformer, and GPT2) and (2) language models built with software engineering related textual artifacts (CodeBERT, GraphCodeBERT, seBERT, CodeT5, PLBart, and CodeGen). This exploration shows that models like CodeBERT and RoBERTa are suitable for representing Stack Overflow posts. However, it also illustrates the “No Silver Bullet” concept, as none of the models consistently wins against all the others. Inspired by the findings, we propose SOBERT, which employs a simple yet effective strategy to improve the representation models of Stack Overflow posts by continuing the pre-training phase with the textual artifact from Stack Overflow. The overall experimental results demonstrate that SOBERT can consistently outperform the considered models and increase the SOTA performance significantly for all the downstream tasks.

Relevant
Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health

When data is scarce, software analytics can make many mistakes. For example, consider learning predictors for open source project health (e.g., the number of closed pull requests in 12 months time). The training data for this task may be very small (e.g., 5 years of data, collected every month means just 60 rows of training data). The models generated from such tiny datasets can make many prediction errors. Those errors can be tamed by a landscape analysis that selects better learner control parameters. Our niSNEAK tool (a) clusters the data to find the general landscape of the hyperparameters, then (b) explores a few representatives from each part of that landscape. niSNEAK is both faster and more effective than prior state-of-the-art hyperparameter optimization algorithms (e.g., FLASH, HYPEROPT, OPTUNA). The configurations found by niSNEAK have far less error than other methods. For example, for project health indicators such as C = number of commits, I = number of closed issues, and R = number of closed pull requests, niSNEAK ’s 12-month prediction errors are {I=0%, R=33% C=47%}, whereas other methods have far larger errors of {I=61%,R=119% C=149%}. We conjecture that niSNEAK works so well since it finds the most informative regions of the hyperparameters, then jumps to those regions. Other methods (that do not reflect over the landscape) can waste time exploring less informative options. Based on the preceding, we recommend landscape analytics (e.g., niSNEAK ) especially when learning from very small datasets. This article only explores the application of niSNEAK to project health. That said, we see nothing in principle that prevents the application of this technique to a wider range of problems. To assist other researchers in repeating, improving, or even refuting our results, all our scripts and data are available on GitHub at https://github.com/zxcv123456qwe/niSneak.

Open Access
Relevant
Evaluation of Soybean Genotypes (Glycine max and G. soja) for Resistance to the Root-Knot Nematode, Meloidogyne enterolobii.

Potential resistance to the root-knot nematode (RKN) Meloidogyne enterolobii in 72 Glycine soja and 44 G. max soybean genotypes was evaluated in greenhouse experiments. Approximately 2,500 eggs of M. enterolobii were inoculated on each soybean genotype grown in a steam sterilized 1:1 sand to soil mixture. Sixty days postinoculation, plants were destructively harvested to determine the host status. The host status of each soybean genotype was determined by assessing root galling severity and calculating the final eggs per root system divided by the initial inoculum, or the reproduction factor (Rf). Five G. soja soybean genotypes were identified as resistant (Rf < 1) to M. enterolobii: '407202', '407239', '424083', '507618', and '639621'. None of the tested G. max soybean genotypes were identified as resistant to M. enterolobii. Some of the G. max genotypes determined to be susceptible to M. enterolobii include 'Hagood', 'Avery', 'Rhodes', 'Santee', and 'Bryan'. The genotype 'Bryan' had the lowest Rf values among the group at 5.06 and 6.67 in two independent trials, respectively, which represents a five- to sixfold increase in reproduction of M. enterolobii. Plant genotypes resistant to RKNs are effective in managing the disease and preserving yield, cost-efficient, and environmentally sustainable, and host resistance is often regarded as the most robust management tactic for controlling plant-parasitic nematodes. Resistance to RKNs in soybean genotypes has been identified for other Meloidogyne species, yet there is currently limited data regarding soybean host status to the highly aggressive nematode M. enterolobii. This study adds to the knowledge of potential native resistance to M. enterolobii in wild and cultivated soybean.

Relevant
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions.

Reliable maps of species distributions are fundamental for biodiversity research and conservation. The International Union for Conservation of Nature (IUCN) range maps are widely recognized as authoritative representations of species' geographic limits, yet they might not always align with actual occurrence data. In recent area of habitat (AOH) maps, areas that are not habitat have been removed from IUCN ranges to reduce commission errors, but their concordance with actual species occurrence also remains untested. We tested concordance between occurrences recorded in camera trap surveys and predicted occurrences from the IUCN and AOH maps for 510 medium- to large-bodied mammalian species in 80 camera trap sampling areas. Across all areas, cameras detected only 39% of species expected to occur based on IUCN ranges and AOH maps; 85% of the IUCN only mismatches occurred within 200km of range edges. Only 4% of species occurrences were detected by cameras outside IUCN ranges. The probability of mismatches between cameras and the IUCN range was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya and in areas with shorter canopy forests. Our findings suggest that range and AOH maps rarely underrepresent areas where species occur, but they may more often overrepresent ranges by including areas where a species may be absent, particularly at range edges. We suggest that combining range maps with data from ground-based biodiversity sensors, such as camera traps, provides a richer knowledge base for conservation mapping and planning.

Open Access
Relevant
Interaction of Human Gut Microflora with Commonly Consumed Herbs and Spices: A Review

Abstract: Herbs and spices are used since time memorable to transfuse color and add flavors to food. Their antibacterial properties also help preserve raw and cooked foods. Various diets composed of herbs and spices, as consistent with various researches, have been shown to influence life within the human digestive tract. This modulation forms the basis of various health effects that the herbs and spices and the microflora have on the human health. The intestinal microbiota is engaged in a critical function of promoting health, composed of favourable microbes (Lactobacillus and Bifidobacterium) and potentially harmful microorganisms (Salmonella thyphimurium and Escherichia coli). Spices and herbs make double oddities, i.e., inhibiting the proliferation of hazardous microbes while promoting favorable ones. The paper reviews the relevant manuscripts published in the past 20 years to understand the microbial modulation dynamics of herbs and spices. PubMed, Mendeley, SciELO, Scopus, Science Direct, and other peer-reviewed databases were accessed for the review. Microbial modulation is achieved by means of herbs and spices owing to the reduction of oxidative stress caused by reactive oxygen radicals, such as OHˉ, singlet O2, hydrogen peroxide, and superoxide radical, which leads to a threat to the intestinal microbiota. Spices and herbs have essential oils that serve as prebiotics, reducing the demand to impart artificial antioxidants, thus avoiding the associated health risks. Thus, the present review explores the mechanisms and underlying functions of herbs and spices in the human gut biome.

Relevant