Knowledge Representation with Multiple Logical Theories and Time

We present a knowledge representation framework where a collection of logic programs can be combined together by means of meta-level program composition operations. Each object-level program is composed of a collection of extended clauses, equipped with a time interval representing the time period in which they hold. The interaction between program composition operations and time yields a powerful knowledge representation language in which many applications can be naturally developed. The language is given a meta-level semantics which also provides an executable specification. Moreover, we define an abstract semantics by extending the immediate consequence operator from a single logic program to compositions of logic programs and taking into account time intervals. The operational, meta-level semantics is proven sound and complete with respect to the abstract bottom-up semantics. The approach is further extended in order to cope with the problem of reasoning over joined intervals of time. Three applications in the field of business regulations are shown

The layered structure of company share networks

We present a framework for the analysis of corporate governance problems using network science and graph algorithms on ownership networks. In such networks, nodes model companies/shareholders and edges model shares owned. Inspired by the widespread pyramidal organization of corporate groups of companies, we model ownership networks as layered graphs, and exploit the layered structure to design feasible and efficient solutions to three key problems of corporate governance. The first one is the long-standing problem of computing direct and indirect ownership (integrated ownership problem). The other two problems are introduced here: computing direct and indirect dividends (dividend problem), and computing the group of companies controlled by a parent shareholder (corporate group problem). We conduct an extensive empirical analysis of the Italian ownership network, which, with its 3.9M nodes, is 30× the largest network studied so far

Opening the black box: a primer for anti-discrimination

The pervasive adoption of Artificial Intelligence (AI) models in the modern information society, requires counterbalancing the growing decision power demanded to AI models with risk assessment methodologies. In this paper, we consider the risk of discriminatory decisions and review approaches for discovering discrimination and for designing fair AI models. We highlight the tight relations between discrimination discovery and explainable AI, with the latter being a more general approach for understanding the behavior of black boxes

GLocalX - From Local to Global Explanations of Black Box AI Models

Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications

Phenotypic and genotypic resistance to colistin in E. coli isolated from wild boar (Sus scrofa) hunted in Italy

The One Health approach is not only focused on diseases and zoonosis control but also on antimicrobial resistance. As concern this important issue, the problem of plasmid-mediated colistin resistance recently emerged. Few studies reported data about colistin resistance and mcr genes in bacteria from wildlife. In this manuscript, 168 Escherichia coli isolated from hunted wild boar were tested; colistin resistance was evaluated by MIC microdilution method, and the presence of mcr-1 and mcr-2 genes was evaluated by PCR. Overall, 27.9% of isolates resulted resistant to colistin, and most of them showed a MIC value > 256 μg/mL. A percentage of 44.6% of tested E. coli scored positive for one or both genes. In details, 13.6% of isolated harbored mcr-1 and mcr-2 in combination; most of them exhibiting the highest MIC values. Interestingly, 19.6% of mcr-positive E. coli resulted phenotypically susceptible to colistin. Wild boar could be considered a potential reservoir of colistin-resistant bacteria. In the light of the possible contacts with domestic animals and humans, this wild species could play an important role in the diffusion of colistin resistance. Thus, the monitoring programs on wildlife should include this aspect

Oral administration of chestnut tannins to reduce the duration of neonatal calf diarrhea

Background: Neonatal calf diarrhea is generally caused by infectious agents and is a very common disease in bovine practice, leading to substantial economic losses. Tannins are known for their astringent and anti- inflammatory properties in the gastro-enteric tract. The aim of this study was to evaluate the effect of the oral administration of chestnut tannins (Castanea sativa Mill.) in order to reduce the duration of calf neonatal diarrhea. Twenty-four Italian Friesian calves affected by neonatal diarrhea were included. The duration of the diarrheic episode (DDE) was recorded and the animals were divided into a control group (C), which received Effydral® in 2 l of warm water, and a tannin-treated group (T), which received Effydral® in 2 l of warm water plus 10 g of extract of chestnut tannins powder. A Mann-Whitney test was performed to verify differences for the DDE values between the two groups. Results: The DDE was significantly higher in group C than in group T (p = 0.02), resulting in 10.1 ± 3.2 and 6.6 ± 3. 8 days, respectively. Conclusions: Phytotherapic treatments for various diseases have become more common both in human and in veterinary medicine, in order to reduce the presence of antibiotic molecules in the food chain and in the environment. Administration of tannins in calves with diarrhea seemed to shorten the DDE in T by almost 4 days compared to C, suggesting an effective astringent action of chestnut tannins in the calf, as already reported in humans. The use of chestnut tannins in calves could represent an effective, low-impact treatment for neonatal diarrhea

Evaluation of different methods to estimate the transfer of immunity in donkey foals fed with colostrum of good IgG quality: A preliminary study

