Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative Lipidomics and Metabolomics for System-Level Understanding of the Metabolic Syndrome in Long-Term Treated HIV-Infected Individuals

People living with HIV (PLWH) require life-long anti-retroviral treatment and often present with comorbidities such as metabolic syndrome (MetS). Systematic lipidomic characterization and its association with the metabolism are currently missing. We included 100 PLWH with MetS and 100 without MetS from the Copenhagen Comorbidity in HIV Infection (COCOMO) cohort to examine whether and how lipidome profiles are associated with MetS in PLWH. We combined several standard biostatistical, machine learning, and network analysis techniques to investigate the lipidome systematically and comprehensively and its association with clinical parameters. Additionally, we generated weighted lipid-metabolite networks to understand the relationship between lipidomic profiles with those metabolites associated with MetS in PLWH. The lipidomic dataset consisted of 917 lipid species including 602 glycerolipids, 228 glycerophospholipids, 61 sphingolipids, and 26 steroids. With a consensus approach using four different statistical and machine learning methods, we observed 13 differentially abundant lipids between PLWH without MetS and PLWH with MetS, which mainly belongs to diacylglyceride (DAG, n = 2) and triacylglyceride (TAG, n = 11). The comprehensive network integration of the lipidomics and metabolomics data suggested interactions between specific glycerolipids’ structural composition patterns and key metabolites involved in glutamate metabolism. Further integration of the clinical data with metabolomics and lipidomics resulted in the association of visceral adipose tissue (VAT) and exposure to earlier generations of antiretroviral therapy (ART). Our integrative omics data indicated disruption of glutamate and fatty acid metabolism, suggesting their involvement in the pathogenesis of PLWH with MetS. Alterations in the lipid homeostasis and glutaminolysis need clinical interventions to prevent accelerated aging in PLWH with MetS.
Rules, cases and arguments in artificial intelligence and law

In this chapter, three styles of legal reasoning are discussed as they have been studied in the field of Artificial Intelligence and Law: rule-based reasoning, case-based reasoning and argument-based reasoning. In rule-based reasoning, conclusions are drawn when the rule conditions are fulfilled, unless there is an exception to the rule. In case-based reasoning, the decision of a precedent case is followed in the current case when it is analogous to the precedent, but there can be relevant distinctions. In argument-based reasoning, a conclusion follows when it is supported by an argument that is not defeated by a counterargument. Rule-based, case-based and argument-based reasoning are illustrated using tort law in the Netherlands as example. The chapter ends with suggestions for further reading in Artificial Intelligence and Law research.
Randomised controlled trial comparing narrative exposure therapy with present-centred therapy for older patients with post-traumatic stress disorder.

BACKGROUND Evidence-based treatment and age-specific services are required to address the needs of trauma-affected older populations. Narrative exposure therapy (NET) may present an appropriate treatment approach for this population since it provides prolonged exposure in a lifespan perspective. As yet, however, no trial on this intervention has been conducted with older adults from Western Europe.AimsExamining the efficacy of NET in a sample of older adults. METHOD Out-patients with post-traumatic stress disorder (PTSD), aged 55 years and over, were randomly assigned to either 11 sessions of NET (n = 18) or 11 sessions of present-centred therapy (PCT) (n = 15) and assessed on the Clinician-Administered PTSD Scale (CAPS) pre-treatment, post-treatment and at follow-up. Total scores as well as symptom scores (re-experience, avoidance and hyperarousal) were evaluated. RESULTS Using a piecewise mixed-effects growth model, at post-treatment a medium between-treatment effect size for CAPS total score (Cohen's d = 0.44) was found, favouring PCT. At follow-up, however, the between-treatment differences were non-significant. Drop-out rates were low (NET 6.7%, PCT 14.3%) and no participant dropped out of the study because of increased distress. CONCLUSIONS Both NET and PCT appear to be safe and efficacious treatments with older adults: PCT is non-intrusive and NET allows for imaginal exposure in a lifespan perspective. By selectively providing these approaches in clinical practice, patient matching can be optimised.Declaration of interestNone.
Efficacy of a web-based intervention for improving psychosocial well-being in patients with implantable cardioverter-defibrillators: the randomized controlled ICD-FORUM trial

