**1. Introduction**

The biotic components of any ecosystem are diverse in constitution and dependent on limited and specific resources in order to subsist, adapt, and evolve. Species interact on a broad spectrum, ranging from neutral interaction to lethal parasitism. Elucidating the network of eco-epidemiology is not only vital for understanding disease emergence, but also how it was established and escalated. Other viruses such as bird/swine flu, Ebola, SARS, and recently, nCoV-2019 have been discussed in relation to how cross-species transmission behavior could lead to viral evolution in a new host [1]. Thus, the capacity of a pathogenic organism to be a multi-host infective agent is not restricted to viruses, bacteria, and helminths. Indeed, fungi such as *Aspergillus*, *Penicillium*, and *Fusarium* spp. are able to infect multiple host species [2–5]. Although infective mechanisms have been observed significantly across species, inter-kingdom infective patterns are very rare. Inter-kingdom pathogens like *Fusarium* as well as others

with their cognate hosts are well-known. However, to our knowledge, there is no evidence that the infective pattern is sequentially maintained across the inter-kingdom jump. Interestingly, other diverse parasites in humans such as *Pseudomonas* spp. show the same cross-infecting behavior from plant to animal [6].

The genus *Fusarium* is comprised of diverse and ubiquitous hyaline filamentous fungi that are adaptable to any habitable niche, making them the quintessential opportunistic pathogens [7]. These fungi and their mycotoxins adversely a ffect approximately 80 economically important crops [8]. However, over the last 30 years, they have emerged as an opportunistic human pathogen, producing lethal systemic infections with a wide range of morbidities in superficial infections [9,10]. This change in epidemiology is likely due to a number of complex factors. Certain strains infect a broad spectrum of host organisms, ranging from plants and insects to humans [3,5,11]. In humans, *Fusarium* can produce fungal keratitis, also known as ocular keratomycosis, which results in severe vision complications. This condition is also a significant cause of surgical intervention in 15%–27% of cases, leading to corneal transplantation, enucleation, removal of eye contents, or even treatment for vision deterioration caused by non-e ffective drug treatment [12]. Various fungi species can cause this illness, but *Fusarium* is the main causative agent in 37%–50% of fungal keratitis cases [13,14]. Onychomycosis is another human infection caused by *Fusarium* fungi [14]. In plants, *Fusarium* is the most persistent fungi isolated from soil that is associated with vascular invasive mycoses, and its conidia can infect aerial tissue such as corn ears. In maize, *Fusarium* spp. can provoke rots and blight that a ffect stalk, grain, roots, and seedlings [15–17]. Mycotoxins produced by *Fusarium* are a prominent economic issue since they can cause crop loss, in addition to having important animal and human health repercussions [18,19]. *Fusarium* species and their respective strains are rapidly becoming multidrug-resistant [20]. From a health perspective, one of the most harmful *Fusarium*-related diseases is ocular keratomycosis, given the fact that ophthalmic fungal infections represent one of the main etiologic factors of blindness in humans. Although the precise reasons are unknown, the incidence of fungal keratitis has dramatically increased in the last two decades, particularly in countries such as China, India, Brazil, and Mexico [9].

Transmission of pathogens between plants and humans has been hypothesized in the past, but a specific mechanism could not be detected [21]. Fungi are capable of both direct and indirect transmission. Inter-kingdom infective patterns across species is rare. The present study elucidates the ability of clinical samples of *Fusarium* to infect monocotyledonous (corn) or dicotyledonous (*Arabidopsis*) plants as well as human tissue by testing 13 specific fungal keratitis samples from four *Fusarium* species. The findings in the present study are a step forward in clarifying whether there is a cyclical pattern of infection between plants and humans, or whether the infection is only oriented in a plant-to-human direction.


