Gut Microbiota and Bipolar Disorder: An Overview on a Novel Biomarker for Diagnosis and Treatment

The gut microbiota is the set of microorganisms that colonize the gastrointestinal tract of living creatures, establishing a bidirectional symbiotic relationship that is essential for maintaining homeostasis, for their growth and digestive processes. Growing evidence supports its involvement in the intercommunication system between the gut and the brain, so that it is called the gut–brain–microbiota axis. It is involved in the regulation of the functions of the Central Nervous System (CNS), behavior, mood and anxiety and, therefore, its implication in the pathogenesis of neuropsychiatric disorders. In this paper, we focused on the possible correlations between the gut microbiota and Bipolar Disorder (BD), in order to determine its role in the pathogenesis and in the clinical management of BD. Current literature supports a possible relationship between the compositional alterations of the intestinal microbiota and BD. Moreover, due to its impact on psychopharmacological treatment absorption, by acting on the composition of the microbiota beneficial effects can be obtained on BD symptoms. Finally, we discussed the potential of correcting gut microbiota alteration as a novel augmentation strategy in BD. Future studies are necessary to better clarify the relevance of gut microbiota alterations as state and disease biomarkers of BD.     

A model of abstraction in visual perception

In artificial intelligence, abstraction has been mainly studied as a mapping between languages in relation to problem-solving, with the aim of reducing the complexity of the task. However, abstraction has a much larger scope in reasoning; we are investigating, in this article, how abstraction can be used in concept representation. To this aim, we propose a novel, perception-based model of abstraction, which originates from the observation that conceptualization of a domain, even though involving entities belonging to several epistemological levels, is nevertheless primarily based on perception. This view has been recently advocated by Goldstone and Barsalou in cognitive science. A model of representation/abstraction is then proposed and its application to a real-world problem of robot visual perception and categorization is presented.     

A formal definition and a sound implementation of analogical reasoning in logic programming

A formal model of analogy is introduced in the logic programming setting, and an analogical reasoning program (called DIANA, i.e. Declarative Inference by ANAlogy) is developed in accordance with precise procedural and declarative semantics. Given the source and target domains of analogy as two logic programsP _s andP _t , together with a specificationS of the analogical correspondence between predicate symbols, atoms involving these symbols are analogically derived fromP=P _s P _t givenS, which are not derivable fromP _s orP _t orP _s P _t alone. In this paper, the requirements of the analogical process are first stated. The declarative semantics of analogy is then given, by defining the least analogical model ofP as an extension of the classical semantics of Horn clauses. A procedural semantics is also described, in terms of an extension of SLD resolution. Both semantics rely on implicit analogical axioms defining the kind of analogical reasoning envisaged. The implementation of DIANA has been done in Reflective Prolog, a metalogic programming language previously developed by the first two authors. It is shown that analogical axioms can be viewed as an instance of reflection axioms used in Reflective Prolog. By exploiting this feature, the implementation of DIANA is argued to be sound w.r.t. the defined semantics. Examples of analogical reasoning in DIANA are also described. By comparison with the AI literature on analogy, it is claimed that this is the first approach which gives a declarative semantics to analogical reasoning, thanks to the possibility of carrying over in this field the basic logic programming concepts.     

Evaluation of machine learning-based information extraction algorithms: criticisms and recommendations

We survey the evaluation methodology adopted in information extraction (IE), as defined in a few different efforts applying machine learning (ML) to IE. We identify a number of critical issues that hamper comparison of the results obtained by different researchers. Some of these issues are common to other NLP-related tasks: e.g., the difficulty of exactly identifying the effects on performance of the data (sample selection and sample size), of the domain theory (features selected), and of algorithm parameter settings. Some issues are specific to IE: how leniently to assess inexact identification of filler boundaries, the possibility of multiple fillers for a slot, and how the counting is performed. We argue that, when specifying an IE task, these issues should be explicitly addressed, and a number of methodological characteristics should be clearly defined. To empirically verify the practical impact of the issues mentioned above, we perform a survey of the results of different algorithms when applied to a few standard datasets. The survey shows a serious lack of consensus on these issues, which makes it difficult to draw firm conclusions on a comparative evaluation of the algorithms. Our aim is to elaborate a clear and detailed experimental methodology and propose it to the IE community. Widespread agreement on this proposal should lead to future IE comparative evaluations that are fair and reliable. To demonstrate the way the methodology is to be applied we have organized and run a comparative evaluation of ML-based IE systems (the Pascal Challenge on ML-based IE) where the principles described in this article are put into practice. In this article we describe the proposed methodology and its motivations. The Pascal evaluation is then described and its results presented.

