High-resolution 3D mapping of cold-water coral reefs using machine learning

Structure-from-Motion (SfM) photogrammetry is a time and cost-effective method for high-resolution 3D mapping of cold-water corals (CWC) reefs and deep-water environments. The accurate classification and analysis of marine habitats in 3D provide valuable information for the development of management strategies for large areas at various spatial and temporal scales. Given the amount of data derived from SfM data sources such as Remotely-Operated Vehicles (ROV), there is an increasing need to advance towards automatic and semiautomatic classification approaches. However, the lack of training data, benchmark datasets for CWC environments and processing resources are a bottleneck for the development of classification frameworks. In this study, machine learning (ML) methods and SfM-derived 3D data were combined to develop a novel multiclass classification workflow for CWC reefs in deep-water environments. The Piddington Mound area, southwest of Ireland, was selected for 3D reconstruction from high-definition video data acquired with an ROV. Six ML algorithms, namely: Support Vector Machines, Random Forests, Gradient Boosting Trees, k-Nearest Neighbours, Logistic Regression and Multilayer Perceptron, were trained in two datasets of different sizes (1,000 samples and 10,000 samples) in order to evaluate accuracy variation between approaches in relation to the number of samples. The Piddington Mound was classified into four classes: live coral framework, dead coral framework, coral rubble and sediment and dropstones. Parameter optimisation was performed with grid search and cross-validation. Run times were measured to evaluate the trade-off between processing time and accuracy. In total, eighteen variations of ML algorithms were created and tested. The results show that four algorithms yielded f1-scores >90% and were able to discern between the four classes, especially those with usually similar characteristics, e.g., coral rubble and dead coral. The accuracy variation among them was 3.6% which suggests that they can be used interchangeably depending on the classification task. Furthermore, results on sample size variations show that certain algorithms benefit more from larger datasets whilst others showed discrete accuracy variations (<5%) when trained in datasets of different sizes

Estimulação cerebral profunda na Doença de Parkinson: evidências de estudos de longa duração

A Doença de Parkinson (DP) é uma condição neurodegenerativa crônica que afeta principalmente idosos, mas pode ocorrer em adultos jovens. É a segunda doença neurodegenerativa mais comum, após o Alzheimer. A DP afeta 1% dos indivíduos acima de 60 anos em países industrializados. Sua causa envolve fatores genéticos e ambientais, como exposição a pesticidas e envelhecimento. A Estimulação Cerebral Profunda (DBS) é um tratamento que simula lesões cerebrais, melhorando sintomas motores e não motores. O presente estudo tem como objetivo analisar evidências de estudos sobre a eficácia da DBS no tratamento da DP. Trata-se de uma revisão sistemática de estudos quantitativos que utiliza as bases de dados PubMed (Medline), Cochrane Library e Scientific Electronic Library Online (SciELO) para selecionar artigos científicos. Os estudos incluídos abrangem o período de 2013 a 2023 e estão em inglês, abordando a DBS no tratamento da DP. A DBS melhora diversos sintomas motores e não motores, resultando em uma melhor qualidade de vida para os pacientes. Tais benefícios são sustentados mesmo em estágios avançados da Doença de Parkinson, a qual consiste em fornecer pulsos de corrente elétrica a áreas cerebrais profundas através de eletrodos implantados cirurgicamente, geralmente quando a terapia medicamentosa já não é eficaz. Em um estudo com 82 pacientes, a terapia com DBS resultou em uma redução de ± 52% nos sintomas motores do UPDRS sob medicação antes da cirurgia. A melhora nos sintomas motores com a estimulação, em comparação com a ausência de estimulação e medicação, foi de ± 61% no primeiro ano e ± 39% de 8 a 15 anos após a cirurgia (antes da reprogramação). A medicação foi reduzida em ± 55% após 1 ano e ± 44% após 8 a 15 anos, com a maioria dos pacientes mostrando melhorias após a reprogramação. De acordo com as literaturas analisadas, a DBS é uma terapia eficaz para a DP. Enfatiza-se a importância da inovação contínua e dos novos estudos para explorar as facetas não investigadas desse campo. Com a abordagem dos aspectos clínicos, cirúrgicos, tecnológicos e científicos, destacam-se os benefícios, limitações e desafios a serem superados. Ademais, inovações tecnológicas na DBS, como a estimulação direcional, adaptativa e a telemedicina estão sendo exploradas. Em suma, este artigo fornece evidências sobre os benefícios da DBS na DP, ressaltando a necessidade de pesquisas adicionais para otimizar tal intervenção terapêutica e melhorar a qualidade de vida dos pacientes

