Investigating the impact of greenery elements in office environments on cognitive performance, visual attention and distraction: An eye-tracking pilot-study in virtual reality

The human-nature connection is one of the main aspects determining supportive and comfortable office environments. In this context, the application of eye-tracking-equipped Virtual Reality (VR) devices to support an evaluation on the effect of greenery elements indoors on individuals' efficiency and engagement is limited. A new approach to investigate visual attention, distraction, cognitive load and performance in this field is carried out via a pilot-study comparing three virtual office layouts (Indoor Green, Outdoor Green and Non-Biophilic). 63 participants completed cognitive tasks and surveys while measuring gaze behaviour. Sense of presence, immersivity and cybersickness results supported the ecological validity of VR. Visual attention was positively influenced by the proximity of users to the greenery element, while visual distraction from tasks was negatively influenced by the dimension of the greenery. In the presence of greenery elements, lower cognitive loads and more efficient information searching, resulting in improved performance, were also highlighted.

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis

One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager , is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities.

Sustainable and resilient strategies for touristic cities against COVID-19: An agent-based approach

Touristic cities will suffer from COVID-19 emergency because of its economic impact on their communities. The first emergency phases involved a wide closure of such areas to support "social distancing" measures (i.e. travels limitation; lockdown of (over)crowd-prone activities). In the "second phase", individual's risk-mitigation strategies (facial masks) could be properly linked to "social distancing" to ensure re-opening touristic cities to visitors. Simulation tools could support the effectiveness evaluation of risk-mitigation measures to look for an economic and social optimum for activities restarting. This work modifies an existing Agent-Based Model to estimate the virus spreading in touristic areas, including tourists and residents' behaviours, movement and virus effects on them according to a probabilistic approach. Consolidated proximity-based and exposure-time-based contagion spreading rules are included according to international health organizations and previous calibration through experimental data. Effects of tourists' capacity (as "social distancing"-based measure) and other strategies (i.e. facial mask implementation) are evaluated depending on virus-related conditions (i.e. initial infector percentages). An idealized scenario representing a significant case study has been analysed to demonstrate the tool capabilities and compare the effectiveness of those solutions. Results show that "social distancing" seems to be more effective at the highest infectors' rates, although represents an extreme measure with important economic effects. This measure loses its full effectiveness (on the community) as the infectors' rate decreases and individuals' protection measures become predominant (facial masks). The model could be integrated to consider other recurring issues on tourist-related fruition and schedule of urban spaces and facilities (e.g. cultural/leisure buildings).

Machine Learning (ML) based-method applied in recurrent pregnancy loss (RPL) patients diagnostic work-up: a potential innovation in common clinical practice

RPL is a very debated condition, in which many issues concerning definition, etiological factors to investigate or therapies to apply are still controversial. ML could help clinicians to reach an objectiveness in RPL classification and access to care. Our aim was to stratify RPL patients in different risk classes by applying an ML algorithm, through a diagnostic work-up to validate it for the appropriate prognosis and potential therapeutic approach. 734 patients were enrolled and divided into 4 risk classes, according to the numbers of miscarriages. ML method, called Support Vector Machine (SVM), was used to analyze data. Using the whole set of 43 features and the set of the most informative 18 features we obtained comparable results: respectively 81.86 ± 0.35% and 81.71 ± 0.37% Unbalanced Accuracy. Applying the same method, introducing the only features recommended by ESHRE, a correct classification was obtained only in 58.52 ± 0.58%. ML approach could provide a Support Decision System tool to stratify RPL patients and address them objectively to the proper clinical management.

Sociability and gazing toward humans in dogs and wolves: Simple behaviors with broad implications

Sociability, defined as the tendency to approach and interact with unfamiliar people, has been found to modulate some communicative responses in domestic dogs, including gaze behavior toward the human face. The objective of this study was to compare sociability and gaze behavior in pet domestic dogs and in human-socialized captive wolves in order to identify the relative influence of domestication and learning in the development of the dog-human bond. In Experiment 1, we assessed the approach behavior and social tendencies of dogs and wolves to a familiar and an unfamiliar person. In Experiment 2, we compared the animal's duration of gaze toward a person's face in the presence of food, which the animals could see but not access. Dogs showed higher levels of interspecific sociability than wolves in all conditions, including those where attention was unavailable. In addition, dogs gazed longer at the person's face than wolves in the presence of out-of-reach food. The potential contributions of domestication, associative learning, and experiences during ontogeny to prosocial behavior toward humans are discussed.

[Delirium and psychotropic substance abuse]

Delirium represents a common symptom in psychiatric diseases such as psychotic and affective disorders, organic illnesses and psychotropic substance abuse. The literature shows a high risk of developing psychosis in psychotropic substance abusers and a higher proneness to psychotropic substance abuse in people suffering from psychosis. The aim of this review is to discuss the relationship between substance abuse (cocaine, amphetamine, alcohol, cannabinoids, opioids, etc.) and the development of delirium, the way each molecule influences the pathogenesis of delirium, the neurochemical basis of delirium induced by psychotropic substances and the potential endophenotypes involved in a biologically plausible mechanism of mental diseases' pathogenesis.