The aims of the present study were to evaluate the correlation between IgG Serum Radial Immunodiffusion (SRID), Electrophoresis Gamma Globulins (EGG), Electrophoresis Total Protein (ETP) and the serum total protein (TP) analyzed by refractometry and by a dry chemistry analyzer (Biuret) and to estimate serum IgG concentrations using serum TP. A total of 36 samples collected at four different times (birth, 6, 12, 24 hours after birth) from nine Amiata donkey foals were evaluated with SRID, EGG, ETP, serum TP Biuret and refractometry. SRID IgG concentration increased significantly over time until T12. Serum TP analyzed with refractometry, electrophoresis and Biuret showed a statistically significant difference between T0 and T6 vs T12 and T24. A good or strong correlation was found between different tests performed. Equations to quantify serum IgG were created and can be used for estimating the donkey foals’ serum IgG in the first day of life. Serum TP refractometry showed a high correlation with SRID IgG (0.91) which may be a particularly useful and economic instrument to estimate the transfer of immunity in donkey foals during the first day of life

Evaluation of jennies' colostrum: IgG concentrations and absorption in the donkey foals. A preliminary study

Immunoglobulin type G (IgG) concentration both in jennies' colostrum and in serum of donkey foals are mostly unknown in the first 24 h after delivery. The aims of the present study were to evaluate the IgG concentrations of colostrum during the first 24 h of lactation of Amiata jennies, the absorption of colostrum and the weekly body weight gain of the donkey foals. IgG concentrations were assessed in the jennies' colostrum and in the serum of donkey foals. Colostrum was collected in 9 jennies ready after delivery, and at 6, 12, 24 h after foaling from both halves. Serum was collected at the same sampling times from 9 donkey foals. Donkey foals were weighted at birth and then weekly until the 28th days of life. Temporal changes of IgG concentrations in dam's colostrum and in donkey foal serum were analyzed by a linear regression model and a general linear model, respectively. Results showed that colostrum IgG concentration were similar between the left and the right half. Colostrum IgG concentrations decreased continuously throughout the time in all jennies by 0.0244 Log10 mg/mL per hour. Serum IgG concentrations in donkey foals at birth was significantly lower compared to other times. No correlation was found between the colostrum IgG concentrations and the average weekly body weight gain of the donkey foal. The pattern of colostrum IgG levels in jennies and serum IgG concentration in donkey foals seem to be similar to what reported for equine. However, the donkey foals seem to be less agammaglobulinemic at birth compared to the horse foal. The pattern and both serum and colostrum concentrations evaluated in the Amiata donkeys were slightly different from results reported in other donkey breeds, underlying the importance of setting references specific to breed

Mammary cistern size during the dry period in healthy dairy cows: A preliminary study for an ultrasonographic evaluation

We evaluated the udder cistern (UC) size during the dry period using ultrasound. Forty healthy quarters were evaluated in both the longitudinal and cross-section of the UC. Quarters were evaluated at the drying-off (T0) and 24 h later (T1), then regularly until the end of the dry period (T7–T58), during the colostrum production phase (TCPP) and at 7 days in milking (T7PP). The Spearman test was applied to find the correlation between the ultrasonographic UC size (UUCS) assessment and time. The Friedman test and Dunn’s test for multiple comparisons as a post-hoc test were performed to compare the forequarter and hindquarter cross-sections (FQCSs and HQCSs, respectively) and the forequarter and hindquarter longitudinal sections (FQLSs and HQLSs, respectively) at T0 vs. T58 vs. TCPP vs. T7PP. A total of 440 images were evaluated. A negative linear correlation between time and FQCS and FQLS (r = −0.95; p < 0.0004) and between time and HQCS and HQLS (r = −0.90; p < 0.002) was found. The UUCS decreased throughout the dry period, starting to increase at the beginning of the next lactation. Measuring the UUCS provides useful information for monitoring the dry period

Hepatic adrenal rest tumor in a patient with multifactorial liver cirrhosis: a case report with CT and MRI findings and pathologic correlation

AbstractBackgroundAdrenal rest tumor is an ectopic collection of adrenocortical cells in an extra-adrenal site, more frequently located around the kidney, retroperitoneum, spermatic cord, para-testicular region and broad ligament, but very rarely occurring also in the liver. Hepatic adrenal rest tumor poses a diagnostic challenge in differentiating it from hepatocellular carcinoma, particularly in a cirrhotic liver.Case presentationAn 83-years-old male was referred to our hospital by his family doctor for hepatological evaluation due to multifactorial liver cirrhosis. Ultrasound revealed a centimetric hypoechoic nodule in the VI hepatic segment in the context of a liver with signs of cirrhosis and steatosis. The patient first underwent MRI and then CT, which showed a fat containing focal liver lesion in the subcapsular location of the right lobe, strictly adjacent to the homolateral adrenal gland. The nodule was hypervascular in the arterial phase, washed out in the portal-venous and transitional phases, resulting hypointense in the hepato-biliary phase at MR imaging. In the suspicion of a hepatocellular carcinoma, the nodule was surgically removed, and the patient's postoperative course was unremarkable. The final histopathological diagnosis was of adrenal rest tumor of the liver.ConclusionsHepatic adrenal rest tumor is an extremely rare hepatic tumor, often without any clinical manifestation, that can also occur in the cirrhotic liver as in our case. Although there are not specific imaging findings, the possible diagnosis of HART should be considered when we observe a well-defined lesion in the subcapsular location of the right lobe, with fat containing, hypervascularity after contrast medium injection and vascular supply from the right hepatic artery