Abstract Aims Anxiety, depression, and reduced quality of life (QoL) are common in patients with implantable cardioverter-defibrillators (ICDs). Treatment options are limited and insufficiently defined. We evaluated the efficacy of a web-based intervention (WBI) vs. usual care (UC) for improving psychosocial well-being in ICD patients with elevated psychosocial distress. Methods and results This multicentre, randomized controlled trial (RCT) enrolled 118 ICD patients with increased anxiety or depression [≥6 points on either subscale of the Hospital Anxiety and Depression Scale (HADS)] or reduced QoL [≤16 points on the Satisfaction with Life Scale (SWLS)] from seven German sites (mean age 58.8 ± 11.3 years, 22% women). The primary outcome was a composite assessing change in heart-focused fear, depression, and mental QoL 6 weeks after randomization to WBI or UC, stratified for age, gender, and indication for ICD placement. Web-based intervention consisted of 6 weeks’ access to a structured interactive web-based programme (group format) including self-help interventions based on cognitive behaviour therapy, a virtual self-help group, and on-demand support from a trained psychologist. Linear mixed-effects models analyses showed that the primary outcome was similar between groups (η p 2 = 0.001). Web-based intervention was superior to UC in change from pre-intervention to 6 weeks (overprotective support; P = 0.004, η p 2 = 0.036), pre-intervention to 1 year (depression, P = 0.004, η p 2 = 0.032; self-management, P = 0.03, η p 2 = 0.015; overprotective support; P = 0.02, η p 2 = 0.031), and 6 weeks to 1 year (depression, P = 0.02, η p 2 = 0.026; anxiety, P = 0.03, η p 2 = 0.022; mobilization of social support, P = 0.047, η p 2 = 0.018). Conclusion Although the primary outcome was neutral, this is the first RCT showing that WBI can improve psychosocial well-being in ICD patients.
Neural network model with positional deviation correction for Fourier ptychography

Fourier ptychography is a new type of computational imaging technology, which uses a stack of low‐resolution images obtained from overlapped apertures (or equivalent) to reconstruct super‐resolved image. However, the accuracy of the aperture position will directly affect the quality and resolution of the reconstructed image. This paper proposes a new perspective for FP positional deviations correction using the neural network. We construct a trainable neural network to perform positional deviations correction along with object reconstruction. The real part and imaginary part of the object as well as the different and irregular positional deviations of each aperture are set as the weights of convolutional layer. The gradients over these weights are computed automatically, and gradient‐based optimization algorithm is employed to recover the object and find the correct aperture position. Simulation and experiment are performed to verify our algorithm. The results show that the proposed algorithm can accurately find the aperture position and improve the reconstruction quality.
On the origin of multiple populations during massive star cluster formation

We investigate the possibility that multiple populations in globular clusters arise as a natural by-product of massive star-cluster formation. We use 3D radiative hydrodynamics simulations for the formation of young massive clusters to track their chemical self-enrichment during their first 5 Myr. These clusters form embedded within filamentary Giant Molecular Clouds by a combination of gas accretion and rapid merging of protoclusters. Chemical enrichment is a dynamic process happening as the young cluster assembles, so that the original (1P) and enriched (2P) subpopulations of stars form almost simultaneously. Here we test two simple and opposite extremes for the injection of enriched material into the intracluster gas: we assume either continuous injection in a way that tracks the star formation rate; or sudden injection by a single instantaneous event. Using helium abundance as a proxy for the enrichment, we find that realistic multiple population features can be reproduced by injecting a total helium mass amounting to a few percent of the cluster's total mass. The differences in individual growth histories can lead to widely differing 1P/2P outcomes. These models suggest that dual or multiple populations can emerge rapidly in massive star clusters undergoing the typical mode of star cluster formation.
Spatial Shortcut Network for Human Pose Estimation

Like many computer vision problems, human pose estimation is a challenging problem in that recognizing a body part requires not only information from local area but also from areas with large spatial distance. In order to spatially pass information, large convolutional kernels and deep layers have been normally used, introducing high computation cost and large parameter space. Luckily for pose estimation, human body is geometrically structured in images, enabling modeling of spatial dependency. In this paper, we propose a spatial shortcut network for pose estimation task, where information is easier to flow spatially. We evaluate our model with detailed analyses and present its outstanding performance with smaller structure.
Geometry of Bäcklund transformations II: Monge–Ampère invariants

This article is concerned with the question: For which pairs of hyperbolic Euler–Lagrange systems in the plane does there exist a rank-$1$ Bäcklund transformation relating them? We express some obstructions to such existence in terms of the local invariants of the Euler–Lagrange systems. In addition, we discover a class of Bäcklund transformations relating two hyperbolic Euler–Lagrange systems of distinct types.
How does Omnichannel Integration Quality Affect Consumers' Stickiness Intention

