Social environment elicits lateralized behaviors in Gorillas (Gorilla gorilla gorilla) and Chimpanzees (Pan troglodytes)

The influence of the social environment on lateralized behaviors has now been investigated across a wide variety of animal species. New evidence suggests that the social environment can modulate behavior. Currently, there is a paucity of data relating to how primates navigate their environmental space, and investigations that consider the naturalistic context of the individual are few and fragmented. Moreover, there are competing theories about whether only the right or rather both cerebral hemispheres are involved in the processing of social stimuli, especially in emotion processing. Here we provide the first report of lateralized social behaviors elicited by great apes. We employed a continuous focal animal sampling method to record the spontaneous interactions of a captive zoo-living colony of chimpanzees (Pan troglodytes) and a biological family group of peer-reared western lowland gorillas (Gorilla gorilla gorilla). We specifically focused on which side of the body (i.e., front, rear, left, right) the focal individual preferred to keep conspecifics. Utilizing a newly developed quantitative corpus-coding scheme, analysis revealed both chimpanzees and gorillas demonstrated a significant group-level prefer- ence for focal individuals to keep conspecifics positioned to the front of them compared with behind them. More interestingly, both groups also manifested a population-level bias to keep conspecifics on their left side compared with their right side. Our findings suggest a social processing dominance of the right hemisphere for context-specific social environments. Results are discussed in light of the evolu- tionary adaptive value of social stimulus as a triggering factor for the manifestation of group-level lateralized behaviors

Data Quality Dimensions for Fair AI

Artificial Intelligence (AI) systems are not intrinsically neutral and biases trickle in any type of tech- nological tool. In particular when dealing with people, the impact of AI algorithms’ technical errors originating with mislabeled data is undeniable. As they feed wrong and discriminatory classifications, these systems are not systematically guarded against bias. In this article we consider the problem of bias in AI systems from the point of view of data quality dimensions. We highlight the limited model construction of bias mitigation tools based on accuracy strategy, illustrating potential improvements of a specific tool in gender classification errors occurring in two typically difficult contexts: the classification of non-binary individuals, for which the label set becomes incomplete with respect to the dataset; and the classification of transgender individuals, for which the dataset becomes inconsistent with respect to the label set. Using formal methods for reasoning about the behavior of the classification system in presence of a changing world, we propose to reconsider the fairness of the classification task in terms of completeness, consistency, timeliness and reliability, and offer some theoretical results

The epistemic dimension of algorithmic fairness: assessing its impact in innovation diffusion and fair policy making

Algorithmic fairness is an expanding field that addresses a range of discrimination issues associated with algorithmic processes. However, most works in the literature focus on analyzing it only from an ethical perspective, focusing on moral principles and values that should be considered in the design and evaluation of algorithms, while disregarding the epistemic dimension related to knowledge transmission and validation. However, this aspect of algorithmic fairness should also be included in the debate, as it is crucial to introduce a specific type of harm: an individual may be systematically excluded from the dissemination of knowledge due to the attribution of a credibility deficit/excess. In this work, we specifically focus on characterizing and analyzing the impact of this credibility deficit or excess on the diffusion of innovations on a societal scale, a phenomenon driven by individual attitudes and social interactions, and also by the strength of mutual connections. Indeed, discrimination might shape the latter, ultimately modifying how innovations spread within the network. In this light, to incorporate, also from a formal point of view, the epistemic dimension in innovation diffusion models becomes paramount, especially if these models are intended to support fair policy design. For these reasons, we formalize the epistemic properties of a social environment, by extending the well-established Linear Threshold Model (LTM) in an epistemic direction to show the impact of epistemic biases in innovation diffusion. Focusing on the impact of epistemic bias in both open-loop and closed-loop scenarios featuring optimal fostering policies, our results shed light on the pivotal role the epistemic dimension might have in the debate of algorithmic fairness in decision-making

Qualification and Quantification of Fairness for Sustainable Mobility Policies

The adoption of new mobility technologies on a large-scale plays a crucial role to promote a green transition in the mobility field. Nonetheless, the acceptance of new mobility solutions implies radical changes in the everyday lives of individuals and, thus, it can be hampered by many different factors besides transport habits, such as socio-economic individual features. For this reason, it is essential to design human-centered policies directly addressing such barriers, avoiding the unwanted effect of amplifying inequalities at the edges of society. To this end, we propose a data-driven approach to embed socio-economic factors in the design of new mobility strategies that quantitatively account for fairness in a control-oriented and dynamic fashion. The formalization (and the inclusion in the approach) of the concepts of doxastic equality and equity allows us to mitigate epistemic exclusions, assessing system fairness. Thus, by combining tools from the control framework with those of philosophy, our approach offers an actionable tool for the support of the design of fair policies to foster the adoption of sustainable mobility habits.</p

Qualification and Quantification of Fairness for Sustainable Mobility Policies

The adoption of new mobility technologies on a large-scale plays a crucial role to promote a green transition in the mobility field. Nonetheless, the acceptance of new mobility solutions implies radical changes in the everyday lives of individuals and, thus, it can be hampered by many different factors besides transport habits, such as socio-economic individual features. For this reason, it is essential to design human-centered policies directly addressing such barriers, avoiding the unwanted effect of amplifying inequalities at the edges of society. To this end, we propose a data-driven approach to embed socio-economic factors in the design of new mobility strategies that quantitatively account for fairness in a control-oriented and dynamic fashion. The formalization (and the inclusion in the approach) of the concepts of doxastic equality and equity allows us to mitigate epistemic exclusions, assessing system fairness. Thus, by combining tools from the control framework with those of philosophy, our approach offers an actionable tool for the support of the design of fair policies to foster the adoption of sustainable mobility habits.</p

