Case Study on the Impact of Water Resources in Beef Production: Corn vs. Triticale Silage in the Diet of Limousine × Podolian Young Bulls.

In this study, we have included the water footprint (WF) in the process of optimizing animal feed rations. The global footprint of cattle production accounts for the largest share (33%) of the global water footprint of livestock production. Using two homogeneous groups of Limousine × Podolian young bulls, two different diets were compared: corn silage feeding (CSF), with a corn silage-based diet; and triticale silage feeding (TSF), with a triticale silage-based diet. Silage constituted about 41% and 46% of the feed composition (for CSF and TSF, respectively). Diets were characterised by the same energy and protein content. Despite the lower WF in the TSF group than in the CSF group (7726 vs. 8571 L/day/calf respectively), no significant differences were found in animal performances (i.e., daily weight gain and final weight), feed conversion or income over feed costs. These results show that simple production decisions can have a significant impact on water resource. Therefore, the use of triticale silage should be further promoted, especially in world regions with limited water resources where low WF feed formulation is more strategic than elsewhere.

Dietary Supplementation with Oregano and Linseed in Autochthonous "Facciuta Lucana" Goats: Effects on Meat Quality Traits in Suckling Kids.

Extruded linseed (Linum usitatissimum) in ruminant diets has been investigated as a strategy to improve the nutritional value and healthiness of meat fat; however, increased polyunsaturated fatty acids may limit the shelf-life of meat. Oregano (Origanum vulgare) has a documented antioxidant activity. The aim of the study was to investigate the effects of dietary supplementation with extruded linseed and oregano on goat milk quality and whether the characteristics of goat milk affect the physical and chemical features, fatty acid profile, meat lipid oxidation, and sensory properties of meat from suckling kids. Thirty-six male kids were weaned and divided into three homogeneous groups (n = 12); each group was either fed a control diet (C), or a diet containing 3% extruded linseed with or without the addition of 0.6% oregano ("L + O" and "L", respectively). The diets containing linseed lowered (p < 0.05) the saturated fatty acid content in meat, and increased (p < 0.05) monounsaturated and polyunsaturated fatty acids and conjugated linoleic acid. Oregano addition to the linseed diet proved to be effective in preserving meat shelf-life, as shown by a significant (p < 0.01) reduction in the malondialdehyde concentration after 10 days of storage, and improved meat succulence, tenderness, juiciness, and overall acceptance.

Design and Experimental Validation of a 3D-Printed Embedded-Sensing Continuum Robot for Neurosurgery.

A minimally-invasive manipulator characterized by hyper-redundant kinematics and embedded sensing modules is presented in this work. The bending angles (tilt and pan) of the robot tip are controlled through tendon-driven actuation; the transmission of the actuation forces to the tip is based on a Bowden-cable solution integrating some channels for optical fibers. The viability of the real-time measurement of the feedback control variables, through optoelectronic acquisition, is evaluated for automated bending of the flexible endoscope and trajectory tracking of the tip angles. Indeed, unlike conventional catheters and cannulae adopted in neurosurgery, the proposed robot can extend the actuation and control of snake-like kinematic chains with embedded sensing solutions, enabling real-time measurement, robust and accurate control of curvature, and tip bending of continuum robots for the manipulation of cannulae and microsurgical instruments in neurosurgical procedures. A prototype of the manipulator with a length of 43 mm and a diameter of 5.5 mm has been realized via 3D printing. Moreover, a multiple regression model has been estimated through a novel experimental setup to predict the tip angles from measured outputs of the optoelectronic modules. The sensing and control performance has also been evaluated during tasks involving tip rotations.

A General Approach for the Modelling of Negative Feedback Physiological Control Systems.

Mathematical models can improve the understanding of physiological systems behaviour, which is a fundamental topic in the bioengineering field. Having a reliable model enables researchers to carry out in silico experiments, which require less time and resources compared to their in vivo and in vitro counterparts. This work's objective is to capture the characteristics that a nonlinear dynamical mathematical model should exhibit, in order to describe physiological control systems at different scales. The similarities among various negative feedback physiological systems have been investigated and a unique general framework to describe them has been proposed. Within such a framework, both the existence and stability of equilibrium points are investigated. The model here introduced is based on a closed-loop topology, on which the homeostatic process is based. Finally, to validate the model, three paradigmatic examples of physiological control systems are illustrated and discussed: the ultrasensitivity mechanism for achieving homeostasis in biomolecular circuits, the blood glucose regulation, and the neuromuscular reflex arc (also referred to as muscle stretch reflex). The results show that, by a suitable choice of the modelling functions, the dynamic evolution of the systems under study can be described through the proposed general nonlinear model. Furthermore, the analysis of the equilibrium points and dynamics of the above-mentioned systems are consistent with the literature.

Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review.

BACKGROUND AND OBJECTIVE: Mechanistic-based Model simulations (MM) are an effective approach commonly employed, for research and learning purposes, to better investigate and understand the inherent behavior of biological systems. Recent advancements in modern technologies and the large availability of omics data allowed the application of Machine Learning (ML) techniques to different research fields, including systems biology. However, the availability of information regarding the analyzed biological context, sufficient experimental data, as well as the degree of computational complexity, represent some of the issues that both MMs and ML techniques could present individually. For this reason, recently, several studies suggest overcoming or significantly reducing these drawbacks by combining the above-mentioned two methods. In the wake of the growing interest in this hybrid analysis approach, with the present review, we want to systematically investigate the studies available in the scientific literature in which both MMs and ML have been combined to explain biological processes at genomics, proteomics, and metabolomics levels, or the behavior of entire cellular populations. METHODS: Elsevier Scopus®, Clarivate Web of Science™ and National Library of Medicine PubMed® databases were enquired using the queries reported in Table 1, resulting in 350 scientific articles. RESULTS: Only 14 of the 350 documents returned by the comprehensive search conducted on the three major online databases met our search criteria, i.e. present a hybrid approach consisting of the synergistic combination of MMs and ML to treat a particular aspect of systems biology. CONCLUSIONS: Despite the recent interest in this methodology, from a careful analysis of the selected papers, it emerged how examples of integration between MMs and ML are already present in systems biology, highlighting the great potential of this hybrid approach to both at micro and macro biological scales.

Sensory Profile and Consumer Liking of Sustainable Salamis Differing in Wild Boar Meat and Seasoning Ingredients Addition.

The production of game meat is a proven way of promoting sustainable food, which is also consistent with the proper management of the expansion of the wild boar population in Italy. In the present study, we investigated consumer response to sensory attributes and consumer preference for ten types of "cacciatore" salamis prepared with different mixtures of wild boar/pork (30/50 or 50/50) and spice ingredients. PCA analysis showed a clear characterization of the salamis based on the first component with the hot pepper powder and fennel types differing from the others. For the second component, salamis without flavorings could be discriminated by those flavored with aromatized garlic wine or with black pepper only. The main findings of the hedonic test revealed that products with hot pepper and fennel seeds received the highest ratings, as well as satisfactory acceptance in the consumer test sensory analysis for eight out of ten products. The panelists and consumers' ratings were influenced by the flavors used, but not by the ratio of wild boar to pork. This gives us the opportunity to produce more cost-effective and environmentally friendly products, as doughs with a high proportion of wild boar meat can be used without affecting product preference.

Detection of Suspicious Cardiotocographic Recordings by Means of a Machine Learning Classifier.

Cardiotocography (CTG) is one of the fundamental prenatal diagnostic methods for both antepartum and intrapartum fetal surveillance. Although it has allowed a significant reduction in intrapartum and neonatal mortality and morbidity, its diagnostic accuracy is, however, still far from being fully satisfactory. In particular, the identification of uncertain and suspicious CTG traces remains a challenging task for gynecologists. The introduction of computerized analysis systems has enabled more objective evaluations, possibly leading to more accurate diagnoses. In this work, the problem of classifying suspicious CTG recordings was addressed through a machine learning approach. A machine-based labeling was proposed, and a binary classification was carried out using a support vector machine (SVM) classifier to distinguish between suspicious and normal CTG traces. The best classification metrics showed accuracy, sensitivity, and specificity values of 92%, 92%, and 90%, respectively. The main results were compared both with results obtained by considering a more unbalanced dataset and with relevant literature studies in the field. The use of the SVM proved to be promising in the field of CTG classification. However, appropriate feature selection and dataset balancing are crucial to achieve satisfactory performance of the classifier.

Effect of the Addition of Donkey Milk on the Acceptability of Caciotta Cow Cheese.

