Spontaneous hypothermia on intensive care unit admission is a predictor of unfavorable neurological outcome in patients after resuscitation: an observational cohort study

Introduction: A large number of patients resuscitated for primary cardiac arrest arrive in the intensive care unit (ICU) with a body temperature = 35.0 degrees C). Neurological outcome after six months was assessed by means of the Glasgow Outcome Score (GOS), with GOS 1 to 3 defined as unfavorable and GOS 4 to 5 as favorable. A logistic regression model was used to analyze the influence of the different parameters on neurological outcome. Results: The data of 105 consecutive patients resuscitated for primary cardiac arrest and treated with induced mild hypothermia were analyzed. Median ICU admission temperature was 35.1 degrees C (interquartile range (IQR) 34.3 to 35.7). After six months, 61% of the patients had an unfavorable outcome (59% died and 2% were severely disabled), whereas 39% had a favorable outcome (moderate disability or good recovery). Among patients with spontaneous hypothermia on ICU admission, the percentage with unfavorable outcome was higher (69% versus 50%, P = 0.05). Logistic regression showed that age, acute physiology and chronic health evaluation (APACHE) II and sequential organ failure assessment (SOFA) scores and spontaneous hypothermia on ICU admission all had an increased odds ratio (OR) for an unfavorable outcome after six months. Spontaneous hypothermia had the strongest association with unfavorable outcome (OR 2.6, 95% CI (confidence interval) 1.1 to 5.9), which became even stronger after adjustment for age, presenting heart rhythm, APACHE II and SOFA scores (OR 3.8, CI 1.3 to 11.0). Conclusions: In this observational cohort study, spontaneous hypothermia on ICU admission was the strongest predictor of an unfavorable neurological outcome in patients resuscitated for primary cardiac arres

A Vulnerability Assessment of Fish and Invertebrates to Climate Change on the Northeast U.S. Continental Shelf

Climate change and decadal variability are impacting marine fish and invertebrate species worldwide and these impacts will continue for the foreseeable future. Quantitative approaches have been developed to examine climate impacts on productivity, abundance, and distribution of various marine fish and invertebrate species. However, it is difficult to apply these approaches to large numbers of species owing to the lack of mechanistic understanding sufficient for quantitative analyses, as well as the lack of scientific infrastructure to support these more detailed studies. Vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species with existing information. These methods combine the exposure of a species to a stressor (climate change and decadal variability) and the sensitivity of species to the stressor. These two components are then combined to estimate an overall vulnerability. Quantitative data are used when available, but qualitative information and expert opinion are used when quantitative data is lacking. Here we conduct a climate vulnerability assessment on 82 fish and invertebrate species in the Northeast U.S. Shelf including exploited, forage, and protected species. We define climate vulnerability as the extent to which abundance or productivity of a species in the region could be impacted by climate change and decadal variability. We find that the overall climate vulnerability is high to very high for approximately half the species assessed; diadromous and benthic invertebrate species exhibit the greatest vulnerability. In addition, the majority of species included in the assessment have a high potential for a change in distribution in response to projected changes in climate. Negative effects of climate change are expected for approximately half of the species assessed, but some species are expected to be positively affected (e.g., increase in productivity or move into the region). These results will inform research and management activities related to understanding and adapting marine fisheries management and conservation to climate change and decadal variability

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

Application of a deep learning image classifier for identification of Amazonian fishes

Abstract Given the sharp increase in agricultural and infrastructure development and the paucity of widespread data available to support conservation management decisions, a more rapid and accurate tool for identifying fish fauna in the world's largest freshwater ecosystem, the Amazon, is needed. Current strategies for identification of freshwater fishes require high levels of training and taxonomic expertise for morphological identification or genetic testing for species recognition at a molecular level. To overcome these challenges, we built an image masking model (U‐Net) and a convolutional neural net (CNN) to classify Amazonian fish in photographs. Fish used to generate training data were collected and photographed in tributaries in seasonally flooded forests of the upper Morona River valley in Loreto, Peru in 2018 and 2019. Species identifications in the training images (n = 3068) were verified by expert ichthyologists. These images were supplemented with photographs taken of additional Amazonian fish specimens housed in the ichthyological collection of the Smithsonian's National Museum of Natural History. We generated a CNN model that identified 33 genera of fishes with a mean accuracy of 97.9%. Wider availability of accurate freshwater fish image recognition tools, such as the one described here, will enable fishermen, local communities, and citizen scientists to more effectively participate in collecting and sharing data from their territories to inform policy and management decisions that impact them directly