Joint keypoint detection and description network for color fundus image registration

[Absctract]: Background: Retinal imaging is widely used to diagnose many diseases, both systemic and eye-specific. In these cases, image registration, which is the process of aligning images taken from different viewpoints or moments in time, is fundamental to compare different images and to assess changes in their appearance, commonly caused by disease progression. Currently, the field of color fundus registration is dominated by classical methods, as deep learning alternatives have not shown sufficient improvement over classic methods to justify the added computational cost. However, deep learning registration methods are still considered beneficial as they can be easily adapted to different modalities and devices following a data-driven learning approach. Methods: In this work, we propose a novel methodology to register color fundus images using deep learning for the joint detection and description of keypoints. In particular, we use an unsupervised neural network trained to obtain repeatable keypoints and reliable descriptors. These keypoints and descriptors allow to produce an accurate registration using RANdom SAmple Consensus (RANSAC). We train the method using the Messidor dataset and test it with the Fundus Image Registration Dataset (FIRE) dataset, both of which are publicly accessible. Results: Our work demonstrates a color fundus registration method that is robust to changes in imaging devices and capture conditions. Moreover, we conduct multiple experiments exploring several of the method’s parameters to assess their impact on the registration performance. The method obtained an overall Registration Score of 0.695 for the whole FIRE dataset (0.925 for category S, 0.352 for P, and 0.726 for A). Conclusions: Our proposal improves the results of previous deep learning methods in every category and surpasses the performance of classical approaches in category A which has disease progression and thus represents the most relevant scenario for clinical practice as registration is commonly used in patients with diseases for disease monitoring purposes.This work was supported by Ministerio de Ciencia e Innovación, Government of Spain, through the RTI2018-095894-B-I00, PID2019-108435RB-I00, TED2021-131201B-I00, and PDC2022-133132-I00 research projects; Consellería de Cultura, Educación e Universidade Xunta de Galicia through the Grupos de Referencia Competitiva grant (Ref. ED431C 2020/24), the predoctoral fellowship (Ref. ED481A 2021/147) and the postdoctoral fellowship (Ref. ED481B-2022-025); CITIC, Centro de Investigación de Galicia (Ref. ED431G 2019/01), itself received financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%).Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED481A 2021/147Xunta de Galicia; ED481B-2022-025Xunta de Galicia; ED431G 2019/0

Color Fundus Image Registration Using a Learning-Based Domain-Specific Landmark Detection Methodology

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Medical imaging, and particularly retinal imaging, allows to accurately diagnose many eye pathologies as well as some systemic diseases such as hypertension or diabetes. Registering these images is crucial to correctly compare key structures, not only within patients, but also to contrast data with a model or among a population. Currently, this field is dominated by complex classical methods because the novel deep learning methods cannot compete yet in terms of results and commonly used methods are difficult to adapt to the retinal domain. In this work, we propose a novel method to register color fundus images based on previous works which employed classical approaches to detect domain-specific landmarks. Instead, we propose to use deep learning methods for the detection of these highly-specific domain-related landmarks. Our method uses a neural network to detect the bifurcations and crossovers of the retinal blood vessels, whose arrangement and location are unique to each eye and person. This proposal is the first deep learning feature-based registration method in fundus imaging. These keypoints are matched using a method based on RANSAC (Random Sample Consensus) without the requirement to calculate complex descriptors. Our method was tested using the public FIRE dataset, although the landmark detection network was trained using the DRIVE dataset. Our method provides accurate results, a registration score of 0.657 for the whole FIRE dataset (0.908 for category S, 0.293 for category P and 0.660 for category A). Therefore, our proposal can compete with complex classical methods and beat the deep learning methods in the state of the art.This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00 136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095 894-B-I00 research project; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the predoctoral grant contract ref. ED481A 2021/147 and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). The funding institutions had no involvement in the study design, in the collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED481A 2021/147Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED431G 2019/0

ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high-quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is supported by Ministerio de Ciencia e Innovación, Government of Spain, through the PID2019-108435RB-I00, TED2021-131201B-I00, and PDC2022-133132-I00 research projects; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, through Grupos de Referencia Competitiva ref. ED431C 2020/24, predoctoral fellowship ref. ED481A 2021/147 and the postdoctoral fellowship ref. ED481B-2022-025; and Instituto de Salud Carlos III (ISCIII) under the grant FORT23/00010 as part of the Programa FORTALECE of Ministerio de Ciencia e Innovación. Funding for open access charge: Universidade da Coruña/CISUG.Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED481A 2021/147Xunta de Galicia; ED481B-2022-02

Empyema associated with Lemierre syndrome: case report and literature review

Postanginal septicemia, also called Lemierre syndrome, is a metastatic infection that can have multiple complications, including empyema. Therefore, the natural history of the disease begins with an infection of the oropharynx by microbiota from the digestive system, which causes a thrombophlebitis of the jugular vein with septic infiltrations, including into the lungs causing pneumonia, which in turn can generate parapneumonic effusions and/or empyemas. Furthermore, it is a syndrome that was thought to have been forgotten by the era of antibiotics, but with resistance to these antibiotics it has begun to re-emerge. Next, we will talk about a case of a 41-year-old man with no significant pathological history, who entered secondary to a peritonsillar abscess which turned into Lemierre syndrome with a treatment based on broad-spectrum antibiotics and then performed of lung decortication by thoracotomy. Empyema as a complication of Lemierre syndrome is rare and even more so in this post-antibiotic era, so health personnel should have a high clinical suspicion since adequate and timely treatment will help reduce the complications of this disease, as well as like his mortality