This study investigated the effects of adding donkey milk in cheesemaking on the acceptability of a Caciotta cow cheese after 10 and 45 days of ripening. The cheeses produced were: a control cheese with cow's milk only and experimental cheese with the addition of 5% donkey's milk. The acceptability of Caciotta was determined by the judgement of 80 habitual cheese consumers. The acceptability of the Caciotta cheese was significantly influenced by the addition of donkey milk, with the exception of the texture parameter. At a ripening time of 10 days, the control cheese scored significantly higher than the experimental cheese for aroma, flavor and overall liking; conversely, at the longest time, the experimental cheese had significantly higher scores for color, aroma, flavor, and overall liking. Our results confirm that the use of donkey milk in cheesemaking can improve cheese acceptability. In addition, the known benefits of using donkey milk in cheesemaking, such as the reduction of blowing defects and the probiotic properties, could increase interest in innovative products among both processors and consumers. Processors could reduce, if not eliminate, the use of additives in cheesemaking, while cheese could also appeal to consumers of probiotic and fermented products.

Diet Selection by the Italian Hare (Lepus corsicanus de Winton, 1898) in Two Protected Coastal Areas of Latium.

This study was focused on the diet and feeding behaviour of Lepus corsicanus in two protected coastal areas of Latium, Castelporziano Presidential Estate (CPE) and Circeo National Park (CNP). Plant frequency was assessed by the quadrat method, while diet composition was determined by microhistological analysis of faecal samples. Over the year, the Italian hare fed on 185 of the 229 plant species identified in vegetation, with most of them ingested in low percentages (≤1%). During the dry season (DS), in both areas, Brachypodium sylvaticum, Cynodon dactylon, and Avena fatua were among the most consumed species. In the wet season (WS) the most common plant species in diet were B. sylvaticum, Poa trivialis, and Carex distachya in CPE and Dactylis glomerata, Cynosurus echinatus, and Spartium junceum in CNP. In both sites, considering the annual selection of life forms, grasses and leguminous forbs were preferred, while non-leguminous forbs and shrubs were used less than expected according to their availability. ANOSIM analysis showed significant differences between sites in DS and WS diets. Our study evidenced that the Italian hare behaved as generalist, revealing its capability for exploiting several plant species and to adapt its diet preferences to space-time variation of food availability.

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals.

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

CBRA: Cardiac biomarkers release analyzer.

BACKGROUND AND OBJECTIVES: The most advanced technologies and continuous innovations in the medical field require a necessary interaction between the clinical and the engineering world. In this context, software applications are proposed as a bridge between the two scientific fields and, therefore, as powerful tools, easy to use, and with great analytical skills. In this work, we propose CBRA as an innovative software platform, moving towards personalized medicine, which aims to simplify and speed up the triage of patients and support doctors in the diagnostic and prognostic phase. METHODS: The computational core of the devised software application consists of a model-based identification algorithm, which enables the reconstruction of the cardiac biomarkers release curves in patients with ST-Elevation Acute Myocardial Infarction (STEMI). Identification and parametric optimization techniques allow the application of the proposed approach to each singular patient: based on a few experimental acquisitions, CBRA can extrapolate several quantitative features of high clinical relevance, thus facilitating and rendering more objective the clinical evaluation and therapeutic choices. A dedicated database to collect and manage patients clinical and personal data, as well as a graphical user interface, provides clinicians and researchers with an intuitive and user-friendly environment. RESULTS: In the following work, we present some examples of the possible applications of CBRA, ranging from the management of the cardiac biomarkers time-series, up to the real analysis of the clinical features that CBRA can extract from the reconstructed curve, such as, e.g., maximum concentration values of biomarkers in the plasma and relative times, in the distinct phases of the acute myocardial infarction, or identification of the time to onset of symptoms. CONCLUSIONS: CBRA makes it easy for clinicians to use modeling and parametric identification tools to reconstruct release curves. Furthermore, CBRA provides support to the clinical decision, thanks to its capability to extract information of high clinical relevance, not easily obtainable from the mere visual analysis of experimental samples. Having information about the previously listed clinical parameters could allow, e.g., identify in which stage of AMI the patient is, when She/He goes to the emergency room, with significant benefits in the therapy.

Deciphering the Role of Wnt and Rho Signaling Pathway in iPSC-Derived ARVC Cardiomyocytes by In Silico Mathematical Modeling.