*2.3. Artificial Neural Network Modelling*

The applicability of the artificial neural network models for predicting the DES pH values based on the σ profiles was also studied. The best neural network was chosen based on the following criteria: *R*2 and *RMSE* for training, test, and validation sets taking into account the number of neurons in the hidden layer. The properties of the created networks that were chosen are shown in Table 3. Based on the goodness of fit and validation error and considering the number of neurons in the hidden layer, the MLP model 10-5-1 was selected as optimal. Fewer neurons in the hidden layer make the ANN architecture simpler. The selected ANN was characterized by 10 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer. The hidden activation function for the selected ANN was Tanh while the output activation function was Logistic. The described ANN provides a good agreement between the experimental data and the data predicted by the model (*R*2 validation = 0.9797, *RMSE*validation = 0.0012). As presented in Figure 1c, it can be observed that the data are distributed around the fitted function and that there are no evident outliers. As for the MLP and PLR models, the residual analysis was also performed for the ANN model (Figure 2c) and confirmed the ANN model's goodness of fit through a normal probability plot of the residuals (Figure 2c), residuals versus the predicted values plot (Figure 2c), histogram of the residuals (Figure 2c), and residuals versus the order of the data plot (Figure 2c).

**Table 3.** Architecture of the developed ANN (selected network is marked in bold). The numbers in the network name denote the number of neurons in the input, hidden, and output layers, respectively.

Based on the presented results, it can be concluded that the σ profiles are good molecular descriptors of DESs since the mathematical correlation of the experimental and predicted values is high. Moreover, based on the obtained *R*2 values and the residual analysis, it can be concluded that both the PLR and ANN model can be efficiently applied for the prediction of the DES pH values based on the σ profiles. Due to the simplicity of the PLR model, this model is proposed for the prediction of physicochemical properties.

#### *2.4. MLR, PLR, and ANN Models' Independent Validation*

Validation of the MLR, PLR, and ANN models developed for the prediction of the DES pH values based on the σ profiles was performed on the independent set of data. The validation set included the σ profiles of 16 DESs. Comparisons between the experimental data and model-predicted data are shown in Figure 2. The validation performance of the developed models was estimated based on *R*2 and *RMSE* and the obtained values were as follows: (i) for MLR *R*2 = 0.7097, *RMSE* = 1.1140; (ii) for PLR *R*2 = 0.8605, *RMSE* = 0.7652; and (iii) for ANN *R*2 = 0.8885, *RMSE* = 0.82926.

It can be noticed that all three proposed models predict the pH value with high accuracy. As expected, the highest *R*2 between the experiment and model-predicted data was obtained for ANN prediction of the analyzed DES pH values while the lowest *R*2 between the experiment and model-predicted data was obtained for the MLR model. These findings demonstrate that σ profile ANN modeling is a useful and reliable method for predicting DES pH values based on the σ profiles. Nevertheless, considering RMSE, it can be noticed that the PLR model can efficiently be used for the prediction of pH values based on the σ profiles. As described, the *R*2 values are scaled between 0 and 1, whereas the RMSE is not scaled to a specific value and, therefore, provides explicit information about how much the prediction deviates.

As stated before, it was relatively easy to link the parameters of the MLR and PLR models to their physical significance. On the other hand, ANNs, by definition, belong to a class of agnostic models and, thus, it is difficult, if not impossible, to reveal their physical meaning. At the same time, this is the reason why they behave much better in interpolation than in extrapolation. The independent validation presented here may be considered as interpolation since the DES members of the independent validation dataset belong to the same DES classes as those used for constructing the model. However, given the rather simple and rather clear relation between the σ profile and pH as revealed by MLR, there is no true reason to believe that the models would behave poorly in extrapolation, even for ANN, i.e., for DES classes not involved in the development of the models. However, this is yet to be checked, e.g., for DESs based on metal chlorides or DESs containing ionic liquids, etc.

The current literature data refer to the prediction of other physicochemical properties (such as viscosity and density) and only a narrow range of values characteristic for limited groups of structurally related DESs [11–14]. Based on our current knowledge, only one study has investigated the development of a mathematical model for DES pH value prediction [13]. In that study, the pH literature data of 41 DESs were processed in a similar way using the COSMO-RS and mathematical models, MLR and ANN, also covering a variety of cations, anions, and functional groups. The literature study [12] used literature data and included different temperatures (with temperature as an input parameter) while our study used our data obtained at a single temperature. The literature study also showed the potential of MLR and ANN modeling for the prediction of the pH value, however, with more complex models (models with more coefficients) than those developed in this work. Taking into consideration the specific future application of the developed models, it is recommended that they are as simple as possible and as robust as possible. Summing up the presented results, it can be concluded that the PLR model developed in this research can efficiently be used for the prediction of a wide range of DES pH values based on the σ profiles.

#### **3. Materials and Methods**

#### *3.1. Materials*

Betaine, choline chloride, glucose, L-(−)-proline, oxalic acid, sucrose, sorbitol, and xylitol were all purchased from Acros Organics, USA. Citric acid, D-fructose, D-(+)-xylose, D,L-malic acid, ethylene glycol, glycerol, and urea were all purchased from Sigma-Aldrich, USA. BIOVIA TmoleX19 version 2021 software (Dassault Systèmes, Vélizy-Villacoublay, France) was used for geometry and energy optimization of the HBAs and HBDs used in this study. BIOVIA COSMOtherm 2020 version 20.0.0. software (Dassault Systèmes) was used for the σ profile calculations of the defined DESs.

#### *3.2. Methods*

#### 3.2.1. DES Preparation

DESs were prepared by mixing defined molar ratios of HBA to HBD. The two or more components were weighed in a specific ratio in a round-bottomed glass flask, adding 10–50% (*w*/*w*) of water. Then, the flasks were sealed, and the mixtures stirred and heated to 50 ◦C for 2 h until homogeneous transparent colorless liquids formed. The DES abbreviations and corresponding molar ratios are given in Table 1.


**1. Introduction**

The monitoring of the oceanic surface currents is a major scientific and socio-economic challenge. The ocean currents modulate natural and anthropogenic processes at different space and time scales, from global climate change to local dispersal of tracers and pollutants, with relevant impacts on marine ecosystem services and maritime activities (e.g., optimization of the ship routes, maritime safety, coastal protection). An appropriate monitoring of the oceanic currents necessitates high frequency and high resolution observations of the global ocean, which are achieved using satellite measurements. Nowadays, no satellite mission provides a direct measurement of the sea surface currents and their global-to-regional scale monitoring is mainly provided by satellite altimetry. Being based on the observation of the sea surface height (SSH), satellite altimetry reconstructs the large-scale geostrophic component of the surface motions at an operational level. Indeed, the altimeter-derived currents have spatio-temporal spectral responses from the mesoscale to the basin-scale range [1], corresponding to temporal scales from some weeks to several years. This is not sufficient for many applications, especially in semi-enclosed basins as the Mediterranean Sea, where the most energetic variable signals are found at relatively small scales [2,3]. In fact, the synoptic retrieval of the sea surface dynamics in the mesoscale-to-submesoscale range, i.e., at spatial resolutions of a few kilometres and temporal

resolution of a few days, is a crucial topic in physical oceanography. The characterization of the surface processes at these scales has significant impacts on the human activities in the marine context [4,5] and can also provide information on the interior dynamics, e.g., [6]. This can be obtained either by conceiving new satellite sensors and/or by optimizing the present-day space-based observations.

For instance, the incoming NASA/CNES Surface Water and Ocean Topography Satellite (SWOT) mission aims at retrieving the global SSH at 15 km horizontal resolution, providing improvements for the retrieval of the surface geostrophic flow (though the temporal coverage of the global ocean will be around 20 days) [7]. In addition, the potential observations from the sea surface KInematics Multiscale monitoring mission (SKIM, presently in competitive feasibility phase to be the ESA-EE9) aims at providing wide-swath measurements of the sea surface currents at 40 km horizontal resolution, going down to a few kilometers for along-swath data, and errors even lower than 0.1 m·s−1 [8,9].