Haptoglobin Phenotype, Preeclampsia Risk and the Efficacy of Vitamin C and E Supplementation to Prevent Preeclampsia in a Racially Diverse Population

Haptoglobin's (Hp) antioxidant and pro-angiogenic properties differ between the 1-1, 2-1, and 2-2 phenotypes. Hp phenotype affects cardiovascular disease risk and treatment response to antioxidant vitamins in some non-pregnant populations. We previously demonstrated that preeclampsia risk was doubled in white Hp 2-1 women, compared to Hp 1-1 women. Our objectives were to determine whether we could reproduce this finding in a larger cohort, and to determine whether Hp phenotype influences lack of efficacy of antioxidant vitamins in preventing preeclampsia and serious complications of pregnancy-associated hypertension (PAH). This is a secondary analysis of a randomized controlled trial in which 10,154 low-risk women received daily vitamin C and E, or placebo, from 9-16 weeks gestation until delivery. Hp phenotype was determined in the study prediction cohort (n = 2,393) and a case-control cohort (703 cases, 1,406 controls). The primary outcome was severe PAH, or mild or severe PAH with elevated liver enzymes, elevated serum creatinine, thrombocytopenia, eclampsia, fetal growth restriction, medically indicated preterm birth or perinatal death. Preeclampsia was a secondary outcome. Odds ratios were estimated by logistic regression. Sampling weights were used to reduce bias from an overrepresentation of women with preeclampsia or the primary outcome. There was no relationship between Hp phenotype and the primary outcome or preeclampsia in Hispanic, white/other or black women. Vitamin supplementation did not reduce the risk of the primary outcome or preeclampsia in women of any phenotype. Supplementation increased preeclampsia risk (odds ratio 3.30; 95% confidence interval 1.61-6.82, p<0.01) in Hispanic Hp 2-2 women. Hp phenotype does not influence preeclampsia risk, or identify a subset of women who may benefit from vitamin C and E supplementation to prevent preeclampsia

Atlantic mammal traits: a dataset of morphological traits of mammals in the atlantic forest of south America

Measures of traits are the basis of functional biological diversity. Numerous works consider mean species-level measures of traits while ignoring individual variance within species. However, there is a large amount of variation within species and it is increasingly apparent that it is important to consider trait variation not only between species, but also within species. Mammals are an interesting group for investigating trait-based approaches because they play diverse and important ecological functions (e.g., pollination, seed dispersal, predation, grazing) that are correlated with functional traits. Here we compile a data set comprising morphological and life history information of 279 mammal species from 39,850 individuals of 388 populations ranging from −5.83 to −29.75 decimal degrees of latitude and −34.82 to −56.73 decimal degrees of longitude in the Atlantic forest of South America. We present trait information from 16,840 individuals of 181 species of non-volant mammals (Rodentia, Didelphimorphia, Carnivora, Primates, Cingulata, Artiodactyla, Pilosa, Lagomorpha, Perissodactyla) and from 23,010 individuals of 98 species of volant mammals (Chiroptera). The traits reported include body mass, age, sex, reproductive stage, as well as the geographic coordinates of sampling for all taxa. Moreover, we gathered information on forearm length for bats and body length and tail length for rodents and marsupials. No copyright restrictions are associated with the use of this data set. Please cite this data paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data.Fil: Gonçalves, Fernando. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Bovendorp, Ricardo S.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Beca, Gabrielle. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Bello, Carolina. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Costa Pereira, Raul. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Muylaert, Renata L.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Rodarte, Raisa R.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Villar, Nacho. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Souza, Rafael. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Graipel, Maurício E.. Universidade Federal de Santa Catarina; BrasilFil: Cherem, Jorge J.. Caipora Cooperativa, Florianopolis; BrasilFil: Faria, Deborah. Universidade Estadual de Santa Cruz; BrasilFil: Baumgarten, Julio. Universidade Estadual de Santa Cruz; BrasilFil: Alvarez, Martín R.. Universidade Estadual de Santa Cruz; BrasilFil: Vieira, Emerson M.. Universidade do Brasília; BrasilFil: Cáceres, Nilton. Universidade Federal de Santa María. Santa María; BrasilFil: Pardini, Renata. Universidade de Sao Paulo; BrasilFil: Leite, Yuri L. R.. Universidade Federal do Espírito Santo; BrasilFil: Costa, Leonora Pires. Universidade Federal do Espírito Santo; BrasilFil: Mello, Marco Aurelio Ribeiro. Universidade Federal de Minas Gerais; BrasilFil: Fischer, Erich. Universidade Federal do Mato Grosso do Sul; BrasilFil: Passos, Fernando C.. Universidade Federal do Paraná; BrasilFil: Varzinczak, Luiz H.. Universidade Federal do Paraná; BrasilFil: Prevedello, Jayme A.. Universidade do Estado de Rio do Janeiro; BrasilFil: Cruz-Neto, Ariovaldo P.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Carvalho, Fernando. Universidade do Extremo Sul Catarinense; BrasilFil: Reis Percequillo, Alexandre. Universidade de Sao Paulo; BrasilFil: Paviolo, Agustin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Duarte, José M. B.. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil. Fundación Oswaldo Cruz; BrasilFil: Bernard, Enrico. Universidade Federal de Pernambuco; BrasilFil: Agostini, Ilaria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Lamattina, Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Ministerio de Salud de la Nación; ArgentinaFil: Vanderhoeven, Ezequiel Andres. Ministerio de Salud de la Nación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentin

Estudio serológico de Neospora caninum en ganado de leche del altiplano Norte de Antioquia, Colombia

RESUMEN Objetivos. Determinar la seroprevalencia de Neospora caninum en ganado lechero sin vacunar del altiplano Norte de Antioquia. Materiales y métodos. Se realizó un estudio transversal para determinar la prevalencia de neosporosis en bovinos de la principal zona lechera antioqueña. En Mayo-Junio del 2014 se recolectaron muestras de sangre de 1003 bovinos en 29 hatos ubicados en el municipio de San Pedro de los Milagros, en Antioquia. Los niveles de anticuerpos de N. caninum se midieron mediante un kit de inmunoensayo enzimático (ELISA) y los resultados se clasificaron como positivos o negativos. Resultados. Se observaron bovinos seropositivos en todos los hatos, con una prevalencia entre el 7 y 97%, y una media (±SE) del 37.1% (±4.2). La distribución de los animales seropositivos por grupos  de <1, 1-2, 2-3 y ≥3 años de edad fue del 25.5, 30.3, 46.1 y 39.1%, respectivamente. Conclusiones. Se detectó una alta tasa de seroprevalencia de N. caninum en la principal zona de ganado lechero de Antioquia. La gran variación entre hatos apunta a que existen factores de riesgo cuya identificación sería esencial a la hora de instaurar programas de control. En vista de que cualquier vaca seropositiva tiene un mayor riesgo de abortar que congéneres no infectados, los próximos estudios deberían abordar la epidemiología de abortos atribuible a neosporosis previo a establecer cualquier plan de control

Combined Raman spectroscopic and Rietveld analyses as a useful and nondestructive approach to studying flint raw materials at prehistoric archaeological sites

Artículo sobre análisis de espectroscopía

Coralsnake venomics: Analyses of venom gland transcriptomes and proteomes of six Brazilian taxa

Venom gland transcriptomes and proteomes of six Micrurus taxa (M. corallinus, M. lemniscatus carvalhoi, M. lemniscatus lemniscatus, M. paraensis, M. spixii spixii, and M. surinamensis) were investigated, providing the most comprehensive, quantitative data on Micrurus venom composition to date, and more than tripling the number of Micrurus venom protein sequences previously available. The six venomes differ dramatically. All are dominated by 2–6 toxin classes that account for 91–99% of the toxin transcripts. The M. s. spixii venome is compositionally the simplest. In it, three-finger toxins (3FTxs) and phospholipases A2 (PLA2s) comprise >99% of the toxin transcripts, which include only four additional toxin families at levels ≥0.1%. Micrurus l. lemniscatus venom is the most complex, with at least 17 toxin families. However, in each venome, multiple structural subclasses of 3FTXs and PLA2s are present. These almost certainly differ in pharmacology as well. All venoms also contain phospholipase B and vascular endothelial growth factors. Minor components (0.1–2.0%) are found in all venoms except that of M. s. spixii. Other toxin families are present in all six venoms at trace levels (<0.005%). Minor and trace venom components differ in each venom. Numerous novel toxin chemistries include 3FTxs with previously unknown 8- and 10-cysteine arrangements, resulting in new 3D structures and target specificities. 9-cysteine toxins raise the possibility of covalent, homodimeric 3FTxs or heterodimeric toxins with unknown pharmacologies. Probable muscarinic sequences may be reptile-specific homologs that promote hypotension via vascular mAChRs. The first complete sequences are presented for 3FTxs putatively responsible for liberating glutamate from rat brain synaptosomes. Micrurus C-type lectin-like proteins may have 6–9 cysteine residues and may be monomers, or homo- or heterodimers of unknown pharmacology. Novel KSPIs, 3× longer than any seen previously, appear to have arisen in three species by gene duplication and fusion. Four species have transcripts homologous to the nociceptive toxin, (MitTx) α-subunit, but all six species had homologs to the β-subunit. The first non-neurotoxic, non-catalytic elapid phospholipase A2s are reported. All are probably myonecrotic. Phylogenetic analysis indicates that the six taxa diverged 15–35 million years ago and that they split from their last common ancestor with Old World elapines nearly 55 million years ago. Given their early diversification, many cryptic micrurine taxa are anticipated