Anisotropic Thermal Expansion of <Emphasis Type="Italic">p</Emphasis>-Terphenyl: a Self-Assembled Supramolecular Array of Poly-<Emphasis Type="Italic">p</Emphasis>-phenyl Nanoribbons

The recent discovery of superconductivity in a metallic aromatic hydrocarbon, alkali-doped p-terphenyl, has attracted considerable interest. The critical temperature T _c ranges from few to 123 K, the record for organic superconductors, due to uncontrolled competition of multiple phases and dopants concentrations. In the proposed mechanism of Fano resonance in a superlattice of quantum wires with coexisting polarons and Fermi particles, the lattice properties play a key role. Here, we report a study of the temperature evolution of the parent compound p-terphenyl crystal structure proposed to be made of a self-assembled supramolecular network of nanoscale nanoribbons. Using temperature-dependent synchrotron X-ray diffraction, we report the anisotropic thermal expansion in the ab plane, which supports the presence of a nanoscale network of one-dimensional nanoribbons running in the b-axis direction in the P21/a structure. Below the enantiotropic phase transition at 193 K, the order parameter of the C-1 structure follows a power law behavior with the critical exponent α =?0.34 ± 0.02 and the thermal expansion of the a-axis and b-axis show major changes supporting the formation of a two-dimensional bonds network. The large temperature range of the orientation fluctuations in a double well potential of the central phenyl has been determined.     

Biocompatible glass–ceramic materials for bone substitution

A new bioactive glass composition (CEL2) in the SiO₂–P₂O₅–CaO–MgO–K₂O–Na₂O system was tailored to control pH variations due to ion leaching phenomena when the glass is in contact with physiological fluids. CEL2 was prepared by a traditional melting-quenching process obtaining slices that were heat-treated to obtain a glass-ceramic material (CEL2GC) that was characterized thorough SEM analysis. Pre-treatment of CEL2GC with SBF was found to enhance its biocompatibility, as assessed by in vitro tests. CEL2 powder was then used to synthesize macroporous glass–ceramic scaffolds. To this end, CEL2 powders were mixed with polyethylene particles within the 300–600 μm size-range and then pressed to obtain crack-free compacted powders (green). This was heat-treated to remove the organic phase and to sinter the inorganic phase, leaving a porous structure. The biomaterial thus obtained was characterized by X-ray diffraction, SEM equipped with EDS, density measurement, image analysis, mechanical testing and in vitro evaluation, and found to be a glass–ceramic macroporous scaffold with uniformly distributed and highly interconnected porosity. The extent and size-range of the porosity can be tailored by varying the amount and size of the polyethylene particles.     

Insights into Mechanisms of Tumorigenesis in Neuroendocrine Neoplasms

Genomic studies have identified some of the most relevant genetic players in Neuroendocrine Neoplasm (NEN) tumorigenesis. However, we are still far from being able to draw a model that encompasses their heterogeneity, elucidates the different biological effects consequent to the identified molecular events, or incorporates extensive knowledge of molecular biomarkers and therapeutic targets. Here, we reviewed recent insights in NEN tumorigenesis from selected basic research studies on animal models, highlighting novel players in the intergenic cooperation and peculiar mechanisms including splicing dysregulation, chromatin stability, or cell dedifferentiation. Furthermore, models of tumorigenesis based on composite interactions other than a linear progression of events are proposed, exemplified by the involvement in NEN tumorigenesis of genes regulating complex functions, such as MEN1 or DAXX. Although limited by interspecies differences, animal models have proved helpful for the more in-depth study of every facet of tumorigenesis, showing that the identification of driver mutations is only one of the many necessary steps and that other mechanisms are worth investigating.     

