Abstract
With four companions at separations from 16 to 71 au, HR 8799 is a unique target for direct imaging, presenting an opportunity for a comparative study of exoplanets with a shared formation history. Combining new VLTI/GRAVITY observations obtained within the ExoGRAVITY program with archival data, we performed a systematic atmospheric characterisation across all four planets. We explored different levels of model flexibility to understand the temperature structure, chemistry, and clouds of each planet using both petitRADTRANS atmospheric retrievals and fits to self-consistent radiative-convective equilibrium models. Using Bayesian model averaging to combine multiple retrievals (a total of 89 across all four planets), we find that the HR 8799 planets are highly enriched in metals, with [M/H] greater than or similar to 1, and have stellar to superstellar atmospheric C/O ratios. The C/O ratio increases with increasing separation from 0.55(-0.10)(+0.12) for d to 0.78(-0.04)(+0.03) for b, with the exception of the innermost planet, which has a C/O ratio of 0.87 +/- 0.03. Such high metallicities are unexpected for these massive planets, and challenge planet-formation models. By retrieving a quench pressure and using a disequilibrium chemistry model, we derive vertical mixing strengths compatible with predictions for high-metallicity, self-luminous atmospheres. Bayesian evidence comparisons strongly favour the presence of HCN in HR 8799 c and e, as well as CH4 in HR 8799 c, with detections at > 5 sigma confidence. All of the planets are cloudy, with no evidence of patchiness. The clouds of c, d, and e are best fit by silicate clouds lying above a deep iron cloud layer, while the clouds of the cooler HR 8799 b are more likely composed of Na2S. With well-defined atmospheric properties, future exploration of this system is well positioned to unveil further details of these planets, extending our understanding of the composition, structure, and formation history of these siblings.

Abstract
The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity. However the other main stream of the brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity, has been still little explored. Inherent complexity of brain activities in resting-state, as observed in Blood Oxygenation-Level Dependant fluctuations, calls for exploratory methods for characterizing these causal networks [1]. To determine the structure of the network that causes this dynamics, it is developed a method of identification based on least squares, which assumes knowledge of the signals of brain activity in different regions. As there is no access to functional Magnetic Resonance Imaging, data it is developed a model to obtain the Blood Oxygenation Level Dependent signals and it is implemented a reverse hemo-dynamic function. To assess the performance of the created model Monte Carlo simulations have been used.

Abstract
Unmanned Aerial Vehicle (UAV) applications, particularly for indoor tasks such as inventory management, infrastructure inspection, and emergency response, are becoming increasingly complex with dynamic environments and their different elements. During operation, the vehicle's response depends on various decisions regarding its surroundings and the task goal. Reinforcement Learning techniques can solve this decision problem by helping build more reactive, adaptive, and efficient navigation operations. This paper presents a framework to simulate the navigation of a UAV in an operational environment, training and testing it with reinforcement learning models for further deployment in the real drone. With the support of the 3D simulator Gazebo and the framework Robot Operating System (ROS), we developed a training environment conceivably simple and fast or more complex and dynamic, explicit as the real-world scenario. The multi-environment simulation runs in parallel with the Deep Reinforcement Learning (DRL) algorithm to provide feedback for the training. TD3, DDPG, PPO, and PPO+LSTM were trained to validate the framework training, testing, and deployment in an indoor scenario.

Abstract
Currently, lung cancer is one of the deadliest diseases that affects millions of people globally. However, Artificial Intelligence is being increasingly integrated with healthcare practices, with the goal to aid in the early diagnosis of lung cancer. Although such methods have shown very promising results, they still lack transparency to the user, which consequently could make their generalised adoption a challenging task. Therefore, in this work we explore the use of post-hoc explainable methods, to better understand the inner-workings of an already established multitasking framework that executes the segmentation and the classification task of lung nodules simultaneously. The idea behind such study is to understand how a multitasking approach impacts the model's performance in the lung nodule classification task when compared to single-task models. Our results show that the multitasking approach works as an attention mechanism by aiding the model to learn more meaningful features. Furthermore, the multitasking framework was able to achieve a better performance in regard to the explainability metric, with an increase of 7% when compared to our baseline, and also during the classification and segmentation task, with an increase of 4.84% and 15.03%; for each task respectively, when also compared to the studied baselines.

Abstract
Methylation is considered one of the proteins' most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of computer modelling, methylation sites can be identified quickly and accurately, providing valuable information for further trial and investigation. In this study, we propose a new machine-learning model called MeSEP to predict methylation sites that incorporates both evolutionary and structural-based information. To build this model, we first extract evolutionary and structural features from the PSSM and SPD2 profiles, respectively. We then employ Extreme Gradient Boosting (XGBoost) as the classification model to predict methylation sites. To address the issue of imbalanced data and bias towards negative samples, we use the SMOTETomek-based hybrid sampling method. The MeSEP was validated on an independent test set (ITS) and 10-fold cross-validation (TCV) using lysine methylation sites. The method achieved: an accuracy of 82.9% in ITS and 84.6% in TCV; precision of 0.92 in ITS and 0.94 in TCV; area under the curve values of 0.90 in ITS and 0.92 in TCV; F1 score of 0.81 in ITS and 0.83 in TCV; and MCC of 0.67 in ITS and 0.70 in TCV. MeSEP significantly outperformed previous studies found in the literature. MeSEP as a standalone toolkit and all its source codes are publicly available at https://github.com/arafatro/MeSEP.

Abstract
Despite the prevailing paradigm of user-friendliness and enjoyment in mainstream game design and user interface design, intentional friction in game user interfaces that can be used to create meaningful experiences and to encourage reflection in players. This work aims to explore such use of intentional friction, providing designers with a valuable resource to generate unconventional game interfaces. As a starting point, we previously identified six strategies for intentional friction: (1) exploit memory shortcomings; (2) faulty feedback; (3) mismatched mental models; (4) impairment of ability; (5) deliberate inefficiency; and (6) oppressive constraints. Afterwards, to help operationalise these strategies and identify others, we ran co-creation workshops with game and UI designers, which lead to the development of a tool composed of three decks of cards, combining additional friction strategies, intentions, emotions, and ideation triggers, and enabling designers to create expressive game interfaces that intentionally incorporate friction as a design strategy. The Friction Firestarter toolkit is intended to inspire designers to explore various options and think creatively about friction in UI design. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.