Self-supervised out-of-distribution detection in wireless capsule endoscopy images.

While deep learning has displayed excellent performance in a broad spectrum of application areas, neural networks still struggle to recognize what they have not seen, i.e., out-of-distribution (OOD) inputs. In the medical field, building robust models that are able to detect OOD images is highly critical, as these rare images could show diseases or anomalies that should be detected. In this study, we use wireless capsule endoscopy (WCE) images to present a novel patch-based self-supervised approach comprising three stages. First, we train a triplet network to learn vector representations of WCE image patches. Second, we cluster the patch embeddings to group patches in terms of visual similarity. Third, we use the cluster assignments as pseudolabels to train a patch classifier and use the Out-of-Distribution Detector for Neural Networks (ODIN) for OOD detection. The system has been tested on the Kvasir-capsule, a publicly released WCE dataset. Empirical results show an OOD detection improvement compared to baseline methods. Our method can detect unseen pathologies and anomalies such as lymphangiectasia, foreign bodies and blood with > 0.6. This work presents an effective solution for OOD detection models without needing labeled images

Validation of Sentinel-3a Sral Coastal Sea Level Data at High Posting Rate: 80Hz

Altimetry data of two and a half years (June 2016-November 2018) of Sentinel 3A SRAL were validated at the sampling frequency of 80 Hz. The study areas are three coastal sites in Spain: Huelva (HU) (Gulf of Cadiz), Barcelona (BA), and Bilbao (BI). Two tracks were selected in each site: one ascending and one descending. Data were validated using in situ tide gauge (TG) data provided by the Spanish Puertos del Estado. In the 5 to 20 km segment, the results were 6-8 cm (rmse) and 0.7-0.8 (r) for all the tracks. The 0 to 5 km segment was also analyzed in detail to study the land effect on the altimetry data quality. The results showed that the track orientation, the angle of intersection with the coast, and the land topography concur to determine the nearest distance to the coast at which the data retain a similar level of accuracy than in the 5 to 20 km segment. This distance of good quality to shore reaches a minimum of 3 km for the tracks at HU and the descending track at BA

Actinobacteria isolated from subterranean and cultural heritage: implications for biotechnology

Due to their extraordinary properties, Actinobacteria can thrive in extreme environments, such as limestone caves, lava tubes and stone monuments. They grow forming microbial mats and speleothems on the walls and ceilings of caves, ranging from extensive coatings to small colonies (Riquelme et al. 2015). Their colour includes yellow, tan, orange, grey, pink and white. Recently, we have found abundant yellow and white-coloured bacterial mats coating the cave walls and secondary mineral deposits (speleothems) of lava tubes from La Palma Island, Canary Islands, Spain (Gonzalez-Pimentel et al. 2018) and Mount Etna in Catania (Sicily, Italy). Field Emission Scanning Electron Microscopy (FESEM) of the coloured microbial mats revealed abundant Actinobacteria-like cells, including a variety of filaments and spore structures with smooth surface ornamentation or profuse surface appendages. The DNA-/RNA-based analyses confirmed that these microbial mats are mainly composed of metabolically active Actinobacteria (Gonzalez-Pimentel et al. 2018). It is well known that Actinobacteria, mainly isolated from marine and soil ecosystems, are an important source of bioactive compounds, with Streptomyces ranking first with a huge number of bioactive secondary metabolites (Guo et al. 2015). These compounds, not only produced by Streptomyces but also by Bacillus, are very important to the industrial sector, such as pharmacology, biofuel and food industries, as well as to the conservation of stone cultural heritage, due to their antimicrobial properties (Silva et al. 2017). In the last decades, these sectors have intensified demands for exploring novel eco-friendly bioactive compounds, which stresses the need to investigate new groups of Actinobacteria from underexplored habitats. Yet, Actinobacteria from caves have not been the target of intensive screening for bioactive secondary metabolites. Hence, Actinobacterial-like microbial mats were collected and isolated from lava tubes in La Palma and Mount Etna to investigate their biotechnological potential. The screening of antimicrobial activity was based both on culture-dependent techniques using the agar diffusion assay and on metagenomics. Our study has showed that the strain Streptomyces sp. MZ0467C isolated from La Palma lava tube has antimicrobial activity against Microbacterium, Rhodococcus, Arthrobacter, Kocuria, Sphingomonas and Paenibacillus due to its ingenious adaptations and metabolic strategies to survive under extreme environmental conditions. This demonstrates that Actinobacteria from subterranean environments are promising sources of antibacterial compounds with interest for cultural heritage conservation

Time-based self-supervised learning for Wireless Capsule Endoscopy

State-of-the-art machine learning models, and especially deep learning ones, are significantly data-hungry; they require vast amounts of manually labeled samples to function correctly. However, in most medical imaging fields, obtaining said data can be challenging. Not only the volume of data is a problem, but also the imbalances within its classes; it is common to have many more images of healthy patients than of those with pathology. Computer-aided diagnostic systems suffer from these issues, usually over-designing their models to perform accurately. This work proposes using self-supervised learning for wireless endoscopy videos by introducing a custom-tailored method that does not initially need labels or appropriate balance. We prove that using the inferred inherent structure learned by our method, extracted from the temporal axis, improves the detection rate on several domain-specific applications even under severe imbalance. State-of-the-art results are achieved in polyp detection, with 95.00 ± 2.09% Area Under the Curve, and 92.77 ± 1.20% accuracy in the CAD-CAP dataset

An enhanced recruitment of blue whiting in the Porcupine bank (NE Atlantic) during 2020 in response to favourable environmental conditions