ADMA/SDMA in Elderly Subjects with Asymptomatic Carotid Atherosclerosis: Values and Site-Specific Association

Asymmetric dimethylarginine (ADMA) is an endogenous nitric oxide synthase (NOS) inhibitor known as a mediator of endothelial dysfunction and atherosclerosis. Circulating ADMA levels are correlated with cardiovascular risk factors such as hypercholesterolemia, arterial hypertension, diabetes mellitus, hyperhomocysteinemia, age and smoking. We assessed the relationship between ADMA values and site-specific association of asymptomatic carotid atherosclerosis (intima-media thickness (CIMT) and plaque) in elderly subjects. One hundred and eighty subjects underwent a complete history and physical examination, determination of serum chemistries and ADMA levels, and carotid ultrasound investigation (CUI). All subjects had no acute or chronic symptoms of carotid atherosclerosis. Statistical analyses showed that high plasma levels of ADMA/SDMA were positively correlated to carotid atherosclerosis (CIMT and plaque) (p < 0.001), with significant site-specific association. Total cholesterol, low density lipoprotein cholesterol, triglycerides and C-reactive protein plasma concentrations were significantly associated with asymptomatic carotid atherosclerosis (p < 0.001). High serum concentrations of ADMA and SDMA were associated with carotid atherosclerotic lesions as measured by CIMT ad plaque and may represent a new marker of asymptomatic carotid atherosclerosis in elderly subjects.     

The Functional Characteristics of Goat Cheese Microbiota from a One-Health Perspective

Goat cheese is an important element of the Mediterranean diet, appreciated for its health-promoting features and unique taste. A pivotal role in the development of these characteristics is attributed to the microbiota and its continuous remodeling over space and time. Nevertheless, no thorough study of the cheese-associated microbiota using two metaomics approaches has previously been conducted. Here, we employed 16S rRNA gene sequencing and metaproteomics to explore the microbiota of a typical raw goat milk cheese at various ripening timepoints and depths of the cheese wheel. The 16S rRNA gene-sequencing and metaproteomics results described a stable microbiota ecology across the selected ripening timepoints, providing evidence for the microbiologically driven fermentation of goat milk products. The important features of the microbiota harbored on the surface and in the core of the cheese mass were highlighted in both compositional and functional terms. We observed the rind microbiota struggling to maintain the biosafety of the cheese through competition mechanisms and/or by preventing the colonization of the cheese by pathobionts of animal or environmental origin. The core microbiota was focused on other biochemical processes, supporting its role in the development of both the health benefits and the pleasant gustatory nuances of goat cheese.     

Functional and Taxonomic Traits of the Gut Microbiota in Type 1 Diabetes Children at the Onset: A Metaproteomic Study

Type 1 diabetes (T1D) is a chronic autoimmune metabolic disorder with onset in pediatric/adolescent age, characterized by insufficient insulin production, due to a progressive destruction of pancreatic β-cells. Evidence on the correlation between the human gut microbiota (GM) composition and T1D insurgence has been recently reported. In particular, 16S rRNA-based metagenomics has been intensively employed in the last decade in a number of investigations focused on GM representation in relation to a pre-disease state or to a response to clinical treatments. On the other hand, few works have been published using alternative functional omics, which is more suitable to provide a different interpretation of such a relationship. In this work, we pursued a comprehensive metaproteomic investigation on T1D children compared with a group of siblings (SIBL) and a reference control group (CTRL) composed of aged matched healthy subjects, with the aim of finding features in the T1D patients’ GM to be related with the onset of the disease. Modulated metaproteins were found either by comparing T1D with CTRL and SIBL or by stratifying T1D by insulin need (IN), as a proxy of β-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity.