Complete Genome Sequences of Paenibacillus Larvae Phages BN12, Dragolir, Kiel007, Leyra, Likha, Pagassa, PBL1c, and Tadhana

We present here the complete genomes of eight phages that infect Paenibacillus larvae, the causative agent of American foulbrood in honeybees. Phage PBL1c was originally isolated in 1984 from a P. larvae lysogen, while the remaining phages were isolated in 2014 from bee debris, honeycomb, and lysogens from three states in the USA

The impact of language co-activation on L1 and L2 speech fluency

Fluent speech depends on the availability of well-established linguistic knowledge and routines for speech planning and articulation. A lack of speech fluency in late second-language (L2) learners may point to a deficiency of these representations, due to incomplete acquisition. Experiments on bilingual language processing have shown, however, that there are strong reasons to believe that multilingual speakers experience co-activation of the languages they speak. We have studied to what degree language co-activation affects fluency in the speech of bilinguals, comparing a monolingual German control group with two bilingual groups: 1) first-language (L1) attriters, who have fully acquired German before emigrating to an L2 English environment, and 2) immersed L2 learners of German (L1: English). We have analysed the temporal fluency and the incidence of disfluency markers (pauses, repetitions and self-corrections) in spontaneous film retellings. Our findings show that learners to speak more slowly than controls and attriters. Also, on each count, the speech of at least one of the bilingual groups contains more disfluency markers than the retellings of the control group. Generally speaking, both bilingual groups-learners and attriters-are equally (dis)fluent and significantly more disfluent than the monolingual speakers. Given that the L1 attriters are unaffected by incomplete acquisition, we interpret these findings as evidence for language competition during speech production

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

An OER COUP: College Teacher and Student Perceptions of Open Educational Resources

Abstract: Despite increased development and dissemination, there has been very little empirical research on Open Educational Resources (OER). Teachers and students involved in a large-scale OER initiative at eight community colleges across the United States were given a detailed questionnaire aimed at uncovering their perceptions of the cost, outcomes, uses and perceptions of quality of the OER used in their courses. Teachers and students alike reported significant cost savings and various pedagogical and learning impacts due to the implementation of OER in the classroom. In addition, most students and teachers perceived their OER to be at least equal in quality to traditional textbooks they had used in the past. Implications for further research are discussed

Biophysical differentiation of susceptibility and chemical differences in Staphylococcus aureus

Differentiating bacteria strains using biophysical forces has been the focus of recent studies using dielectrophoresis (DEP). The refinement of these studies has created high-resolution separations such that very subtle properties of the cells are enough to induce significant differences in measurable biophysical properties. These high-resolution capabilities build upon the advantages of DEP which include small sample sizes and fast analysis times. Studies focusing on differentiating antimicrobial resistant and susceptible bacteria potentially have significant impact on human health and medical care. A prime example is Staphylococcus aureus, which commonly colonizes adults without ill effects. However, the pathogen is an important cause of infections, including surgical site infections. Treatment of S. aureus infections is generally possible with antimicrobials, but antimicrobial resistance has emerged. Of special importance is resistance to methicillin, an antimicrobial created in response to resistance to penicillin. Here, dielectrophoresis is used to study methicillin-resistant (MRSA) and-susceptible S. aureus (MSSA) strains, both with and without the addition of a fluorescent label. The capture onset potential of fluorescently-labeled MRSA (865 +/- 71 V) and thus the ratio of electrokinetic to dielectrophoretic mobility, was found to be higher than that of fluorescently-labeled MSSA (685 +/- 61 V). This may be attributable to the PBP2a enzyme present in the MRSA strain and not in the MSSA bacteria. Further, unlabeled MRSA was found to have a capture onset potential of 732 +/- 44 V, while unlabeled MSSA was found to have a capture onset potential of 562 +/- 59 V. This shows that the fluorescently-labeled bacteria require a higher applied potential, and thus ratio of mobilities, to capture than the unlabeled bacteria.12 month embargo; published online: 19 February 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Bystander Phage Therapy: Inducing Host-Associated Bacteria to Produce Antimicrobial Toxins against the Pathogen Using Phages

Brevibacillus laterosporus is often present in beehives, including presence in hives infected with the causative agent of American Foulbrood (AFB), Paenibacillus larvae. In this work, 12 B. laterosporus bacteriophages induced bactericidal products in their host. Results demonstrate that P. larvae is susceptible to antimicrobials induced from field isolates of the bystander, B. laterosporus. Bystander antimicrobial activity was specific against the pathogen and not other bacterial species, indicating that the production was likely due to natural competition between the two bacteria. Three B. laterosporus phages were combined in a cocktail to treat AFB. Healthy hives treated with B. laterosporus phages experienced no difference in brood generation compared to control hives over 8 weeks. Phage presence in bee larvae after treatment rose to 60.8 ± 3.6% and dropped to 0 ± 0.8% after 72 h. In infected hives the recovery rate was 75% when treated, however AFB spores were not susceptible to the antimicrobials as evidenced by recurrence of AFB. We posit that the effectiveness of this treatment is due to the production of the bactericidal products of B. laterosporus when infected with phages resulting in bystander-killing of P. larvae. Bystander phage therapy may provide a new avenue for antibacterial production and treatment of disease