The popularity of mobile devices and the advancement of modern technology have resulted in more diversified consumer shopping channels, and many retailers have consequently embraced omnichannel retailing. The core concept of omnichannel retailing is to provide consumers with a seamless experience. Using commitment–trust theory as a framework, this study examines the effects of omnichannel integration quality on consumers’ stickiness intention. Analysis of data of 194 respondents reveals that consumer trust affects relationship commitment. Consumer trust and relationship commitment have a strong influence on stickiness intention, and breadth, transparency, and process consistency directly affect trust. Contrary to our expectation, omnichannel integration quality does not affect relationship commitment. Our results show that high levels of omnichannel integration quality can increase consumer trust, which helps retailers maintain favorable customer relationships. The findings of this study provide important theoretical and practical implications for omnichannel retailing research.
Identifying Potential Conflict in Land-Use Planning Using a Values-Centered E-Participation Tool: A Canadian Case Study in Aggregate Mining

An innovative e-participation tool is used to facilitate the articulation of value-laden assumptions, and to identify key points of likely conflict in an aggregate mining controversy in Ontario, Canada. The expert model developed using ‘Public to Public Decision Support System (P2P-DSS) reflects a perspective that differs significantly from public perceptions in terms of the social values that influence rejection of a permit amendment application. By facilitating two-way communication about values assumptions P2P-DSS generates a novel dataset that can support pro-active conflict management and contribute to a shared understanding between government decision-makers and public citizens.
Enhancing Power System Cyber-Security With Systematic Two-Stage Detection Strategy

State estimation estimates the system condition in real-time and provides a base case for other energy management system (EMS) applications including real-time contingency analysis and security-constrained economic dispatch. Recent work in the literature shows malicious cyber-attack can inject false measurements that bypass traditional bad data detection in state estimation and cause actual overloads. Thus, it is very important to detect such cyber-attack. In this paper, multiple metrics are proposed to monitor abnormal load deviations and suspicious branch flow changes. A systematic two-stage approach is proposed to detect false data injection (FDI) cyber-attack. The first stage determines whether the system is under attack while the second stage identifies the target branch. Numerical simulations verify that FDI can cause severe system violations and demonstrate the effectiveness of the proposed two-stage FDI detection (FDID) method. It is concluded that the proposed FDID approach can efficiently detect FDI cyber-attack and identify the target branch, which will substantially improve operators’ situation awareness in real-time.
Mapping Accessible Paths in the City Using Collective Intelligence

New information and communication technologies (ICTs) have an increasingly stronger role in people's lives, especially after the commoditization of smartphones. They affect many aspects of everyday life, including urban mobility. Some applications, including Waze, benefit from the collective intelligence (CI) of the crowds to gather the information they need to provide users with good advice on the routes to follow. But they are mainly focused on roads and streets, giving little information on the quality of sidewalks, which are essential to pedestrians, people on wheelchairs and blind people. With the intention to improve the mobility of citizens with special needs, we developed the prototype of an application that allows users themselves to update accessibility maps, tagging obstacles and also indicating the existence of resources that contribute to improve the mobility of people with special needs in urban spaces. Tests in a controlled environment helped to debug the application’s functionalities, before members of the intended target group of users were finally exposed to it. Results are promising, as users were able to include relevant data by themselves and seem motivated to keep doing so, due a sense of utility, social facilitation or simply due to altruism, as anticipated by the CI literature. One unexpected outcome was that impaired users are more excited about the potential the application has to give visibility to the challenges they face than with the actual improvement it can bring to their mobility.
Social Media Affordances and Sequential Use to Engage Clients During the Sales Process

Social media has changed the ways salespeople communicate and build relationships with clients. This study explores how social media are integrated into the communication repertoires of sellers using an affordances framework. Based on data from a large multinational corporation, seller strategies used to build and develop relationships with customers are analyzed. The findings reveal that employees strategically select media that allow for common affordances in order to build stronger client relationships. The discussion outlines practical implications for managers, and aligns the process of social media selection with the sales cycle based on capabilities afforded by various media choices.
Direct Modelling of Speech Emotion from Raw Speech

Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not always guaranteed to be the best in a statistical modelling framework where the end goal is for example emotion classification. This has fuelled the emerging trend of learning representations from raw speech especially using deep learning neural networks. In particular, a combination of Convolution Neural Networks (CNNs) and Long Short Term Memory (LSTM) have gained great traction for the intrinsic property of LSTM in learning contextual information crucial for emotion recognition; and CNNs been used for its ability to overcome the scalability problem of regular neural networks. In this paper, we show that there are still opportunities to improve the performance of emotion recognition from the raw speech by exploiting the properties of CNN in modelling contextual information. We propose the use of parallel convolutional layers to harness multiple temporal resolutions in the feature extraction block that is jointly trained with the LSTM based classification network for the emotion recognition task. Our results suggest that the proposed model can reach the performance of CNN trained with hand-engineered features from both IEMOCAP and MSP-IMPROV datasets.
Socio-Economic Predictors of Hiring Live-In Migrant Care Workers to Support Community Dwelling Older Adults with Long-Term Care Needs: Recent Evidence from a Central Italian Region