A Spanish bottom trawl research survey was conducted between 2001 and 2020 in the Porcupine Bank to retrieve recruitment data. The survey was routinely carried out in September with the objective of assessing the fisheries in the area. The 2020 data showed the largest abundance of age-0 blue whiting (Micromesistius poutassou), with almost twice as much than in the highest previous record (2004). Thus, this study focused on determining the environmental drivers that could explain that anomalously high abundance through their impact on the blue whiting eggs and larvae survival. For this purpose, satellite SST and chlorophyll were analyzed during the spawning season (March-April), along with reanalysis wind, salinity, and ocean currents data. Our results showed particularly low wind conditions during March and April 2020, which triggered the onset of a stable Taylor Column circulation above the Porcupine Bank, helping not only the accumulation of phytoplankton biomass, which promoted secondary productivity, but also larval retention. This was corroborated by a quantile regression fit applied on the blue whiting recruitment data (September), which showed significant positive (negative) correlations with the chlorophyll concentration (wind mixing index) during the spawning season

Looking for environmental drivers of blue whiting recruitment in the Porcupine Bank (NE Atlantic)

Although temporal and spatial match and mismatch of sh larvae with their potential prey organisms is considered the main factor regulating the year-class strength in marine sh populations, di erent environmental forcings also in uence the survival rate of larvae and therefore recruitment. In 2020, the highest abundance of year-class recruits (total lenght < 20 cm) of Micromesistius poutassou was observed in the record of the Spanish Bottom Trawl Survey on the Porcupine Bank (September) from 2001-2020. Various environmental parameters, namely chlorophyll concentration, surface salinity, temperature, ocean currents, and wind data were used to study their potential impact on the blue whiting eggs and larvae survival. Our results showed that in 2020, during the blue whiting-spawning season (March-April), the calm wind situation along with weaker ocean currents above the Porcupine Bank helped to accumulate phytoplankton biomass, thus promoting secondary productivity. The optimal salinity concentration, as well as surface temperature during this time, helped the buoyancy of eggs and larvae to the food-rich surface, thus improving the larval condition and enhanced the survival rate, which in turn resulted in the largest recruitment since 200

Environmental forcing on blue whiting year-class strength in the Porcupine bank (NE Atlantic)

The highest abundance of age-0 blue whiting Micromesistius poutassou in the Porcupine Bank since 2001 was observed in 2020. Various environmental parameters, namely chlorophyll concentration, surface salinity, temperature, ocean currents, and wind data were used to study their potential impact on the blue whiting eggs and larvae survival. Our results showed that in 2020, during the blue whiting-spawning season (March-April), the calm wind situation along with weaker ocean currents above the Porcupine Bank helped to accumulate phytoplankton biomass, thus promoting secondary productivity. The optimal salinity concentration, as well as surface temperature during this time, helped the buoyancy of eggs and larvae to the food-rich surface, thus improving the larval condition and enhanced the survival rate, which in turn resulted in the highest year-class recruitment since 2001

Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy

Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%

Assessing the trophic ecology of the invasive Atlantic Blue Crab Callinectes sapidus in the coastal waters of the Gulf of Cadiz

Callinectes sapidus, the invasive blue crab from the west of the Atlantic Ocean, has extended its distribution along the Atlantic coast around the Gulf of Cadiz and increased massively since 2016. Food web studies are useful for understanding changes in ecosystems caused by exotic species. Stable isotope analysis (δ13C and δ15N) were used to assess the potential carbon sources and its trophic relationships among different ecosystems (estuaries and saltmarshes), sexes (male and females) and seasons (summer vs. autumn). Significant differences were found in the δ13C of blue crabs from the estuaries and salt-marshes (-21.2 ±2.6 vs -14.2 ±0.9, respectively). These differences may be explained by an increase in the 13C of the blue crabs from the salt-marshes, probably due to the enrichment of this isotope in the primary producers such as the salt marsh plants [1] and its preys inhabiting this ecosystem. Meanwhile, the more depleted 13C values in the estuary crabs seem to reflect a carbon source from mollusks and fish derived from decomposing detritus. Among the ecosystems analyzed, δ15N was only significantly enriched in the crabs of the Guadalquivir estuary and was higher in males than females. Also, seasonal differences were found in this estuary in both sexes, by a decrease in the 15N values between summer and autumn. Those differences, can be explained by the fact that the Guadalquivir estuary suffers nitrogen hyper-nitrification due to intensive agriculture and is more noticeable in the upper part of the estuary, to which the males are more associated due to their life cycle. Previous studies reported, the diet of C. sapidus seems to be opportunistic, dependent on the food availability in different habitats [2], with a divergence in sexes induced by different spatial distributions. Future studies analyzing the stomach content and trophic behavior should be conducted to clarify our results

Anatomical landmarks localization for capsule endoscopy studies

Wireless Capsule Endoscopy is a medical procedure that uses a small, wireless camera to capture images of the inside of the digestive tract. The identification of the entrance and exit of the small bowel and of the large intestine is one of the first tasks that need to be accomplished to read a video. This paper addresses the design of a clinical decision support tool to detect these anatomical landmarks. We have developed a system based on deep learning that combines images, timestamps, and motion data to achieve state-of-the-art results. Our method does not only classify the images as being inside or outside the studied organs, but it is also able to identify the entrance and exit frames. The experiments performed with three different datasets (one public and two private) show that our system is able to approximate the landmarks while achieving high accuracy on the classification problem (inside/outside of the organ). When comparing the entrance and exit of the studied organs, the distance between predicted and real landmarks is reduced from 1.5 to 10 times with respect to previous state-of-the-art methods