Identification of the nuclear factor kappa-beta (NF-kB) in cortical of mice Wistar using Technovit 7200 VCR

Objective: this study aimed to develop a nondecalcified bone sample processing technique enabling immunohistochemical labeling of proteins by kappa-beta nuclear factor (NF-kB) utilizing the Technovit 7200 VCR® in adult male Wistar rats. Study Method: A 1.8 mm diameter defect was performed 0.5mm from the femur proximal joint by means of a round bur. Experimental groups were divided according to fixing solution prior to histologic processing: Group 1- ethanol 70%; Group 2-10% buffered formalin; and Group 3- Glycerol diluted in 70% ethanol at a 70/30 ratio + 10% buffered formalin. The post-surgical periods ranged from 01 to 24 hours. Control groups included a nonsurgical procedure group (NSPG) and surgical procedures where bone exposure was performed (SPBE) without drilling. Prostate carcinoma was the positive control (PC) and samples subjected to incomplete immunohistochemistry protocol were the negative control (NC). Following euthanization, all samples were kept at 4o C for 7 days, and were dehydrated in a series of alcohols at -20o C. The polymer embedding procedure was performed at ethanol/polymer ratios of 70%-30%, 50%-50%, 30%-70%, 100%, and 100% for 72 hours at -20o C. Polymerization followed the manufacturer?s recommendation. The samples were grounded and polished to 10-15?m thickness, and were deacrylated. The sections were rehydrated and were submitted to the primary polyclonal antibody antiNF-kB on a 1:75 dilution for 12 hours at room temperature. Results: Microscopy showed that the Group 2 presented positive reaction to NF-kB, diffuse reactions for NSPG and SPBE, and no reaction for the NC group. Conclusion: The results obtained support the feasibility of the developed immunohistochemistry technique

Ant genera identification using an ensemble of convolutional neural networks

Works requiring taxonomic knowledge face several challenges, such as arduous identification of many taxa and an insufficient number of taxonomists to identify a great deal of collected organisms. Machine learning tools, particularly convolutional neural networks (CNNs), are then welcome to automatically generate high-performance classifiers from available data. Supported by the image datasets available at the largest online database on ant biology, the AntWeb (www.antweb.org), we propose here an ensemble of CNNs to identify ant genera directly from the head, profile and dorsal perspectives of ant images. Transfer learning is also considered to improve the individual performance of the CNN classifiers. The performance achieved by the classifiers is diverse enough to promote a reduction in the overall classification error when they are combined in an ensemble, achieving an accuracy rate of over 80% on top-1 classification and an accuracy of over 90% on top-3 classification131CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP141308/2014-1; 131488/2015-5; 311751/2013-0; 309115/2014-023038.002884/2013-382014/13533-

Guidelines for the management of neuroendocrine tumours by the Brazilian gastrointestinal tumour group

Neuroendocrine tumours are a heterogeneous group of diseases with a significant variety of diagnostic tests and treatment modalities. Guidelines were developed by North American and European groups to recommend their best management. However, local particularities and relativisms found worldwide led us to create Brazilian guidelines. Our consensus considered the best feasible strategies in an environment involving more limited resources. We believe that our recommendations may be extended to other countries with similar economic standards.Univ Sao Paulo, Inst Canc Estado Sao Paulo, BR-01246000 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Radiol & Oncol, BR-01246903 Sao Paulo, BrazilHosp Sirio Libanes, BR-01308050 Sao Paulo, BrazilHosp Moinhos de Vento Porto Alegre, BR-90035000 Porto Alegre, RS, BrazilOncoctr, BR-30360680 Belo Horizonte, MG, BrazilUniv Fed Rio Grande do Sul, Dept Cirurgia, BR-90040060 Porto Alegre, RS, BrazilHosp Clin Porto Alegre, BR-90035903 Porto Alegre, RS, BrazilUniv Fed Ceara, Fac Med, Dept Fisiol & Farmacol, BR-60020180 Fortaleza, Ceara, BrazilHosp Univ Walter Cantidio, BR-60430370 Fortaleza, Ceara, BrazilInst Nacl Canc, BR-20230240 Rio De Janeiro, BrazilUniv Sao Paulo, Fac Med, Disciplina Endocrinol & Metabol, BR-01246903 Sao Paulo, BrazilAC Camargo Canc Ctr, Dept Surg, BR-01509010 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Gastroenterol, Sao Paulo, BrazilUniv Fed Ciencias Saude Porto Alegre, BR-90050170 Porto Alegre, RS, BrazilHosp Albert Einstein, BR-05652900 Sao Paulo, BrazilHosp Base, Fac Med Sao Jose do Rio Preto, BR-15090000 Sao Paulo, BrazilSanta Casa Sao Jose do Rio Preto, BR-15025500 Sao Jose Do Rio Preto, BrazilPontificia Univ Catolica Parana, Hosp Erasto Gaertner, BR-81520060 Curitiba, Parana, BrazilUniv Fed Rio Grande do Norte, BR-59300000 Natal, RN, BrazilUniv Sao Paulo, Inst Coracao, BR-05403900 Sao Paulo, BrazilAC Camargo Canc Ctr, Med Oncol, BR-01509010 Sao Paulo, BrazilUniv Fed Sao Paulo, Disciplina Gastroenterol, BR-04021001 Sao Paulo, BrazilHosp Sao Rafael, BR-41253190 Salvador, BA, BrazilHosp Canc Barretos, Dept Cirurgia Aparelho Digest Alto & Hepatobiliop, BR-14784400 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Patol, BR-01246903 Sao Paulo, BrazilClin AMO, BR-1950640 Salvador, BA, BrazilHosp Sao Jose, BR-01323001 Sao Paulo, BrazilUniv Nove de Julho, BR-02111030 Sao Paulo, BrazilUniv Fed Sao Paulo, Disciplina Gastroenterol, BR-04021001 Sao Paulo, BrazilWeb of Scienc

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

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