To meet the rising demand for home care, many families in Italy hire live-in migrant care workers (MCWs). However, the reliance on MCWs to provide long-term care (LTC) and a lack of alternative formal care services raises concerns around equality in access to care. This study aimed to determine the socio-economic predictors of hiring live-in MCWs among older adults with LTC needs in Italy, the objective care burden placed on MCWs, and the financial barriers that people in need of care and informal caregivers face when hiring MCWs, analysing data from a cross-sectional questionnaire with 366 older adults with LTC needs and their primary family caregivers living in the Marche region. Binary logistic regression was used to calculate the predictors of hiring a live-in MCW. Having a primary caregiver that had a high school education or above significantly increased the odds of hiring a live-in MCW (Odds Ratio (OR) = 3.880), as did receiving a social pension (OR = 2.258). Over half (57.5 percent) of the people in need of care had difficulties in affording the costs of hiring an MCW in the past year. To increase the sustainability of the Italian MCW market and reduce socio-economic barriers to accessing care, the Italian Government should increase funding for LTC benefits and add means testing and restrictions on the use of cash-for-care allowances.
Possibilities of artificial intelligence use in orthodontic diagnosis and treatment planning: Image recognition and three-dimensional VTO

Abstract Orthodontic diagnosis is a comprehensive procedure that integrates various information obtained from the facial and occlusal structure as well as patient's individual needs. Hence it is not easy to imagine if the artificial intelligence(AI) would eventually replace the conventional diagnostic process. Nonetheless, recent advances in the machine learning and artificial intelligence have been applied to the cephalometric tracing and model analysis via automated image recognition, exhibiting relatively high reliability. Based on the cumulated experiences and research outcomes, orthodontic diagnostics have taken a small step towards an automated process. Considering that the orthodontic diagnosis starts from the recognition of the space discrepancy between the initial status and idealized occlusion, semi-automated three-dimensional visualized treatment objectives (VTO) may be established. This article covers the brief overview of the current status in machine learning especially focusing on the image recognition. Recent advances in the fabrication of three-dimensional VTO using surface landmarks and automated setup process is demonstrated. In the near future, a more clinically relevant VTO can be utilized using the imaginary center of resistance, to provide useful clues to the orthodontists in many of the borderline cases between extraction and non-extraction, and between surgery and non-surgery.
System Design for an Online Social Networking App with a Notification and Recommender System to Build Social Capital in a University Setting

Social capital in higher education commuter institutions may be declining because fewer students remain on campus. Social capital is the network of relationships in a group. Higher social capital is derived from broader and more complex networks. Social networks can grow because members who belong to a particular group possess a sense of community. If students spend less time on campus, their sense of community may decrease, because they would be less likely to participate in the community. This puts Higher Ed commuter institutions at a disadvantage in terms of generating and maintaining social capital. We investigate the possibility to counter this disadvantage by actively promoting participating in an Online Social Network (OSN); specifically, with the use of a Notification and Recommender System (NARS) in an OSN via a mobile platform. Our results suggest that introducing a purposefully designed OSN has the potential to facilitate creation of structural and relational social capital, but that it might not have an effect on cognitive social capital.
Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1

Previous studies in that line suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections in the visual system with a neurodynamic network of firing-rate neurons. Early subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions. Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences during free viewing. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) predict attention at distinct image contexts. Author summary Saliency maps are the representations of how certain visual regions attract attention in a visual scene, and these can be measured with eye movements. A myriad of computational models with artificial and biological inspiration have been able to acquire outstanding predictions of human fixations. However, most of these models have been built specifically for visual saliency, a characteristic that denies their biological plausibility for modeling distinct visual processing mechanisms or other visual processes simultaneously. In addition to saliency, our approach is also able to efficiently work for other tasks (without applying any type of training or optimization and keeping the same parametrization) such as Visual Search, Visual Discomfort [1], Brightness [2] and Color Induction [3]. By performing simulations of human physiology and its mechanisms, we propose to build a unified model that could be extended to predict and understand distinct perceptual processes in which V1 is responsible.