Development of Plasmid Vectors for Regulated Expression of Cloned Genes in Streptomyces lividans and Escherichia coli

Using pIJ486 (Ward et al., 1986) two bifunctional (E. coli/Streptomvces) expression vectors were constructed. They contained the promoters lac (pGLW49) and tac (pGLW50) just upstream of a promoter-less kanamycin phosphotransferase gene (aphll)

Immunogenicity and Reactogenicity of 2009 Influenza A (H1N1) Inactivated Monovalent Non-Adjuvanted Vaccine in Elderly and Immunocompromised Patients

Background\ud \ud Immunosuppressed individuals present serious morbidity and mortality from influenza, therefore it is important to understand the safety and immunogenicity of influenza vaccination among them.\ud Methods\ud \ud This multicenter cohort study evaluated the immunogenicity and reactogenicity of an inactivated, monovalent, non-adjuvanted pandemic (H1N1) 2009 vaccine among the elderly, HIV-infected, rheumatoid arthritis (RA), cancer, kidney transplant, and juvenile idiopathic arthritis (JIA) patients. Participants were included during routine clinical visits, and vaccinated according to conventional influenza vaccination schedules. Antibody response was measured by the hemagglutination-inhibition assay, before and 21 days after vaccination.\ud Results\ud \ud 319 patients with cancer, 260 with RA, 256 HIV-infected, 149 elderly individuals, 85 kidney transplant recipients, and 83 with JIA were included.\ud \ud The proportions of seroprotection, seroconversion, and the geometric mean titer ratios postvaccination were, respectively: 37.6%, 31.8%, and 3.2 among kidney transplant recipients, 61.5%, 53.1%, and 7.5 among RA patients, 63.1%, 55.7%, and 5.7 among the elderly, 59.0%, 54.7%, and 5.9 among HIV-infected patients, 52.4%, 49.2%, and 5.3 among cancer patients, 85.5%, 78.3%, and 16.5 among JIA patients. The vaccine was well tolerated, with no reported severe adverse events.\ud Conclusions\ud \ud The vaccine was safe among all groups, with an acceptable immunogenicity among the elderly and JIA patients, however new vaccination strategies should be explored to improve the immune response of immunocompromised adult patients. (ClinicalTrials.gov, NCT01218685)Fundação Butantan funded the study, and employed several of the authors. The funder had a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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

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

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016