The Implementation of the Global Minimum Tax (GloBE): The Need for an Effective Dispute Prevention and Resolution Mechanism

The successful implementation of the Global Anti-Base Erosion (GloBE) rules on aglobal scale cannot be achieved without an international effective dispute prevention and reso-lution mechanism. However, the development of a dispute prevention and resolution frameworkfor the GloBE rules faces significant challenges. This article offers two possible options for aneffective dispute prevention and resolution mechanism: a model based on reciprocal domesticlegislations and the multilateral convention model

Caching mechanisms for habit formation in Active Inference

A popular distinction in the human and animal learning literature is between deliberate (or willed) and habitual (or automatic) modes of control. Extensive evidence indicates that, after sufficient learning, living organisms develop behavioural habits that permit them saving computational resources. Furthermore, humans and other animals are able to transfer control from deliberate to habitual modes (and vice versa), trading off efficiently flexibility and parsimony – an ability that is currently unparalleled by artificial control systems. Here, we discuss a computational implementation of habit formation, and the transfer of control from deliberate to habitual modes (and vice versa) within Active Inference: a computational framework that merges aspects of cybernetic theory and of Bayesian inference. To model habit formation, we endow an Active Inference agent with a mechanism to “cache” (or memorize) policy probabilities from previous trials, and reuse them to skip – in part or in full – the inferential steps of deliberative processing. We exploit the fact that the relative quality of policies, conditioned upon hidden states, is constant over trials; provided that contingencies and prior preferences do not change. This means the only quantity that can change policy selection is the prior distribution over the initial state – where this prior is based upon the posterior beliefs from previous trials. Thus, an agent that caches the quality (or the probability) of policies can safely reuse cached values to save on cognitive and computational resources – unless contingencies change. Our simulations illustrate the computational benefits, but also the limits, of three caching schemes under Active Inference. They suggest that key aspects of habitual behaviour – such as perseveration – can be explained in terms of caching policy probabilities. Furthermore, they suggest that there may be many kinds (or stages) of habitual behaviour, each associated with a different caching scheme; for example, caching associated or not associated with contextual estimation. These schemes are more or less impervious to contextual and contingency changes

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56(bright) and mature cytotoxic NK CD56(dim). Therefore, the ability to discriminate CD56(dim) from CD56(bright) could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56(bright) to CD56(dim). We trained our ML algorithm with sorted NK cell subclasses (>= 86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56(bright) to CD56(dim) and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

Rhus coriaria L. in tradition and innovation like natural dye

: Nowadays, secondary raw materials (SRM) obtained from plant matrices are of great interest for circular economy, suitable for sustainable measures to reduce environmental impact. This work focused on the extraction, characterization and quantification of compounds obtained from leaves and fruits of the Sicilian sumac, Rhus coriaria L. and their application as natural dyes on textile fibres. Extractions were performed with Extractor Naviglio®, maceration and ultrasound assisted methods and food-grade solvents (aqueous and hydroalcoholic) to evaluate the yields for dye compounds. The presence of colouring molecules was evaluated by UV-Vis spectrophotometer, and the extracts selected for colouring were quantified and characterized by LC-MS. The results showed that Extractor Naviglio® achieved the best extraction yield, and the ethanol-water mixture extracts had a higher amount of total phenolic compounds (TPC) and a higher content of total colouring compounds (TCC). These extracts were selected for subsequent applications as dyes for linen, cotton and wool. The chemical profile of selected extracts was rich in compounds such as gallotannin and anthocyanin class. Fibre dyeing was verified by recording CIELAB colouring coordinates. The results suggest that the dyes obtained from R. coriaria can be of great interest for artisanal and industrial processes, in accordance with environmental sustainability

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56bright and mature cytotoxic NK CD56dim. Therefore, the ability to discriminate CD56dim from CD56bright could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56bright to CD56dim. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56bright to CD56dim and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy

Inhibition of Ocular Aldose Reductase by a New Benzofuroxane Derivative Ameliorates Rat Endotoxic Uveitis

The study investigated the effects of the aldose reductase (AR) inhibitor benzofuroxane derivative 5(6)-(benzo[d]thiazol-2-ylmethoxy) benzofuroxane (herein referred to as BF-5m) on the biochemical and tissue alterations induced by endotoxic uveitis in rats. BF-5m has been administered directly into the vitreous, in order to assess the expression and levels of (i) inflammatory markers such as the ocular ubiquitin-proteasome system, NF-κB, TNF-α, and MCP-1; (ii) prooxidant and antioxidant markers such as nitrotyrosine, manganese superoxide dismutase (MnSOD), and glutathione peroxidase (GPX); (iii) apoptotic/antiapoptotic factors caspases and Bcl-xl; (iv) markers of endothelial progenitor cells (EPCs) recruitment such as CD34 and CD117. 5 L of BF-5m (0.01; 0.05; and 0.1 M) into the right eye decreased in a dose-dependent manner the LPS-induced inflammation of the eye, reporting a clinical score 1. It reduced the ocular levels of ubiquitin, 20S and 26S proteasome subunits, NF-κB subunits, TNF-α, MCP-1, and nitrotyrosine. BF-5m ameliorated LPS-induced decrease in levels of MnSOD and GPX. Antiapoptotic effects were seen from BF-5m by monitoring the expression of Bcl-xl, an antiapoptotic protein. Similarly, BF-5m increased recruitment of the EPCs within the eye, as evidenced by CD34 and CD117 antibodies

Alcohol-induced retrograde facilitation renders witnesses of crime less suggestible to misinformation

RATIONALE: Research has shown that alcohol can have both detrimental and facilitating effects on memory: intoxication can lead to poor memory for information encoded after alcohol consumption (anterograde amnesia) and may improve memory for information encoded before consumption (retrograde facilitation). This study examined whether alcohol consumed after witnessing a crime can render individuals less vulnerable to misleading post-event information (misinformation). METHOD: Participants watched a simulated crime video. Thereafter, one third of participants expected and received alcohol (alcohol group), one third did not expect but received alcohol (reverse placebo), and one third did not expect nor receive alcohol (control). After alcohol consumption, participants were exposed to misinformation embedded in a written narrative about the crime. The following day, participants completed a cued-recall questionnaire about the event. RESULTS: Control participants were more likely to report misinformation compared to the alcohol and reverse placebo group. CONCLUSION: The findings suggest that we may oversimplify the effect alcohol has on suggestibility and that sometimes alcohol can have beneficial effects on eyewitness memory by protecting against misleading post-event information