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Objectives: To assess the effectiveness of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) among people poorly represented in clinical trials and potentially at higher risk of suboptimal response to ART. Methods: Observational cohort study on persons with HIV (PWH) enrolled in ICONA who started BIC/FTC/TAF as initial therapy or as switching regimen while virologically suppressed. Primary endpoint was time to treatment failure (TF): new AIDS/death or virological failure (VF) or discontinuation for toxicity/failure. Secondary endpoints were time to treatment discontinuation for toxicity (TDT) and to VF. Groups of interest were those aged >50 years, female sex, and advanced HIV disease at first ART start. Probability of the events overall and according to groups and adjusted HR for every endpoint were calculated by Kaplan–Meier curves and Cox regression models. Results: Nine hundred and thirty-three ART-naive and 1655 ART-experienced PWH initiated BIC/FTC/TAF. Over a median follow-up of 69.8 weeks, 89 (9.6%) PWH at their first regimen experienced TF. PWH aged >50 years had 1.83-fold (95% CI: 1.19–2.83) higher risk of TF; PWH with advanced HIV disease had 2.21-fold (95% CI: 1.53–3.82) higher risk; there were no differences in TF according to sex. Over a median follow-up of 146.3 weeks, 109 (6.6%) out of 1655 switching PWH experienced TF; no differences were found in the risk of TF, TDT and VF according to groups of interest. Conclusions: Overall, BIC/FTC/TAF is well tolerated and virologically effective in the real-world scenario for ART-naive and -experienced PWH. Older ART-naive PWH and those with advanced HIV disease may respond less well as the burden of diseases might compromise treatment efficacy.

This systematic literature review set out to investigate the clinical outcomes of autogenous tooth root blocks used for ridge augmentation: survival rates, block resorption, implant survival, post-surgical complications, and histology findings. This review followed PRISMA guidelines. An automated search was made in four databases, supplemented by a manual search for relevant articles published before December 2020. The quality of evidence provided was assessed with the Newcastle-Ottawa Quality Assessment Scale and the Joanna Briggs Institute Critical Appraisal tool. Seven articles fulfilled the inclusion criteria and underwent analysis. The articles included a total of 136 patients, who received 118 autogenous tooth root blocks and 26 autogenous bone blocks showing block survival rates of 99.15% and 100%, respectively. Tooth root blocks presented a mean bone gain that was similar to autologous bone blocks but showed less resorption. The implant survival rate was 98.32% for autogenous tooth root blocks. Reconstruction of alveolar crests by means of autogenous tooth root blocks appears to be a satisfactory option for single-tooth gaps and low grades of bone atrophy in terms of the survival of the bone block and the implants placed subsequently. More research providing long-term data is needed to confirm these findings., Depto. de Especialidades Clínicas Odontológicas, Fac. de Odontología, TRUE, pub

The SESAR-funded Modern ATM via Human / Automation Learning Optimisation (MAHALO) project recently completed two years of technical work exploring the human performance impacts of AI and Machine Learning (ML), as applied to enroute ATC conflict detection and resolution (CD&R). It first developed a hybrid ML CD&R capability, along with a realtime simulation platform and experimental User Interface. After a series of development trials, the project culminated in a pair of field studies (i.e., human-in-the-loop trials) across two EU countries, with a total of 35 operational air traffic controllers. In each of these two field studies, controller behaviour was first captured in a pre-test phase, and used to train the ML system. Subsequent main experiment trials then experimentally manipulated within controllers both Conformance (as either a personalised-, group average-, or optimized model) and Transparency (as ether a baseline vector depiction, an enhanced graphical diagram, or a diagramplus-text presentation). The proposed paper presents guidelines on the design and implementation of ML systems in Air Traffic Control, derived from the results and lesson learned from the Simulations, as well as the qualitative feedback received from the controllers themselves.

