Annual 30-m maps of global grassland class and extent (2000-2022) based on spatiotemporal Machine Learning.

The paper describes the production and evaluation of global grassland extent mapped annually for 2000-2022 at 30 m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3 M reference samples (visually interpreted in Very High Resolution imagery).

A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution.

Processing large collections of earth observation (EO) time-series, often petabyte-sized, such as NASA's Landsat and ESA's Sentinel missions, can be computationally prohibitive and costly. Despite their name, even the Analysis Ready Data (ARD) versions of such collections can rarely be used as direct input for modeling because of cloud presence and/or prohibitive storage size. Existing solutions for readily using these data are not openly available, are poor in performance, or lack flexibility.

Serial killer and necrophilia: Report of twenty-five years of treatment and management in a rare case.

The authors report a case of a sexual serial killer responsible for four homicides (one homosexual and three prostitutes) over a 12-year period. The perpetrator was diagnosed with a severe personality disorder and necrophilia at the time of the crimes and was declared partially mentally impaired and dangerous to societal security by the Court of Jurisdiction for these crimes. The offender served 22 years in prison, including half of the detention in an OPG (High Security Forensic Psychiatry Hospital) receiving psychiatric treatment.

Homicides by offenders with psychotic illness in Italy and Turkiye: A comparison of offender and crime-scene profiles.

Objective: The primary aim of this study was to conduct a comparative analysis of homicide cases and their perpetrators with psychotic illnesses in Italy and Turkiye, identifying the extent to which country-specific factors influence offender profiles and crime-scene characteristics.

Method: This cross-national chart review study recruited individuals with psychotic illnesses from forensic psychiatric centers in Italy and Turkiye who were referred for criminal responsibility assessments. Data were collected on the offenders' background characteristics, psychiatric history, victim profiles, and crime-scene details.

Italian Evaluation and Excellence in REMS (ITAL-EE-REMS): appropriate placement of forensic patients in REMS forensic facilities.

Background: We set out to assess the appropriateness of current placement of mentally disordered offenders allocated by the courts in Italy to REMS or to forensic community residences. We hypothesised that as in other countries, the match between a standardised assessment and the decision of the court would be imperfect.

Methods: The DUNDRUM Toolkit was translated into Italian.

Efficacy of cognitive training on cognition, symptoms and functioning: Impact of motivation and attendance.

While cognitive remediation therapy (CRT) and compensatory strategy training both have large literature bases supporting their efficacy on both proximal and distal outcomes, the research base on stand-alone cognitive training (CT) is smaller and less consistent, with little information about factors associated with better outcomes. In this study, we examined the efficacy of CT on training task, cognitive, symptom, and functional ability measures as well as the impact of motivational interviewing (MI), motivation level, and session attendance on treatment outcomes. Adults with psychotic spectrum disorders ( = 114) were randomized to MI or a sham control interview (CI), followed by 4 months of computerized CT.

Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolution.

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials.

Excellence in forensic psychiatry services: international survey of qualities and correlates.

Background: Excellence is that quality that drives continuously improving outcomes for patients. Excellence must be measurable. We set out to measure excellence in forensic mental health services according to four levels of organisation and complexity (basic, standard, progressive and excellent) across seven domains: values and rights; clinical organisation; consistency; timescale; specialisation; routine outcome measures; research and development.

Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning.

This dataset presents global soil organic carbon stocks in mangrove forests at 30 m resolution, predicted for 2020. We used spatiotemporal ensemble machine learning to produce predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. This was done by using training data points of both SOC (%) and BD in mangroves from a global dataset and from recently published studies, and globally consistent predictive covariate layers.

Development of a Website for a Living Network Meta-analysis of Atopic Dermatitis Treatments Using a User-Centered Design: Multimethod Study.

Background: A rapid expansion of systemic immunological treatment options for atopic dermatitis (AD) has created a need for clinically relevant and understandable comparative efficacy and safety information for patients and clinicians. Given the scarcity of head-to-head trials, network meta-analysis (NMA) is an alternative way to enable robust comparisons among treatment options; however, NMA results are often complex and difficult to directly implement in shared decision-making.

Objective: The aim of this study is to develop a website that effectively presents the results of a living systematic review and NMA on AD treatments to patient and clinician users.

Between psychopathy and deviant socialization: A close look at the mafia men.

The Italian mafia organizations represent a subculture with values, beliefs and goals that are antithetical to and undermining of the predominant society. The conduct of individual members includes such extreme violence for material gain, it may at least superficially suggest a severe personality disorder. Since the first edition of the DSM and into the 21st century, various terms have been used, sometimes interchangeably, but over time inconsistently, to designate the mentality and practices of mafia members.

Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation.

