AI IN THE DESIGN PROCESS: TRAINING THE HUMAN-AI COLLABORATION

Artificial intelligence (AI) systems are attracting more and more attention as possible tools to enhance creativity in the design process. However, alongside potentialities, introducing non-human agents in a design team can bring specific criticalities, which need a high level of awareness on the part of designers to be tackled. An exploratory study on the perception of design students regarding the inclusion of AI tools in the early stages of the design process was conducted. The results are discussed in the paper, with a specific focus on the possible role of training in supporting the development of critical awareness regarding the challenges posed by human-AI collaborations

Artificial intelligence in the design process

The book discusses how to include artificial intelligence (AI) systems in the early stages of the design process. Today designers need new tools capable of supporting them in dealing with the increasing project’s complexity and empowering their performances and capabilities. AI systems appear to be powerful means to enhance designers’ creativity. This assumption was tested in a workshop where sixteen participants collaborated with three AI systems throughout the creative phases of research, sketching, and color selection. Results show that designers can access a broader level of variance and inspiration while reducing the risk of fossilization by triggering lateral thinking through AI-generated data. Therefore, AI could significantly impact the creative phases of the design process if applied consciously. Being AI systems intelligent agents, the book treats the Human-AI collaboration as a collaboration between human agents, proposing a set of guidelines helpful to achieving an efficient partnership with the machine

Artificial intelligence in the design process

The book discusses how to include artificial intelligence (AI) systems in the early stages of the design process. Today designers need new tools capable of supporting them in dealing with the increasing project’s complexity and empowering their performances and capabilities. AI systems appear to be powerful means to enhance designers’ creativity. This assumption was tested in a workshop where sixteen participants collaborated with three AI systems throughout the creative phases of research, sketching, and color selection. Results show that designers can access a broader level of variance and inspiration while reducing the risk of fossilization by triggering lateral thinking through AI-generated data. Therefore, AI could significantly impact the creative phases of the design process if applied consciously. Being AI systems intelligent agents, the book treats the Human-AI collaboration as a collaboration between human agents, proposing a set of guidelines helpful to achieving an efficient partnership with the machine

Strategies for Psychiatric Rehabilitation and their Cognitive Outcomes in Schizophrenia: Review of Last Five-year Studies

Background: Cognitive deficits are core features of Schizophrenia, showing poor response to antipsychotic treatment, therefore non-pharmacological rehabilitative approaches to such a symptom domain need to be identified. However, since not all patients with Schizophrenia exhibit the same cognitive impairment profile, individualized rehabilitative approaches should be set up. Objectives: We explored the last five-year literature addressing the issue of cognitive dysfunction response to rehabilitative methodologies in Schizophrenia to identify possible predictors of response and individualized strategies to treat such a dysfunction. Conclusion: A total of 76 studies were reviewed. Possible predictors of cognitive rehabilitation outcome were identified among patient-specific and approachspecific variables and a general overview of rehabilitative strategies used in the last five years has been depicted. Studies suggest the existence of multifaced and multi-domain variables that could significantly predict pro-cognitive effects of cognitive rehabilitation, which could also be useful for identifying individual-specific rehabilitation trajectories over time. An individualized rehabilitative approach to cognitive impairment in Schizophrenia is possible if taking into account both patient and approach specific predictors of outcomes

Tuber borchii Vitt. mycorrhiza protects Cistus creticus L. from heavy metal toxicity

Heavy metals (HMs), such as copper, zinc, lead, mercury and cadmium, are the most abundant and dangerous inorganic environmental pollutants. Growing pieces of evidence suggest that mycorrhizal fungi can alleviate metal toxicity in plants. In this study, we focused attention on the ectomycorrhizal (ECM) fungus Tuber borchii Vitt., which is widespread in Italy and is of great ecological interest because of the mutualistic associations and the advantages it provides to host plants. Seedlings of the Mediterranean shrub Cistus creticus L., mycorrhized and non mycorrhized with the ECM fungus 7: borchii, were treated with HMs (zinc, lead and chromium). HMs induced leaves' chlorosis in non mycorrhized seedlings; while no significant differencewas observed impigmentation of mycorrhized seedlings' leaves. This observation was confirmed by Euclidean Distance of color measurements in L*a*b* units from RGB digital images of leaves. The decrease in leaves pigmentation observed in HM treated non mycorrhized seedlings strongly correlated with a reduced expression of key genes associated with chlorophyll biosynthesis; instead, no significant variation of gene expression was detected in mycorrhized seedlings treated with HMs

Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

Background: Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance. Methods: Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities. Results: The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03). Conclusion: Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability

Costimulatory Pathways in Kidney Transplantation: Pathogenetic Role, Clinical Significance and New Therapeutic Opportunities.