On the other hand, a number of approaches are based on the exploitation of the satellite observations in the radio, thermal or optical band, today available at horizontal resolutions up to 1 km (or higher), e.g., [10,11]. For example, the Surface Quasi Geostrophic theory (SQG) relies on the assumption that the surface motions are mostly driven by the sea surface density gradients. This technique proved successful at reconstructing the surface velocity field from single sea-surface temperature (SST) bi-dimensional images [12] and also includes the possibility of deriving the 3-D structure of the current field from surface observations [6,13]. An SQG based approach was also exploited combining the phase of SST and the amplitude of SSH observations, allowing to implement one of the first applications for the global ocean currents retrieval from the synergy of SSH and SST data [14,15]. One requirement of this approach is the condition that SST and SSH patterns are in phase, which is mostly verified for deep Mixed Layer conditions. Moreover, the SQG is based on the assumption that surface density gradients are mostly driven by SST. In reality, the sea-surface salinity (SSS) can also regulate the surface dynamics by compensating the SST anomalies. In the Mediterranean Sea this was observed in the Algerian basin by Isern-Fontanet et al. 2016 [16] and, at global scale, this condition was reported in 0.12% of the cases based on a three years global scale statistics [14].

The radio, thermal and optical-band satellite observations can be exploited for the analysis of consecutive cloud-free two-dimensional images, e.g., sea surface temperature (SST) and surface Chlorophyll concentration (Chl). For instance, the images can be used to infer the surface currents field via maximum cross correlation techniques or inverting the surface tracer dynamical evolution equation [17–20]. However, these approaches only account for the currents horizontal advection and diffusion, neglecting the effects of the source and sink terms for the tracer evolution. Given a surface tracer distribution, these techniques satisfactorily retrieve the cross-gradient velocity field but the along-gradient component cannot be directly inferred and no information can be retrieved in low tracer gradient conditions. In the past, this limitation was overcome by assuming the existence of stochastic forcings for the statistical derivation of unknown parameters [21]. More recently, Piterbarg 2009 (PIT09 hereinafter) [22] solved this issue via an optimal combination of the tracer information with the one of a background velocity. The PIT09 method, whose dynamical framework considers both the effects of advection and of the source/sink terms on the tracer evolution, was successfully applied to an oil spill monitoring case study [23], in which the background velocities were derived from a numerical simulation. In [23], the authors pointed out the potential of using their method with satellite-derived tracers, as SST or Chl, as the main perspective of their results. A first confirmation of their findings appeared recently. Indeed, Rio et al. 2016 [24] showed the potentiality of combing altimetric and thermal-band satellite observations for improving the oceanic surface currents retrieval (nowadays provided by satellite altimetry) at global scale. This was proved in a model study, via an Observing System Simulation Experiment (OSSE) based on one year of currents data simulated via the MERCATOR operational model [25]. The authors showed that, if the large-scale geostrophic currents are combined with the information coming from the SST, the average global improvements in the currents retrieval can reach 35% locally. The method was further successfully applied to satellite altimetry and SST observations (Rio and Santoleri 2018 (RS18 hereinafter)) [26].

The objective of our study is to further exploit the RS18 method to tackle a more challenging application, i.e., we aim at merging the geostrophic currents (derived from sea surface height, SSH) with the SST observations in the Mediterranean Sea. Indeed, the Mediterranean is characterized by Rossby deformation radii that can go down to 10–20 km, hence, its typical mesoscale features (O(10–100 km)) are only partially captured by classical satellite altimetry [3]. Thus, we attempt to quantify the potential of the RS18 method in unvealing the Mediterranean Sea small scale and ageostrohic circulation from space observations, which is not possible using the altimeter estimates alone. In addition, the interest for improving the surface currents retrieval in the Mediterranean has commercial and environmental implications. In fact, this region represents the main ship route between the Indian Ocean and the main harbours of the European Union and hosts 25% of the world oil trade, with a consequent presence of illegal oil spills of the order of 600 kt per year [27]. This makes high-resolution surface currents a necessary information for monitoring the Mediterranean Sea and improving the knowledge of its surface dynamics.

The aim of this paper is to describe the derivation of the merged SSH/SST currents (Optimal currents hereinafter) and to illustrate their assessment via comparison with several in-situ and model derived surface currents. At the same time, the capability of retrieving the small scale ageostrophic circulation from satellite observations is demonstrated. The paper is structured as follows. In Section 2 we present the datasets involved in our study, i.e., used for the determination and the quality assessment of the Optimal currents. Then, Section 3 details the computation of the optimal currents. In Section 4 we will discuss the Optimal currents performances in two test-cases in the western Mediterranean and the Sicily Channel. We go on assessing the quality of the optimal currents via quantitative comparisons with model as well as in-situ derived surface currents and, in the final section, we will give the main conclusions and perspectives of the study.

## **2. Data**

The following datasets have been used to derive and assess the quality of the optimal currents:

	- The daily, 1/20◦ maps of L4 SSS obtained from the multifractal fusion applied to the L3 Soil Moisture and Ocean Salinity (SMOS) observations. The data are distributed by the Barcelona Expert Center (BEC) [34]. The data are available from 2011 to 2016;
	- The 4-daily, 1/4◦ LOCEAN L3 SSS estimates derived from a combination of SMOS ascending and descending orbits measurements [35] and are available from 2010 to 2017.

In the list given above, the datasets labeled with 1 and 2 will be combined according to the method described in PIT09 to obtain the Optimal sea surface currents in the period 2012–2016. The Optimal currents will constitute a series of daily gap-free sea surface currents for the Mediterranean basin mapped onto a regular 1/24◦ grid (more details will be given in Section 3). On the other hand, the datasets labeled with numbers from 3 to 9 will be used for the quality assessment of the Optimal Currents, as we will show in Section 4.


*Review* **The Microbiota-Bone-Allergy Interplay**

**Maria Maddalena Sirufo 1,2, Francesca De Pietro 1,2, Alessandra Catalogna 1,2, Lia Ginaldi 1,2 and Massimo De Martinis 1,2,\***

> 1 Department of Life, Health and Environmental Sciences, University of L'Aquila, Piazzale Salvatore Tommasi n. 1, 67100 L'Aquila, Italy<EMAIL_ADDRESS>(M.M.S<EMAIL_ADDRESS>(F.D.P.); alessandra.cat4@gmail.com(A.C.);lia.ginaldi@cc.univaq.it(L.G.)

**Abstract:** Emerging knowledge suggests an increasing importance of gut microbiota in health and disease. Allergy and bone metabolism are closely interconnected, and the possible negative effects of common therapies are not the only aspects of this relationship. The immune system is influenced by the microbiota-host interactions, and several pieces of evidence suggest the existence of an interplay between microbiota, bone metabolism, and allergies. Understanding these interrelationships is essential for the development of new potential strategies of treatment and prevention targeting microbiota. A wide range of substances and germs, prebiotics and probiotics, are capable of influencing and modifying the microbiota. Prebiotics and probiotics have been shown in several studies to have different actions based on various factors such as sex, hormonal status, and age. In this review, we summarize the latest knowledge on the topic, and we discuss practical implications and the need for further studies.

**Keywords:** microbiota; gut microbiota; allergy; osteoporosis; bone metabolism; food allergy; skeletal health; inflammation; osteoimmunology


*4.5. Software*

Gaussian 09 [59] and 16 [60] produced optimized structures and the extrapolation sequence of RHF, MP2, and coupled-cluster energies. Atoms-in-Molecules results were obtained with AIMALL [61]. NCIPLOT was obtained by download from the Contreras-Garcia group [62] and LMODEA software for the local modes analysis [63] was provided by the Kraka group.

**Supplementary Materials:** The following are available online, which includes: Electronic energies used in extrapolation; Cartesian coordinates of all species; AIM figures with bond paths, line critical points and associated values of the Laplacian of the electron density.

**Author Contributions:** Conceptualization, C.T. and Z.A.; methodology, C.T.; software, Z.A. and E.A.B.; validation, C.T., Z.A. and E.A.B.; formal analysis, C.T.; investigation, C.T., Z.A. and E.A.B.; resources, C.T., Z.A. and E.A.B.; data curation, C.T.; writing—original draft preparation, C.T.; writing— review and editing, C.T. and Z.A.; visualization, C.T. and Z.A.; supervision and project administration, C.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** Our thanks to Elfi Kraka and Marek Freindorf of Texas Christian University for supplying the LMODEA software. Thanks also to the Body foundation which made possible the acquisition of software and computational resources used in the project. E.A.B. is particularly grateful to BAPKO of Marmara University providing computational resources through Grant No. FEN-A-071015-0477.

**Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Sample Availability:** Samples of the compounds Not applicable; no chemical material is used in this research.