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Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize). [ABSTRACT FROM AUTHOR]

Landform mapping is the initial step of many geomorphological analyses (e.g. assessment of natural hazards and natural resources) and requires vast resources to be applied to wide areas at high-resolution. Among geomorphological objects, we focus on glacial moraine mapping, since it is a task relevant to many fields (e.g. paleoclimate and glacial geomorphology). Here we proposed to exploit the potential of Deep Learning-based approaches to map moraine landforms by exploiting multi-source remote sensing imagery. To this end, we propose the first Deep Learning model to map glacial moraines, namely MorNet. As multi-source remote sensing information, we combine together three different sources: Topographic (Pleiades-derived DSM), Multispectral (Sentinel-2), and SAR (Sentinel-1) data. To cope with such heterogeneous information, the proposed model has a dedicated branch for each input source and, a late fusion mechanism is leveraged to combine them with the aim to provide the final mapping. The performance of the MorNet model is evaluated on several glacier valleys in China in the Himalayan range. This area contains minimally eroded moraines, so they are well-defined and of varied morphology. The behavior of the proposed method is compared to models using individual mono-source models in order to highlight the benefit to simultaneously leverage multi-source information. The use of multi-source data allows MorNet to exploit the complementarity of the three input sources and improve its performance from an f1-score of about 41.6 using a single source to 52.8 using three sources. MorNet provides a first-order moraine map through its ability to identify well-defined moraines. Consequently, MorNet can identify areas likely to contain moraines and intends to be used as a tool by experts to facilitate and support large-scale mapping. [ABSTRACT FROM AUTHOR]

Deep learning-based land cover classifiers learnt from Satellite Image Time Series (SITS) are known to reach high performances. In order to explain, at least partly, the rationale leading to each one of their decisions, attention-based architectures have been proposed to automatically weight the importance of predefined data components in the classification process. Though generated for each decision separately, the informational content conveyed by such explanations can remain insufficient to end-users because of the complex nature of SITS. Moreover, getting a general perspective about the way a classifier works requires merging all explanations for each class and relating them to its mode of operation, which is not always straightforward. A preliminary and complementary approach for automatically identifying the data features detected by a pixel-wise deep spatiotemporal land cover classifier and explaining its behavior at the class level is therefore proposed in this paper. Classified pixels are first described using interpretable features coming under the form of data mining patterns. A redescription mining technique is then employed to automatically select, for each class, the features matching the different activation level configurations of the layer that is assumed to capture the aforementioned patterns. Experiments based on a Sentinel-2 time series and a deep spatiotemporal neural network implementing a channel-separated processing as well as a channel-based attention mechanism show the interest of such a combined approach.

Welfare assessment of dairy calves is commonly conducted through the observation of their behavior and the analysis of physiological parameters. Despite the large number of studies on this topic, there is a lack of research on the possible correlation between the physiological parameters and behavior at basal level in dairy calves. For this reason, the present study aimed to investigate the possible correlations between serum cortisol, neutrophil-to-lymphocyte ratio (N/L) and locomotor play behavior in 21 Friesian dairy calves within their first month of life. Every week, an arena test after blood collection was performed. A significant positive correlation between serum cortisol levels and N/L (rrm = 0.381; 95% CI = 0.083, 0.617; p = 0.012) and a negative correlation between time spent running and N/L (rrm = −0.351; 95% CI = −0.575, −0.077; p = 0.012) were found. On the contrary, no significant correlation between time spent running and cortisol levels was detected. Cortisol levels and N/L were higher in the newborn and had a constant decrease until reaching a stable value after the 24th day of life. These findings suggest a better predictivity of N/L, compared to serum cortisol, on locomotor play behavior.

(1) Background: Since 2013, the pathogenic bacterium Xylella fastidiosa has been severely affecting olive production in Apulia, Italy, with consequences for the economy, local culture, landscape and biodiversity. The production of a phenolic extract from fresh olive leaves was employed for endotherapeutic injection into naturally infected olive trees by Xylella fastidiosa in Apulia region, Italy. (2) Methods: The effectiveness of the extract was tested in vitro and in planta in comparison with analogous treatments based on garlic powder and potassium phosphite. (3) Results: The uptake of phenolic compounds from olive leaves through a trunk injection system device resulted in a statistically significant increase in leaf area index and leaf area density, as well as in the growth of newly formed healthy shoots. Plant growth-promoting effects were also observed for potassium phosphite. Moreover, the bacteriostatic activities of the phenolic extract and of the garlic-powder-based solution have been demonstrated in in vitro tests. (4) Conclusions: The results obtained and the contained costs of extraction make the endotherapeutic treatment with phenolic compounds a promising strategy for controlling X fastidiosa to be tested on a larger scale, although the experiments conducted in this study proved not to be suitable for centenary trees.

Archival documents and artworks stored in libraries frequently undergo degradative processes promoted by the so-called “biodeteriogens” that inhabit these places. A renewed interest in plant-derived products has arisen in those research groups focusing on cultural heritage preservation and looking for new and safe disinfection techniques. In this view, essential oils (EOs) and their volatile organic constituents are very appealing thanks to their versatility of action. A literature survey of the scientific publications involving EOs and/or their major constituents related to the conservation of paper items of cultural heritage interest is presented here, aiming to reveal benefits and limitations of such peculiar plant-derived compounds.