[The psychopathology of delirium. Evolution of the research from Jaspers to Kapur]

Delirium represents a symptom of a thought disorder in which a belief is strongly held in spite of invalidating evidence. Many varieties of delirium have been described and this symptom is common in psychotic disorders (schizophrenia, brief psychotic disorder, chronic delusional disorders, etc.), affective disorders (major depressive disorder, psychotic depression, melancholic depression, bipolar disorder, etc.), organic illnesses and psychotropic substance abuse. Delirium has been deeply studied by psychopathology. Our work offers an overview of the most relevant psychopathological descriptions of delirium in the last century, from Jaspers to Kapur.

[Teaching how to ask for help: social sensitiveness and early intervention in psychosis]

Psychosis is the most traumatic mental illness influencing both sufferers and their families' quality of life, because of the symptoms and the social stigma. A delay in the recognition of fi rst episode psychosis is unfortunately common being often associated with social functioning decline and worse prognosis. Early detection and intervention could potentially alter the course of this serious illness. We reviewed evidence from the literature on the most recent examples of early intervention in psychosis and on its effectiveness in delaying transition to psychosis, reducing the duration of untreated psychosis, the admission rates, the suicide rates, the treatment costs, as well as preventing relapses and improving the short and long-term symptomatic and functional outcome. The morbidity and mortality associated with schizophrenia spectrum may be improved by a multidisciplinary approach, involving the School, the Primary care and the Information in order to detect as soon as possible the prodromal feelings associated with early psycho-sis. General practitioners are indeed ideally placed to identify mental and emotional changes in the emerging psychotic illnesses and could bridge the gap between specialist mental health services, patients and their families. These are the reasons why mental health should be demanding commitment for both psychiatrists and primary care.

The dimeric assembly of Photobacterium leiognathi and Salmonella typhimurium SodC1 Cu,Zn superoxide dismutases is affected differently by active site demetallation and pH: an analytical ultracentrifuge study

To establish whether the species-specific variations at the subunit interface of bacterial Cu,Zn superoxide dismutases affect dimer assembly, the association state of the Photobacterium leiognathi (PlSOD) and Salmonella typhimurium (StSOD) enzymes, which differ in 11 out of 19 interface residues, was investigated by analytical ultracentrifugation. The same linkage pattern correlates quaternary assembly, active site metallation, and pH in the two enzymes albeit with quantitative differences. Both holo-enzymes are stable dimers at pH 6.8 and 8.0, although their shape is altered at alkaline pH. In contrast, dimer stability is affected differently by metal removal. Thus, apo-StSOD is a stable dimer at pH 6.8 whereas apo-PlSOD is in reversible monomer-dimer equilibrium. In both apoproteins a pH increase to 8.0 favors monomerization. These effects prove the existence of long-range communication between the active site and the subunit interface and provide a structural explanation for the known functional differences between the two enzymes.

Single mutations at the subunit interface modulate copper reactivity in Photobacterium leiognathi Cu,Zn superoxide dismutase

The functional properties and X-ray structures of five mutant forms of Photobacterium leiognathi Cu,Zn superoxide dismutase carrying single mutations at residues located at the dimer association interface have been investigated. When compared to the wild-type enzyme, the three-dimensional structures of the mutants show structural perturbations limited to the proximity of the mutation sites and substantial identity of active site geometry. Nonetheless, the catalytic rates of all mutants, measured at neutral pH and low ionic strength by pulse radiolysis, are higher than that of the wild-type protein. Such enzymatic activity increase is paralleled by enhanced active site accessibility to external chelating agents, which, in the mutated enzyme, remove more readily the active site copper ion. It is concluded that mutations at the prokaryotic Cu,Zn superoxide dismutase subunit interface can transduce dynamical perturbation to the active site region, promoting substrate active site accessibility. Such long-range intramolecular communication effects have not been extensively described before within the Cu,Zn superoxide dismutase homology family.

Single mutation induces a metal-dependent subunit association in dimeric Cu,Zn superoxide dismutase

Tryptophan 83, a residue strongly involved in the intersubunit interaction of the Cu,Zn superoxide dismutases from Photobacterium leiognathi, has been selectively mutated to phenylalanine or tyrosine. The recombinant mutant enzymes expressed in Escherichia coli were purified in two well distinct and stable forms, one dimeric and fully active and the other monomeric and devoid of metals. In agreement, in vitro experiments indicate that the removal and addition of zinc in the mutant enzymes induces monomerization and dimerization, respectively, while does not perturb the dimeric association of the native protein. This is the first unambiguous experimental proof of a direct communication between the intersubunit interface and the metal active site.