The global potential distribution of biomes (natural vegetation) was modelled using 8,959 training points from the BIOME 6000 dataset and a stack of 72 environmental covariates representing terrain and the current climatic conditions based on historical long term averages (1979-2013). An ensemble machine learning model based on stacked regularization was used, with multinomial logistic regression as the meta-learner and spatial blocking (100 km) to deal with spatial autocorrelation of the training points. Results of spatial cross-validation for the BIOME 6000 classes show an overall accuracy of 0.

Ecodatacube.eu: analysis-ready open environmental data cube for Europe.

The article describes the production steps and accuracy assessment of an analysis-ready, open-access European data cube consisting of 2000-2020+ Landsat data, 2017-2021+ Sentinel-2 data and a 30 m resolution digital terrain model (DTM). The main purpose of the data cube is to make annual continental-scale spatiotemporal machine learning tasks accessible to a wider user base by providing a spatially and temporally consistent multidimensional feature space. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values.

Decentralized study of COVID Vaccine Antibody Response (STOPCoV): Results of a participant satisfaction survey.

The Covid-19 pandemic required many clinical trials to adopt a decentralized framework to continue research activities during lock down restrictions. The STOPCoV study was designed to assess the safety and efficacy of Covid-19 vaccines in those aged 70 and above compared to those aged 30-50 years of age. In this sub-study we aimed to determine participant satisfaction for the decentralized processes, accessing the study website and collecting and submitting study specimens.

COVID-19 vaccine antibody responses in community-dwelling adults to 48 weeks post primary vaccine series.

We report a decentralized prospective cohort study of self-reported adverse events and antibody responses to COVID vaccines derived from dried blood spots. Data are presented for 911 older (aged >70 years) and 375 younger (30-50 years) recruits to 48 weeks after the primary vaccine series. After a single vaccine, 83% younger and 45% older participants had overall seropositivity (p < 0.

Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal machine learning.

This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species ( Mill., Mill., L.

A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000-2019) based on LUCAS, CORINE and GLAD Landsat.

A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner.

Reform of Forensic Mental Health Services in Italy: Stigma and Blaming the Messenger: Hermenoia.

About 40 years after the reforms leading to the closure of psychiatric hospitals (Ospedale Psichiatrico [OP]) in Italy in favor of a widespread model with a strong rehabilitation emphasis, Italy has chosen to close High Security Hospitals as well (Ospedale Psichiatrico Giudiziario [OPG]). The new forensic treatment model is expected to be more respectful of the person, including the perpetrators of violent crimes, and aims to be less stigmatizing and more rehabilitative. Despite the favorable premises of the reform (Law n.

Motivational interviewing to enhance psychosocial treatment attendance in people with SMI.

Background: While there is increasing support for the efficacy of psychosocial interventions for people with SMI, the real-world effectiveness of such treatments is diminished by lack of motivation for treatment, leading to poor treatment engagement/dropout. We sought to evaluate the efficacy of motivational interviewing (MI) in improving attendance in a full course of cognitive training, examine motivation level as a potential mechanism of action, and examine variables associated with initial engagement in the training.

Methods: One hundred fourteen participants with SMI were randomized to MI or sham control interview (CI), both of which were followed by a 4-month active phase during which participants could attend up to 50 unpaid cognitive training sessions.

Image-Based Monitoring of Cracks: Effectiveness Analysis of an Open-Source Machine Learning-Assisted Procedure.

The proper inspection of a cracks pattern over time is a critical diagnosis step to provide a thorough knowledge of the health state of a structure. When monitoring cracks propagating on a planar surface, adopting a single-image-based approach is a more convenient (costly and logistically) solution compared to subjective operators-based solutions. Machine learning (ML)- based monitoring solutions offer the advantage of automation in crack detection; however, complex and time-consuming training must be carried out.

Toward a unified account of nonsymbolic and symbolic representations of number: Insights from a combined psychophysical-computational approach.

There is an ongoing, vibrant debate about whether numerical information in both nonsymbolic and symbolic notations would be supported by different neurocognitive systems or rather by a common preverbal approximate number system, which is ratio dependent and follows Weber's law. Here, we propose that the similarities between nonsymbolic and symbolic number processing can be explained based on the principle of efficient coding. To probe this hypothesis we employed a new empirical approach, by predicting the behavioural performance in number comparison tasks with symbolic (i.

Differences Between Readmitted and Non-readmitted Women in an Italian Forensic Unit: A Retrospective Study.

The main objective of this study was to compare readmitted (RW) and non-readmitted (NRW) female psychiatric patients after being conditionally or unconditionally released from Italian inpatient forensic psychiatry services, in order to identify variables that were significantly linked with readmission. This study included all patients who were discharged from the female Residences for the Execution of the Security Measure (REMS) of Castiglione delle Stiviere from January 2008 to June 2015 who were not readmitted until December 31, 2018 (48). In addition, data were collected on female patients who were discharged from the same REMS before 2008 and readmitted from January 2008 to December 2018 (42).