Arrhythmogenic Right Ventricular cardiomyopathy (ARVC) is an inherited cardiac muscle disease linked to genetic deficiency in components of the desmosomes. The disease is characterized by progressive fibro-fatty replacement of the right ventricle, which acts as a substrate for arrhythmias and sudden cardiac death. The molecular mechanisms underpinning ARVC are largely unknown. Here we propose a mathematical model for investigating the molecular dynamics underlying heart remodeling and the loss of cardiac myocytes identity during ARVC. Our methodology is based on three computational models: firstly, in the context of the Wnt pathway, we examined two different competition mechanisms between ß-catenin and Plakoglobin (PG) and their role in the expression of adipogenic program. Secondly, we investigated the role of RhoA-ROCK pathway in ARVC pathogenesis, and thirdly we analyzed the interplay between Wnt and RhoA-ROCK pathways in the context of the ARVC phenotype. We conclude with the following remark: both Wnt/ß-catenin and RhoA-ROCK pathways must be inactive for a significant increase of PPARγ expression, suggesting that a crosstalk mechanism might be responsible for mediating ARVC pathogenesis.

Feeding Preferences of the Italian Roe Deer (Capreolus capreolus italicus Festa, 1925) in a Coastal Mediterranean Environment.

The present study was aimed to deepen the knowledge on diet and on feeding preferences of the Italian roe deer. The research was carried on in the Castelporziano Presidential Estate, a protected area of Latium coast. Quadrat method was used to assess plant frequency, whereas diet composition was determined by fecal micro-histological analysis. A total of 143 plant species were identified in the site and 109 of them were found in the feces of the Capreolus capreolus italicus. All over the year, most of the taxa were ingested in low percentages (≤5%) and the most utilized plant species were Quercus suber, Prunus spinosa, Rubia peregrina, and Crateaegus monogyna. Fagaceae and Rosaceae plant families represented the bulk of the diet and were positively selected. In addition, a positive selection on Fabaceae and Oleaceae families was found in wet season but not in dry one. In both periods the Italian roe deer showed a positive selection for deciduous woody plants and evergreen woody plants, instead avoided wild forbs and wild graminoids. Our results confirmed that this subspecies behaved as a generalist highly selective feeder.

Metacognition and emotion regulation as treatment targets in binge eating disorder: a network analysis study.

BACKGROUND: This study aims to examine the underlying associations between eating, affective and metacognitive symptoms in patients with binge eating disorder (BED) through network analysis (NA) in order to identify key variables that may be considered the target for psychotherapeutic interventions. METHODS: A total of 155 patients with BED completed measures of eating psychopathology, affective symptoms, emotion regulation and metacognition. A cross-sectional network was inferred by means of Gaussian Markov random field estimation using graphical LASSO and the extended Bayesian information criterion (EBIC-LASSO), and central symptoms of BED were identified by means of the strength centrality index. RESULTS: Impaired self-monitoring metacognition and difficulties in impulse control emerged as the symptoms with the highest centrality. Conversely, eating and affective features were less central. The centrality stability coefficient of strength was above the recommended cut-off, thus indicating the stability of the network. CONCLUSIONS: According to the present NA findings, impaired self-monitoring metacognition and difficulties in impulse control are the central nodes in the psychopathological network of BED whereas eating symptoms appear marginal. If further studies with larger samples replicate these results, metacognition and impulse control could represent new targets of psychotherapeutic interventions in the treatment of BED. In light of this, metacognitive interpersonal therapy could be a promising aid in clinical practice to develop an effective treatment for BED.

Estimation of the Acute Myocardial Infarction Onset Time based on Time-Course Acquisitions.

Quantitative analysis of biochemical parameters is crucial for a correct diagnosis and prognosis of patients subject to acute myocardial infarction (AMI). In order to achieve a quantitative understanding of the dynamics of cardiac biomarkers, we have developed a mathematical model that can be exploited to extrapolate the release curve of cardiac troponin T (cTnT) into the plasma from few experimental acquisitions. The present work introduces a novel approach, based on the cTnT-release model, aimed at the identification of the infarct onset time. Indeed, in spite of the clinical importance of such information, in many cases, it is not easy to establish the exact time of occurrence of the ischemic event. We show that using a model-based optimization approach, the infarct onset time can be reliably estimated using the cTnT concentration acquisitions taken in the first few hours post-AMI. The assessment of the proposed approach is conducted on an experimental dataset, in which the infarct has been artificially induced and, therefore, the onset time is exactly known. In particular, the effectiveness of the devised estimation algorithm has been tested under several scenarios, with the first cTnT acquisition taken up to 12 h after AMI. Altogether, the proposed model-based approach provides a useful tool to help the clinicians in the quantitative estimation of important clinical parameters from the release curves of the cardiac biomarkers.

