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Here we describe the novel, multi-point Comet Interceptor mission. It is dedicated to the exploration of a little-processed long-period comet, possibly entering the inner Solar System for the first time, or to encounter an interstellar object originating at another star. The objectives of the mission are to address the following questions: What are the surface composition, shape, morphology, and structure of the target object? What is the composition of the gas and dust in the coma, its connection to the nucleus, and the nature of its interaction with the solar wind? The mission was proposed to the European Space Agency in 2018, and formally adopted by the agency in June 2022, for launch in 2029 together with the Ariel mission. Comet Interceptor will take advantage of the opportunity presented by ESA's F-Class call for fast, flexible, low-cost missions to which it was proposed. The call required a launch to a halo orbit around the Sun-Earth L2 point. The mission can take advantage of this placement to wait for the discovery of a suitable comet reachable with its minimum Δ V capability of 600  ms - 1 . Comet Interceptor will be unique in encountering and studying, at a nominal closest approach distance of 1000 km, a comet that represents a near-pristine sample of material from the formation of the Solar System. It will also add a capability that no previous cometary mission has had, which is to deploy two sub-probes - B1, provided by the Japanese space agency, JAXA, and B2 - that will follow different trajectories through the coma. While the main probe passes at a nominal 1000 km distance, probes B1 and B2 will follow different chords through the coma at distances of 850 km and 400 km, respectively. The result will be unique, simultaneous, spatially resolved information of the 3-dimensional properties of the target comet and its interaction with the space environment. We present the mission's science background leading to these objectives, as well as an overview of the scientific instruments, mission design, and schedule., Competing Interests: Competing InterestsThe authors declare no competing interests., (© The Author(s) 2024.)

Background: Although children with neurodevelopmental disability (NDD) present with several deficits, they partially share developmental impairments in prelinguistic intersubjective and socio-communicative skills, which are not easily assessed by conventional tests during the first years of life., Aim: The current paper presents a new procedure to assess the prelinguistic intersubjective and socio-communicative skills of NDD children aged 0-36 months. A specific observation form template, called the Observation of Prelinguistic Intersubjective and Socio-Communicative Skills (OPISCoS) form, has been designed to systematically detect infant skills during daily routines (e.g., mealtime, playtime, desk activities). The OPISCoS form helps speech therapists to provide parents support to better perceive and understand early communicative signals from their children, avoiding the risk of excessive or reduced social stimulation., Methods: The OPISCoS form is composed of three sections, namely, "Pragmatics and Communication," "Decoding," and "Expression," which are useful to delineate the communication abilities of children with NDD and are not tapped by traditional batteries. Vignettes from clinical practice illustrate and provide exemplifications for using the OPISCoS form with NDD infants and their parents., Results: The OPISCoS form was reported for two children and showed potential in detecting disrupted communicative behaviors and planning specific early interventions. Further, we observed an improvement not only in children's communicative abilities improve but also in their interactions with parents. From a clinical point of view, the OPISCoS form (1) offers an observational perspective of prelinguistic intersubjective and socio-communicative skills in infants with NDD and (2) may be useful to practitioners to enhance parents' sensitivity to their infants' communicative behavior., Conclusion: The OPISCoS form was developed in clinical practice and is based on a very preliminary description of a new observational procedure as integration for the assessment of NDD children. The OPISCoS form appears to be a useful tool for the clinical assessment of prelinguistic intersubjective and socio-communicative skills in NDD infants as well as for promoting the quality of early parenting., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Strazzer, Sacchi, Rigamonti, Miccoli, Bonino, Giancola, Germiniasi and Montirosso.)