DAXX mutations as potential genomic markers of malignant evolution in small nonfunctioning pancreatic neuroendocrine tumors

Management of localized well-differentiated pancreatic neuroendocrine tumors (panNETs) is controversial and primarily dependent on tumor size. Upfront surgery is usually recommended for tumors larger than 2 cm in diameter since they frequently show metastatic potential, whereas smaller panNETs are generally characterized by an indolent clinical course, with a rate of relapse or metastasis below 15%. To explore whether increased tumor size is paralleled by genomic variations, we compared the rate and the mutational patterns of putative driver genes that are recurrently altered in these tumors by investigating differential cohorts of panNET surgical specimens smaller (n = 27) or larger than 2 cm (n = 29). We found that the cumulative number of mutations detected in panNETs &gt;2 cm was significantly higher (p = 0.03) relative to smaller tumors, while mutations of DAXX were significantly more frequent in the cohort of larger tumors (p = 0.05). Moreover, mutations of DAXX were associated with features of malignancy including increased grade, nodal involvement and lymphovascular invasion, and independently predicted both relapse after surgery (p = 0.05) and reduced DFS in multivariable analysis (p = 0.02). Our data suggest that alterations of the DAXX/ATRX molecular machinery increase the malignant potential of panNETs, and that identification of mutations of DAXX/ATRX in small, nonfunctioning tumors can predict the malignant progression observed in a minority of them

Rare histotypes of epithelial biliary tract tumors: A literature review

Adenocarcinoma represents the most frequent biliary tract cancer. However, other rare histotypes can be found in the biliary tract, such as cholangiolocellular carcinoma, cholangiocarcinoma with ductal plate malformation pattern, adenosquamous carcinoma, mucinous carcinoma, signet ring cell carcinoma, clear cell carcinoma, mucoepidermoid carcinoma, lymphoepithelioma-like carcinoma, and sarcomatous cholangiocarcinoma. These cancer types account for less than 10 % of all the already rare biliary tract tumors. Yet, they represent a relevant issue in everyday clinical practice, given the lack of therapeutic recommendations and the overall scarcity of data, mainly deriving from isolated small center-specific cohorts of patients.The shifts of such histotypes from the most common ones reflect genetic and molecular differences, determine changes in clinical aggressiveness, and suggest a possible variability in sensitivity to the standard treatments of biliary adenocarcinomas. The consistency and degree of these variables are still to be solidly demonstrated and investigated. Therefore, this paper aims to review the current literature concerning very infrequent and rare epithelial biliary tract cancers, focusing our attention on the clinical, molecular, and immunohistochemical features of these tumors

Basal and one-month differed neutrophil, lymphocyte and platelet values and their ratios strongly predict the efficacy of checkpoint inhibitors immunotherapy in patients with advanced BRAF wild-type melanoma

Background To evaluate the capability of basal and one-month differed white blood cells (WBC), neutrophil, lymphocyte and platelet values and their ratios (neutrophils-to-lymphocytes ratio, NLR, and platelets-to-lymphocytes ratio, PLR) in predicting the response to immune checkpoint inhibitors (ICI) in metastatic melanoma (MM). Methods We performed a retrospective study of 272 BRAF wild-type MM patients treated with first line ICI. Bivariable analysis was used to correlate patient/tumor characteristics with clinical outcomes. Variations between time 1 and time 0 (Delta) of blood parameters were also calculated and dichotomized using cut-off values assessed by ROC curve. Results At baseline, higher neutrophils and NLR negatively correlated with PFS, OS and disease control rate (DCR). Higher PLR was also associated with worse OS. In multivariable analysis, neutrophils (p = 0.003), WBC (p = 0.069) and LDH (p = 0.07) maintained their impact on PFS, while OS was affected by LDH (p &lt; 0.001), neutrophils (p &lt; 0.001) and PLR (p = 0.022), while DCR by LDH (p = 0.03) and neutrophils (p = 0.004). In the longitudinal analysis, PFS negatively correlated with higher Delta platelets (p = 0.039), Delta WBC (p &lt; 0.001), and Delta neutrophils (p = 0.020), and with lower Delta lymphocytes (p &lt; 0.001). Moreover, higher Delta NLR and Delta PLR identified patients with worse PFS, OS and DCR. In the multivariable model, only Delta NLR influenced PFS (p = 0.004), while OS resulted affected by higher Delta WBC (p &lt; 0.001) and lower Delta lymphocytes (p = 0.038). Higher Delta WBC also affected the DCR (p = 0.003). When clustering patients in 4 categories using basal LDH and Delta NLR, normal LDH/lower Delta NLR showed a higher PFS than high LDH/higher Delta NLR (20 vs 5 months). Moreover, normal LDH/higher Delta lymphocytes had a higher OS than high LDH/lower Delta lymphocytes (50 vs. 10 months). Conclusions Baseline and early variations of blood cells, together with basal LDH, strongly predict the efficacy of ICI in MM. Our findings propose simple, inexpensive biomarkers for a better selection of patient treatments. Prospective multicenter studies are warranted to confirm these data. © 2022, The Author(s)