SlicerArduino: A Bridge between Medical Imaging Platform and Microcontroller.

Interaction between medical image platform and external environment is a desirable feature in several clinical, research, and educational scenarios. In this work, the integration between 3D Slicer package and Arduino board is introduced, enabling a simple and useful communication between the two software/hardware platforms. The open source extension, programmed in Python language, manages the connection process and offers a communication layer accessible from any point of the medical image suite infrastructure. Deep integration with 3D Slicer code environment is provided and a basic input-output mechanism accessible via GUI is also made available. To test the proposed extension, two exemplary use cases were implemented: (1) INPUT data to 3D Slicer, to navigate on basis of data detected by a distance sensor connected to the board, and (2) OUTPUT data from 3D Slicer, to control a servomotor on the basis of data computed through image process procedures. Both goals were achieved and quasi-real-time control was obtained without any lag or freeze, thus boosting the integration between 3D Slicer and Arduino. This integration can be easily obtained through the execution of few lines of Python code. In conclusion, SlicerArduino proved to be suitable for fast prototyping, basic input-output interaction, and educational purposes. The extension is not intended for mission-critical clinical tasks.

Meat quality and lipid fatty acid profile from wild thrush (Turdus philomelos), woodcock (Scolopax rusticola) and starling (Sturnus vulgaris): a preliminary comparative study.

BACKGROUND: The present study aimed to evaluate the nutritional proximate composition, some qualitative traits and fatty acid profile of meat from wild thrush, woodcock and starling hunted in Southern Italy in 2017 and 2018. METHODS: Nutritive composition and physical traits of meat and lipid fatty acid profile were evaluated in breast muscle (Pectoralis major) of gamebirds. RESULTS: From findings, the meat pH was significantly (P < 0.001) higher in starling when compared to the other two species. Thrush meat was significantly (P = 0.002) darker and had higher redness (P < 0.001) and yellowness (P = 0.004) in comparison to starling and woodcock. Thrush breast muscle showed the highest (P < 0.001) level of lipids and lowest (P < 0.001) protein content. Meat from thrush showed the best lipid fatty acid profile based on the higher (P < 0.001) monounsaturated fatty acids (MUFA) and lower (P < 0.001) saturated fatty acids (SFA) concentrations. Starling breast muscle reported the highest (P = 0.002) polyunsaturated fatty acids (PUFA) level compared to both thrush and woodcock, whereas no differences were detected on total n-3. The ratio n-6/n-3 was higher (P = 0.001) in starling muscle. Thrush breast muscle had the lowest (P < 0.001) atherogenic and thrombogenic indices compared to the other gamebirds. CONCLUSIONS: The findings indicated that meat from the three investigated gamebirds species may represent a healthily lipid food source for human consumption in relation to the prevention of cardiovascular diseases.

Identification of the infarct time in patients with acute myocardial infarction.

In the present work, we introduce a novel technique to identify the infarct time from time-series meaurements of the cardiac troponin T (cTnT) into plasma. Although this information is extremely valuable from a clinical standpoint, it is not always possible to establish with certainty the exact infarct time. Here, we show how the infarct time can be reliably estimated from the cTnT release data in the first few hours after AMI, by using an optimization-based procedure and a model-based approach. To validate the present approach, we have used a clinical dataset of patients in whom the infarct has been induced and, therefore, the infarct time is certain.

Artificial Intelligence for the Objective Evaluation of Acne Investigator Global Assessment.

INTRODUCTION: The evaluation of Acne using ordinal scales reflects the clinical perception of severity but has shown low reproducibility both intra- and inter-rater. In this study, we investigated if Artificial Intelligence trained on images of Acne patients could perform acne grading with high accuracy and reliabilities superior to those of expert physicians. METHODS: 479 patients with acne grading ranging from clear to severe and sampled from three ethnic groups participated in this study. Multi-polarization images of facial skin of each patient were acquired from five different angles using the visible spectrum. An Artificial Intelligence was trained using the acquired images to output automatically a measure of Acne severity in the 0-4 numerical range of the Investigator Global Assessment (IGA). RESULTS: The Artificial Intelligence recognized the IGA of a patient with an accuracy of 0.854 and a correlation between manual and automatized evaluation of r=0.958 (P less than .001). DISCUSSION: This is the first work where an Artificial Intelligence was able to directly classify acne patients according to an IGA ordinal scale with high accuracy, no human intervention and no need to count lesions. J Drugs Dermatol. 2018;17(9):1006-1009.

Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression.

Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.