Electron distribution and bonding in extended structures based on square planar gold complexes, traditionally classified as gold (III), have been addressed through solid‐state DFT calculations. The band structure analysis, taken as a diagnostic tool, highlighted that, when the metal is associated with chalcogen atoms, the gold centers are more electron rich than the classic accepted description. This is in agreement with the Inverted Ligand Field (ILF) theory that predicts a metal center d10 configuration with two electrons vacancy on the orbital combinations of the ligands for sulfur, selenium and tellurium cases. A covalent bond is featured in oxygen compounds, a borderline situation between the ILF and the classical Ligand Field description. In conclusion, the gold atom in chalcogenide square planar complexes should be considered electron richer than commonly thought applying the traditional rules for calculating the oxidation state. [ABSTRACT FROM AUTHOR]

Improving food preservation technologies is a key aspect in the struggle to reduce global food waste, and natural antimicrobial substances, such as essential oil (EO) components represent very promising food preserving agent. However, their intrinsic chemico‐physical properties, such as the low melting point, low water solubility and high volatility, pose some practical difficulties in exploiting them for practical applications. Cocrystallization is used to stabilize liquid or volatile EO components providing them whit a crystalline environment, thus improving their potential application as antibacterial agents. Five EO active ingredients (THY = thymol, CAR = carvacrol, EUG = eugenol, CAD = trans‐cinnamaldehyde, and VAN = o‐vanillin) and two coformers (INA = Isonicotinamide, and HBA = 4‐hydroxybenzoic acid) have been combined and the corresponding cocrystals have been studied for their potential inhibiting effect against four food spoilage bacteria (Bacillus thuringiensis, Enterobacter cloacae, Pseudomonas fluorescens, and Serratia marcescens). The structures of the five cocrystals have been used to derive structure‐activity relationships in terms of release energy of the active ingredients form the crystalline environment, and a correlation has been derived with the Intermolecular Interaction Energies of the EO molecules. [ABSTRACT FROM AUTHOR]

A mitochondrial stroke-like event is an evolving subacute neurological syndrome linked to seizure activity and focal metabolic brain derangement in a genetically determined mitochondrial disorder. The acronym “MELAS” (mitochondrial encephalopathy associated with lactic acidosis and stroke-like lesions) identifies subjects with molecular, biochemical and/or histological evidence of mitochondrial disorder who experience stroke-like lesions. MELAS is a rare inherited mitochondrial disease linked to severe multiorgan involvement and stress-induced episodes of metabolic decompensation and lactic acidosis. Unfortunately, there are no etiopathogenetic therapies for stroke-like episodes to date, and the treatment is mainly based on anti-epileptic drugs and supportive therapies. This perspective opinion article discusses the current care standards for MELAS patients and revises current and innovative emerging therapies for mitochondrial stroke-like episodes.

Due to the proliferation of Earth Observation programmes, information at different spatial, spectral and temporal resolution is collected by means of various sensors (optical, radar, hyperspectral, LiDAR, etc.). Despite such abundance of information, it is not always possible to obtain a complete coverage of the same area (especially for large ones) from all the different sensors due to: (i) atmospheric conditions and/or (ii) acquisition cost. In this context of data (or modalities) misalignment, only part of the area under consideration could be covered by the different sensors (modalities). Unfortunately, standard machine learning approaches commonly employed in operational Earth monitoring systems require consistency between training and test data (i.e., they need to match the same information schema). Such a constraint limits the use of additional fruitful information, i.e., information coming from a particular sensor that may be available at training but not at test time. Recently, a framework able to manage such information misalignment between training and test information is proposed under the name of Generalized Knowledge Distillation (GKD). With the aim to provide a proof of concept of GKD in the context of multi-source Earth Observation analysis, here we provide a Generalized Knowledge Distillation framework for land use land cover mapping involving radar (Sentinel-1) and optical (Sentinel-2) satellite image time series data (SITS). Considering that part of the optical information may not be available due to bad atmospheric conditions, we make the assumption that radar SITS are always available (at both training and test time) while optical SITS are only accessible when the model is learnt (i.e., it is considered as privileged information). Evaluations are carried out on a real-world study area in the southwest of France, namely Dordogne, considering a mapping task involving seven different land use land cover classes. Experimental results underline how the additional (privileged) information ameliorates the results of the radar based classification with a main gain on the agricultural classes.

Severe acute respiratory syndrome–correlated new coronavirus (SARS-Cov-2) infection may result in neurological signs and symptoms through different mechanisms. Although direct infection of the central nervous system is uncertain or very rare and the para-infectious complications (e.g. inflammatory neuropathies) are rare, delirium and septic encephalopathy are common in severely ill patients. Smell dysfunction and headache are very common in mild cases, especially in younger people and females. Muscle pain is common in both mild and severe cases, and in the most compromised patients, it is accompanied by increased creatine kinase levels and by a likely true myopathic damage. Ischemic stroke has been reported as a possible complication of the hypercoagulability associated with severe SARS-Cov-2 infection, but further studies are needed. Most of the neurological manifestations may occur early in the illness. Therefore, during the pandemic period, neurologists need to be involved, alert, and prepared. Neurological practice will not be the same until a vaccine is available.

The presence and infestation level of Bactrocera oleae was monitored in an organic olive orchard divided into differently treated parcels with kaolin (K), spinosad (S) and with kaolin and spinosad (K + S) in alternate rows. The treatments did not seem to affect olive fruit fly population dynamics, while statistically significant protective effects were recorded against total and harmful infestation, but not against the active one. Eventually, neither kaolin nor spinosad were shown to have a particular detrimental effect against naturally occurring B. oleae parasitoids.