The aim of the present study is to investigate and compare the performances of 68 Ga-PSMA and 68 Ga-DOTA-RM2 PET/MRI in identifying recurrent prostate cancer (PCa) after primary treatment and to explore the association of dual-tracer PET findings with clinical and histopathological characteristics. Thirty-five patients with biochemical relapse (BCR) of PCa underwent 68 Ga PSMA PET/MRI for restaging purpose, with 31/35 also undergoing 68 Ga-DOTA-RM2 PET/MRI scan within 16 days (mean: 3 days, range: 2-16 days). Qualitative and quantitative image analysis has been performed by comparing 68 Ga-PSMA and 68 Ga-DOTA-RM2 PET/MRI findings both on a patient and lesion basis. Clinical and instrumental follow-up was used to validate PET findings. Fisher's exact test and Mann-Whitney U test were used to investigate the association between dual-tracer PET findings, clinical and histopathological data. p -value significance was defined below the 0.05 level. Patients' mean age was 70 years (range: 49-84) and mean PSA at time of PET/MR scans was 1.88 ng/mL (range: 0.21-14.4). A higher detection rate was observed for 68 Ga-PSMA PET/MRI, with more lesions being detected compared to 68 Ga-DOTA-RM2 PET/MRI (26/35 patients, 95 lesions vs. 15/31 patients, 41 lesions; p = 0.016 and 0.002). 68 Ga-PSMA and 68 Ga-DOTA-RM2 PET/MRI findings were discordant in 11/31 patients; among these, 10 were 68 Ga-PSMA positive (9/10 confirmed as true positive and 1/10 as false positive by follow-up examination). Patients with higher levels of PSA and shorter PSA doubling time (DT) presented more lesions on 68 Ga-PSMA PET/MRI ( p = 0.006 and 0.044), while no association was found between PET findings and Gleason score. 68 Ga-PSMA has a higher detection rate than 68 Ga-DOTA-RM2 in detecting PCa recurrence. The number of 68 Ga-PSMA PET positive lesions is associated with higher levels of PSA and shorter PSA DT, thus representing potential prognostic factors.

The synthesis of nanogels as devices capable to maintain the drug level within a desired range for a long and sustained period of time is a leading strategy in controlled drug delivery. However, with respect to the good results obtained with antibodies and peptides there are a lot of problems related to the quick and uncontrolled diffusion of small hydrophilic molecules through polymeric network pores. For these reasons research community is pointing toward the use of click strategies to reduce release rates of the linked drugs to the polymer chains. Here we propose an alternative method that considers the electrostatic interactions between polymeric chains and drugs to tune the release kinetics from nanogel network. The main advantage of these systems lies in the fact that the carried drugs are not modified and no chemical reactions take place during their loading and release. In this work we synthesized PEG-PEI based nanogels with different protonation degrees and the release kinetics with charged and uncharged drug mimetics (sodium fluorescein, SF, and rhodamine B, RhB) were studied. Moreover, also the effect of counterion used to induce protonation was taken into account in order to build a tunable drug delivery system able to provide multiple release rates with the same device., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Learning filters to produce sparse image representations in terms of over-complete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, making their use computationally expensive. In this paper, we show that such filters can be computed as linear combinations of a smaller number of separable ones, thus greatly reducing the computational complexity at no cost in terms of performance. This makes filter learning approaches practical even for large images or 3D volumes, and we show that we significantly outperform state-of-the-art methods on the curvilinear structure extraction task, in terms of both accuracy and speed. Moreover, our approach is general and can be used on generic convolutional filter banks to reduce the complexity of the feature extraction step.

We present a novel, fully-discriminative method for curvilinear structure segmentation that simultaneously learns a classifier and the features it relies on. Our approach requires almost no parameter tuning and, in the case of 2D images, removes the requirement for hand-designed features, thus freeing the practitioner from the time-consuming tasks of parameter and feature selection. Our approach relies on the Gradient Boosting framework to learn discriminative convolutional filters in closed form at each stage, and can operate on raw image pixels as well as additional data sources, such as the output of other methods like the Optimally Oriented Flux. We will show that it outperforms state-of-the-art curvilinear segmentation methods on both 2D images and 3D image stacks.

Extracting linear structures, such as blood vessels or dendrites, from images is crucial in many medical imagery applications, and many handcrafted features have been proposed to solve this problem. However, such features rely on assumptions that are never entirely true. Learned features, on the other hand, can capture image characteristics difficult to define analytically, but tend to be much slower to compute than handcrafted features. We propose to complement handcrafted methods with features found using very recent Machine Learning techniques, and we show that even few filters are sufficient to efficiently leverage handcrafted features. We demonstrate our approach on the STARE, DRIVE, and BF2D datasets, and on 2D projections of neural images from the DIADEM challenge. Our proposal outperforms handcrafted methods, and pairs up with learning-only approaches at a fraction of their computational cost.

The contextual method was used to identify the incidence of recent life events and difficulties among 50 suicide attempters compared with a control group selected at random from the general population. Suicide attempters experienced a significantly greater incidence of major life events, although for all life events irrespective of stressfulness, and for independent events the differences were not significant. The overall incidence of difficulties was also higher among suicide attempters. Finally, the results suggest a vulnerability effect as far as three factors are concerned: early loss of/separation from one or both parents, absence of paid employment and living in a nuclear family.