Neuroproteção na ressecção cirúrgica de gliomas cerebrais: revisão da evidência atual

Os gliomas cerebrais são tumores primários do sistema nervoso central que se desenvolvem a partir de células gliais e têm alta morbimortalidade. Seu tratamento padrão envolve a ressecção cirúrgica, radioterapia e quimioterapia, os quais possivelmente podem levar os pacientes a um prognóstico desfavorável. Nesse contexto, a neuroproteção entra como uma aliada para minimizar os efeitos colaterais da ressecção cirúrgica e melhorar a sobrevida e a qualidade de vida dos pacientes. Nesse sentido, o presente estudo tem como objetivo discutir sobre a evidência atual da neuroproteção na ressecção cirúrgica de gliomas cerebrais. Para isso, foram selecionados quatro artigos que que abordavam sobre a evidência atual da neuroproteção na ressecção cirúrgica de gliomas cerebrais, por meio de uma estratégia de busca com recorte temporal entre 2014 e 2023, nas bases de dados PubMed (Medline), Embase e Cochrane Library. Os resultados indicam que o grupo de pacientes que recebeu dexmedetomidina apresentou melhora significativa na cognição e redução da inflamação cerebral em comparação com o grupo-controle pós-ressecção dos gliomas cerebrais, além de menor incidência de efeitos colaterais anestésicos, como náusea e vômitos (p < 0,05). Ademais, foi observado que a modulação da via metabólica do glutamato/glutamina pode inibir o crescimento de gliomas e proteger o parênquima cerebral. Nesse sentido, as evidências atuais indicam que proteger as células nervosas é uma estratégia importante para minimizar os efeitos colaterais da ressecção cirúrgica de gliomas cerebrais, e a dexmedetomidina e a co-cultura de células de glioma e astrócitos que aumenta a concentração extracelular de glutamato e glutamina parecem ser importantes aliadas nessa profilaxia

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Developing Mobile Applications with Augmented Reality and 3D Photogrammetry for Visualisation of Cold-Water Coral Reefs and Deep-Water Habitats

Cold-water coral (CWC) reefs are considered “hotspots” of biodiversity in deep-sea environments. Like tropical coral reefs, these habitats are subject to climate and anthropogenic threats. The use of remotely operated vehicles (ROVSs) in combination with three-dimensional (3D) modelling and augmented reality (AR) has enabled detailed visualisation of terrestrial and marine environments while promoting data accessibility and scientific outreach. However, remote environments such as CWC reefs still present challenges with data acquisition, which impacts the further understanding of these environments. This study aims to develop a mobile application using structure-from-motion (SfM) 3D photogrammetric data and AR for the visualisation of CWC reefs. The mobile application was developed to display 3D models of CWC reefs from the Piddington Mound area, southwest of Ireland. The 3D models were tested at different resolutions to analyse the visualisation experience and trade-off between resolution and application size. The results from the 3D reconstructions with higher resolution indicate that the combination of SfM, AR, and mobile phones is a promising tool for raising awareness and literacy regarding CWC and deep-water habitats. This study is the first of its kind to showcase CWC habitats accessible to anyone, anywhere with a mobile phone and internet connectivity

Developing Mobile Applications with Augmented Reality and 3D Photogrammetry for Visualisation of Cold-Water Coral Reefs and Deep-Water Habitats

Cold-water coral (CWC) reefs are considered “hotspots” of biodiversity in deep-sea environments. Like tropical coral reefs, these habitats are subject to climate and anthropogenic threats. The use of remotely operated vehicles (ROVSs) in combination with three-dimensional (3D) modelling and augmented reality (AR) has enabled detailed visualisation of terrestrial and marine environments while promoting data accessibility and scientific outreach. However, remote environments such as CWC reefs still present challenges with data acquisition, which impacts the further understanding of these environments. This study aims to develop a mobile application using structure-from-motion (SfM) 3D photogrammetric data and AR for the visualisation of CWC reefs. The mobile application was developed to display 3D models of CWC reefs from the Piddington Mound area, southwest of Ireland. The 3D models were tested at different resolutions to analyse the visualisation experience and trade-off between resolution and application size. The results from the 3D reconstructions with higher resolution indicate that the combination of SfM, AR, and mobile phones is a promising tool for raising awareness and literacy regarding CWC and deep-water habitats. This study is the first of its kind to showcase CWC habitats accessible to anyone, anywhere with a mobile phone and internet connectivity