AI slipping on tiles: data leakage in digital pathology

Reproducibility of AI models on biomedical data still stays as a major concern for their acceptance into the clinical practice. Initiatives for reproducibility in the development of predictive biomarkers as the MAQC Consortium already underlined the importance of appropriate Data Analysis Plans (DAPs) to control for different types of bias, including data leakage from the training to the test set. In the context of digital pathology, the leakage typically lurks in weakly designed experiments not accounting for the subjects in their data partitioning schemes. This issue is then exacerbated when fractions or subregions of slides (i.e. "tiles") are considered. Despite this aspect is largely recognized by the community, we argue that it is often overlooked. In this study, we assess the impact of data leakage on the performance of machine learning models trained and validated on multiple histology data collection. We prove that, even with a properly designed DAP (10x5 repeated cross-validation), predictive scores can be inflated up to 41% when tiles from the same subject are used both in training and validation sets by deep learning models. We replicate the experiments for $4$ classification tasks on 3 histopathological datasets, for a total of 374 subjects, 556 slides and more than 27,000 tiles. Also, we discuss the effects of data leakage on transfer learning strategies with models pre-trained on general-purpose datasets or off-task digital pathology collections. Finally, we propose a solution that automates the creation of leakage-free deep learning pipelines for digital pathology based on histolab, a novel Python package for histology data preprocessing. We validate the solution on two public datasets (TCGA and GTEx)

Drone and sensor technology for sustainable weed management: a review

Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts

Geomorphological evolution of western Sicily, Italy

This paper proposes a morphoevolutionary model for western Sicily. Sicily is a chain–foredeep–foreland ­system still being built, with tectonic activity involving uplift which tends to create new relief. To reconstruct the ­morphoevolutionary model, geological, and geomorphological studies were done on the basis of field survey and aerial photographic interpretation. The collected data show large areas characterized by specific geological, geomorphological, and topographical settings with rocks, landforms, and landscapes progressively older from south to north Sicily. The achieved results display: (1) gradual emersion of new areas due to uplift, its interaction with the Quaternary ­glacio-eustatic oscillations of the sea level, and the following production of a flight of stair-steps of uplifted marine ­terraces in southern Sicily, which migrates progressively upward and inwards; in response to the uplift (2) triggering of down-cutting processes that gradually dismantle the oldest terraces; (3) competition between uplift and down-cutting processes, which is responsible for the genesis of river valleys and isolated rounded hills in central Sicily; (4) continuous deepening over time that results in the exhumation of older and more resistant rocks in northern Sicily, where the higher heights of Sicily are realized and the older forms are retained; (5) extensional tectonic event in the northern end of Sicily, that produces the collapse of large blocks drowned in the Tyrrhenian Sea and sealed by coastal-marine deposits during the Calabrian stage; (6) trigger of uplift again in the previously subsiding blocks and its interaction with coastal processes and sea level fluctuations, which produce successions of marine terraces during the Middle–Upper Pleistocene stages

TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting

none6We introduce TAASRAD19, a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 timesteps of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section at 5 min sampling rate, covering an area of 240 km of diameter at 500 m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validate TAASRAD19 as a benchmark for nowcasting methods by introducing a TrajGRU deep learning model to forecast reflectivity, and a procedure based on the UMAP dimensionality reduction algorithm for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available on GitHub (https://github.com/MPBA/TAASRAD19) for study replication and reproducibility.noneFranch, Gabriele; Maggio, Valerio; Coviello, Luca; Pendesini, Marta; Jurman, Giuseppe; Furlanello, CesareFranch, Gabriele; Maggio, Valerio; Coviello, Luca; Pendesini, Marta; Jurman, Giuseppe; Furlanello, Cesar

Anthocyanins are Key Regulators of Drought Stress Tolerance in Tobacco

Abiotic stresses will be one of the major challenges for worldwide food supply in the near future. Therefore, it is important to understand the physiological mechanisms that mediate plant responses to abiotic stresses. When subjected to UV, salinity or drought stress, plants accumulate specialized metabolites that are often correlated with their ability to cope with the stress. Among them, anthocyanins are the most studied intermediates of the phenylpropanoid pathway. However, their role in plant response to abiotic stresses is still under discussion. To better understand the effects of anthocyanins on plant physiology and morphogenesis, and their implications on drought stress tolerance, we used transgenic tobacco plants (AN1), which over-accumulated anthocyanins in all tissues. AN1 plants showed an altered phenotype in terms of leaf gas exchanges, leaf morphology, anatomy and metabolic profile, which conferred them with a higher drought tolerance compared to the wild-type plants. These results provide important insights for understanding the functional reason for anthocyanin accumulation in plants under stress

Root inoculation with Azotobacter chroococcum 76A enhances tomato plants adaptation to salt stress under low N conditions

Background: The emerging roles of rhizobacteria in improving plant nutrition and stress protection have great potential for sustainable use in saline soils. We evaluated the function of the salt-tolerant strain Azotobacter chroococcum 76A as stress protectant in an important horticultural crop, tomato. Specifically we hypothesized that treatment of tomato plants with A. chroococcum 76A could improve plant performance under salinity stress and sub-optimal nutrient regimen. Results: Inoculation of Micro Tom tomato plants with A. chroococcum 76A increased numerous growth parameters and also conferred protective effects under both moderate (50 mM NaCl) and severe (100 mM NaCl) salt stresses. These benefits were mostly observed under reduced nutrient regimen and were less appreciable in optimal nitrogen conditions. Therefore, the efficiency of A. chroococcum 76A was found to be dependent on the nutrient status of the rhizosphere. The expression profiles of LEA genes indicated that A. chroococcum 76A treated plants were more responsive to stress stimuli when compared to untreated controls. However, transcript levels of key nitrogen assimilation genes revealed that the optimal nitrogen regimen, in combination with the strain A. chroococcum 76A, may have saturated plant’s ability to assimilate nitrogen. Conclusions: Roots inoculation with A. chroococcum 76A tomato promoted tomato plant growth, stress tolerance and nutrient assimilation efficiency under moderate and severe salinity. Inoculation with beneficial bacteria such as A. chroococcum 76A may be an ideal solution for low-input systems, where environmental constraints and limited chemical fertilization may affect the potential yield