Costimulatory pathways play a key role in immunity, providing the second signal required for a full activation of adaptive immune response. Different costimulatory families (CD28, TNF-related, adhesion and TIM molecules), characterized by structural and functional analogies, have been described. Costimulatory molecules modulate T cell activation, B cell function, Ig production, cytokine release and many other processes, including atherosclerosis. Patients suffering from renal diseases present significant alterations of the costimulatory pathways, which might make them particularly liable to infections. These alterations are further pronounced in patients undergoing kidney transplantation. In these patients, different costimulatory patterns have been related to distinct clinical features. The importance that costimulation has gained during the last years has led to development of several pharmacological approaches to modulate this critical step in the immune activation. Different drugs, mainly monoclonal antibodies targeting various costimulatory molecules (i.e. anti-CD80, CTLA-4 fusion proteins, anti-CD154, anti-CD40, etc.) were designed and tested in both experimental and clinical studies. The results of these studies highlighted some criticisms, but also some promising findings and now costimulatory blockade is considered a suitable strategy, with belatacept (a CTLA-4 fusion protein) being approved as the first costimulatory blocker for use in renal transplantation. In this review, we summarize the current knowledge on costimulatory pathways in the setting of kidney transplantation. We describe the principal costimulatory molecule families, their role and clinical significance in patients undergoing renal transplantation and the new therapeutic approaches that have been developed to modulate the costimulatory pathways

Evidence of an interaction between FXR1 and GSK3β polymorphisms on levels of Negative Symptoms of Schizophrenia and their response to antipsychotics

Introduction: Genome Wide Association Studies (GWAS) have identified several genes associated with schizophrenia (SCZ) and exponentially increased knowledge on the genetic basis of the disease. Additionally, products of GWAS genes interact with neuronal factors coded by genes lacking association, such that this interaction may confer risk for specific phenotypes of this brain disorder. In this regard, FXR1 (Fragile-X mental-retardation-syndrome-related 1) gene has been GWAS associated with SCZ. FXR1 protein is regulated by Glycogen Synthase Kinase-3 (GSK3), which has been implicated in pathophysiology of SCZ and response to Antipsychotics (APs). rs496250 and rs12630592, two eQTLs of FXR1 and GSK3 respectively, interact on emotion stability and amygdala/PFC activity during emotion processing. These two phenotypes are associated with Negative Symptoms (NS) of SCZ suggesting that the interaction between these SNPs may also affect NS severity and responsiveness to medication. Methods: To test this hypothesis, in two independent samples of patients with SCZ, we investigated rs496250 by rs12630592 interaction on NS severity and response to APs. We also tested a putative link between APs administration and fxr1 expression, as already reported for GSK3 expression. Results: We found that rs496250 and rs12630592 interact on NS severity. We also found evidence suggesting interaction of these polymorphisms also on response to APs. This interaction was not present when looking at positive and general psychopathology scores. Furthermore, chronic olanzapine administration led to a reduction of FXR1 expression in mouse frontal cortex. Discussion: Our findings suggest that, like GSK3 , FXR1 is affected by APs while shedding new light on the role of the FXR1/GSK3 pathway for NS of SCZ

Familial Risk and a Genome-Wide Supported DRD2 Variant for Schizophrenia Predict Lateral Prefrontal-Amygdala Effective Connectivity During Emotion Processing

The brain functional mechanisms translating genetic risk into emotional symptoms in schizophrenia (SCZ) may include abnormal functional integration between areas key for emotion processing, such as the amygdala and the lateral prefrontal cortex (LPFC). Indeed, investigation of these mechanisms is also complicated by emotion processing comprising different subcomponents and by disease-associated state variables. Here, our aim was to investigate the relationship between risk for SCZ and effective connectivity between the amygdala and the LPFC during different subcomponents of emotion processing. Thus, we first characterized with dynamic causal modeling (DCM) physiological patterns of LPFC amygdala effective connectivity in healthy controls (HC) during implicit and explicit emotion processing. Then, we compared DCM patterns in a subsample of HC, in patients with SCZ and in healthy siblings of patients (SIB), matched for demographics. Finally, we investigated in HC association of LPFC amygdala effective connectivity with a genome-wide supported variant increasing genetic risk for SCZ and possibly relevant to emotion processing (DRD2 rs2514218). In HC, we found that a "bottom-up" amygdala-to-LPFC pattern during implicit processing and a "top-down" LPFC-to-amygdala pattern during explicit processing were the most likely directional models of effective connectivity. Differently, implicit emotion processing in SIB, SCZ, and HC homozygous for the SCZ risk rs2514218 C allele was associated with decreased probability for the "bottom-up" as well as with increased probability for the "top-down" model. These findings suggest that task-specific anomaly in the directional flow of information or disconnection between the amygdala and the LPFC is a good candidate endophenotype of SCZ.Peer reviewe