Exigência de energia metabolizável e lisina digestível para codornas japonesas (Coturnix coturnix japonica) em crescimento

Este trabalho objetivou estimar as exigências nutricionais de energia metabolizável (EM) e lisina digestível (LD) para codornas Japonesas nas fases de cria (1 a 14 dias) e recria (15 a 42 dias) e verificar suas implicações na composição química corporal, peso relativo dos órgãos, parâmetros sanguíneos e ósseos. O delineamento adotado foi o inteiramente casualizado em esquema fatorial 4 x 4 (EM = 2.830, 2.970, 3.110 e 3.250 kcal x LD = 0,90; 1,07; 1,24 e 1,41%), perfazendo 16 tratamentos com 3 repetições cada, contendo 50 codornas por unidade experimental na fase de cria (totalizando 2.400 aves) e 35 codornas por unidade experimental na fase de recria (totalizando 1.680 aves). Não foi verificada interação entre os fatores sobre o desempenho de codornas Japonesas na fase de cria, sendo que as variáveis peso médio, ganho de peso, consumo de ração e conversão alimentar apresentaram efeito quadrático tanto para EM quanto para LD. Nesta fase, o extrato etéreo da carcaça e o índice de Seedor do fêmur e da tíbia exibiram interação significativa, e o peso relativo do fígado apresentou efeito linear da LD. Na fase de recria houve interação dos fatores para ganho de peso e consumo de ração. A conversão alimentar apresentou efeito quadrático de ambos os fatores e o peso médio foi influenciado de modo quadrático pela EM, que também influenciou de modo quadrático o índice de Seedor nos dois ossos e a densidade óssea do fêmur. Considerando as estimativas obtidas por meio dos gráficos de contornos sobrepostos, os níveis de 3.030 kcal de EM e 1,221% de LD foram estimados para a fase de cria e os níveis de 3.055 kcal de EM e 1,202% de LD foram estimados para a fase de recria

How future surgery will benefit from SARS-COV-2-related measures: a SPIGC survey conveying the perspective of Italian surgeons

COVID-19 negatively affected surgical activity, but the potential benefits resulting from adopted measures remain unclear. The aim of this study was to evaluate the change in surgical activity and potential benefit from COVID-19 measures in perspective of Italian surgeons on behalf of SPIGC. A nationwide online survey on surgical practice before, during, and after COVID-19 pandemic was conducted in March-April 2022 (NCT:05323851). Effects of COVID-19 hospital-related measures on surgical patients' management and personal professional development across surgical specialties were explored. Data on demographics, pre-operative/peri-operative/post-operative management, and professional development were collected. Outcomes were matched with the corresponding volume. Four hundred and seventy-three respondents were included in final analysis across 14 surgical specialties. Since SARS-CoV-2 pandemic, application of telematic consultations (4.1% vs. 21.6%; p < 0.0001) and diagnostic evaluations (16.4% vs. 42.2%; p < 0.0001) increased. Elective surgical activities significantly reduced and surgeons opted more frequently for conservative management with a possible indication for elective (26.3% vs. 35.7%; p < 0.0001) or urgent (20.4% vs. 38.5%; p < 0.0001) surgery. All new COVID-related measures are perceived to be maintained in the future. Surgeons' personal education online increased from 12.6% (pre-COVID) to 86.6% (post-COVID; p < 0.0001). Online educational activities are considered a beneficial effect from COVID pandemic (56.4%). COVID-19 had a great impact on surgical specialties, with significant reduction of operation volume. However, some forced changes turned out to be benefits. Isolation measures pushed the use of telemedicine and telemetric devices for outpatient practice and favored communication for educational purposes and surgeon-patient/family communication. From the Italian surgeons' perspective, COVID-related measures will continue to influence future surgical clinical practice

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

PhDAY 2020 -FOO (Facultad de Óptica y Optometría)

Por cuarto año consecutivo los doctorandos de la Facultad de Óptica y Optometría de la Universidad Complutense de Madrid cuentan con un congreso propio organizado por y para ellos, el 4º PhDAY- FOO. Se trata de un congreso gratuito abierto en la que estos jóvenes científicos podrán presentar sus investigaciones al resto de sus compañeros predoctorales y a toda la comunidad universitaria que quiera disfrutar de este evento. Apunta en tu agenda: el 15 de octubre de 2020. En esta ocasión será un Congreso On-line para evitar que la incertidumbre asociada a la pandemia Covid-19 pudiera condicionar su celebración

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