Detailed knowledge of nearshore topography and bathymetry is required for a wide variety of purposes, including ecosystem protection, coastal management, and flood and erosion monitoring and research, among others. Both topography and bathymetry are usually studied separately; however, many scientific questions and challenges require an integrated approach. LiDAR technology is often the preferred data source for the generation of topobathymetric models, but because of its high cost, it is necessary to exploit other data sources. In this regard, the main goal of this study was to present a methodological proposal to generate a topobathymetric model, using low-cost unmanned platforms (unmanned aerial vehicle and unmanned surface vessel) in a very shallow/shallow and turbid tidal environment (Bahía Blanca estuary, Argentina). Moreover, a cross-analysis of the topobathymetric and the tide level data was conducted, to provide a classification of hydrogeomorphic zones. As a main result, a continuous terrain model was built, with a spatial resolution of approximately 0.08 m (topography) and 0.50 m (bathymetry). Concerning the structure from motion-derived topography, the accuracy gave a root mean square error of 0.09 m for the vertical plane. The best interpolated bathymetry (inverse distance weighting method), which was aligned to the topography (as reference), showed a root mean square error of 0.18 m (in average) and a mean absolute error of 0.05 m. The final topobathymetric model showed an adequate representation of the terrain, making it well suited for examining many landforms. This study helps to confirm the potential for remote sensing of shallow tidal environments by demonstrating how the data source heterogeneity can be exploited.

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Studies based on continuous monitoring of diel changes in dissolved oxygen concentration allow the estimation of ecosystem metabolism and provide a measure of the overall trophic processes of an ecosystem. In this study, net ecosystem production (NEP), community/ecosystem respiration (R), and gross primary production (GPP) rates were estimated in relation to physicochemical and climatic variables for 18 months in La Salada, a saline shallow lake. Net autotrophic conditions prevailed during the study period (NEP: 64.05 ± 44.22 mmol O 2 m -2 day -1 ). GPP and R were positively correlated and were synchronized on a daily timescale, with GPP typically greater than R. Principal component analysis revealed that monthly rates of GPP, R, and NEP responded, as expected, to temperature and light seasonal patterns. Water level and conductivity fluctuations, because of evapoconcentration and water management, were relevant as a driver of the physicochemical and biological characteristics of the lake. In saline lakes as La Salada, an adequate management of water resources will be relevant to maintain the ecosystem equilibrium and the quality of its resources.

Objectives We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML’s accuracy with that of a breast radiologist, and verify if the radiologist’s performance is improved by using ML. Methods Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar’s test. Results After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70–90%) with an AUC of 0.82 (95% CI = 0.70–0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist’s performance improved when using ML (80.2%), but not significantly (p = 0.508). Conclusions A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images. Key Points • Machine learning showed good accuracy in discriminating benign from malignant breast lesions • The machine learning classifier’s performance was comparable to that of a breast radiologist • The radiologist’s accuracy improved with machine learning, but not significantly

Small analytes such as glucose, L-glutamine (Gln), and ammonium nitrate are detected by means of optical biosensors based on a very common nanostructured material, porous silicon (PSi). Specific recognition elements, such as protein receptors and enzymes, were immobilized on hydrogenated PSi wafers and used as probes in optical sensing systems. The binding events were optically transduced as wavelength shifts of the porous silicon reflectivity spectrum or were monitored via changes of the fluorescence emission. The biosensors described in this article suggest a general approach for the development of new sensing systems for a wide range of analytes of high social interest.

Understanding the drivers and how they affect ecosystem metabolism is essential for developing effective management policy and plans. In this study, net ecosystem production (NEP), ecosystem respiration (R), and gross primary production (GPP) rates were estimated in relation to physicochemical, hydrological, and meteorological variables in La Salada (LS) and Sauce Grande (SG), two shallow lakes located in an important agricultural region with water management. LS is a mesosaline, mesotrophic-eutrophic lake, whereas SG is a hyposaline and eutrophic lake. GPP and R showed daily and seasonal variations, with R exceeding GPP during most of the study period in both lakes. Net heterotrophic conditions prevailed during the study period (NEP LS: −1.1 mmol O2 m−2 day−1 and NEP SG: −1.25 mmol O2 m−2 day−1). From data analysis, the temperature, wind speed, and lake volume are the main drivers of ecosystem metabolism for both lakes. Despite the significant differences between the two lakes, the NEP values were similar. The different hydrological characteristics (endorheic vs. flushing lake) were crucial in explaining why the two different systems presented similar ecosystem metabolic rates, emphasizing the importance of water management.

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