Climate change represents a threat to narrow-ranged mountain species living in low-altitude massifs. We studied the disjunct Apennine population of Lasiommata petropolitana (Lepidoptera, Nymphalidae) in the Gran Sasso and Monti della Laga National Park. We quantified the altitudinal shifts undergone in the last decades (1964–2021) in the Alps and Apennines and estimated the local extinction risk due to climate change. We also sequenced the COI mitochondrial marker of seven Apennine specimens, comparing them with those available across the Palearctic. We projected the probability of presence for the species under a future climatic scenario using an ensemble forecasting approach. We found that, despite geographical isolation, the Apennine population of L. petropolitana displays a single widespread COI haplotype also occurring in most European populations. In the Alps and Apennines, this species has shifted uphill an average of 6.3 m per year since 1964. Accordingly, our model predicted a likely extinction in the Apennines by about 2060, due to a reduction of the climatic suitability in this region. Implications for insect conservation: Implications for insect conservation Despite its potential loss in the Apennines would not erode mitochondrial diversity, L. petropolitana characterises the butterfly community of the Gran Sasso massif as an alpine enclave. The loss of the Apennine population, together with those of other orophilous butterflies, could trigger a homogenization of alpha and beta diversity and induce a loss of functional diversity in the impoverished high-altitude biotas. As habitat heterogeneity is a key aspect for populations to endure climate change, the maintenance of varied microhabitats, mainly through grazing management, could address the decline of this population.

Butterfly monitoring enjoys a growing popularity in Europe, mainly supported by Butterfly Conservation Europe (BCE) and its partners. While Butterfly Monitoring Schemes are present in a growing number of countries and new ones are being initiated in many places, long time-series are currently only available for a limited number of countries. For the indicators in this report, we used data from 22 countries (Figure 1): Andorra, Austria, Belgium, Czech Republic, Estonia, Finland, France, Germany, Hungary, Ireland, Jersey, Latvia, Lithuania, Luxembourg, Norway, Romania, Slovenia, Spain, Sweden, Switzerland, The Netherlands and the United Kingdom.The indicators use field data up to and including the 2020 field season. The method for calculating indicators has been greatly improved and enhanced. During 2020, more than 2,500 standardised butterfly transects distributed across 22 monitoring schemes were used to inform the EU27 Grassland Butterfly Indicator and almost 5,000 from 25 schemes for the European Grassland Butterfly Indicator (Figure 2). Since 1990 over 6,350 and 11,500 separate transects have contributed to the EU27 and Europe indicators, respectively.

Over 100 participants joined the event on Pledges and Butterflies was organised by Butterfly Conservation Europe (BCE) and DG-Environment with support from the Natura2000 Networking events consortium led by Wageningen University. Representatives from 26 Member States, butterfly experts from across the EU, Butterfly Conservation Europe and DG-Environment met online on the 29th and 30th of March 2022 to explore the opportunities to include actions for butterflies in MS’s Pledges to implement EU Biodiversity Strategy 2030 targets.ContextThis event was organised within the frame of the EU Natura 2000 Biogeographical process and was focussed on improving the conservation and restoration of butterflies and their habitats to assist Member States in achieving the targets in the EUBDS 2030, including reversing the decline in wild pollinators and supporting the recovery of Habitats Directive and Red listed butterflies and moths.Key aims were to enhance contacts, share knowledge and expertise and make sure it was available to help MSs with developing Pledges to meet the Protected Areas and Species improvement targets.

1. Climate change is causing shifts in the distribution of many species and populations inhabiting mountain tops are particularly vulnerable to these threats because they are constrained in altitudinal shifts. Apennines are a relatively narrow and low mountain chain located in Southern Europe, which hosts many isolated populations of mountain species. The butterfly Erebia pandrose was recorded for the last time in the Apennines in 1977, on the top of a single massif (Monti della Laga). 2. We confirmed the presence of a small, isolated population of E. pandrose in the Apennines, at a distance of more than 400 km to any other known populations. Then, we examined the cytochrome c oxidase subunit 1 mitochondrial DNA marker of this species across the Palaearctic area and estimated the potential decline over the Alps and the Apennines due to future climatic changes. 3. The Apennine population represents an endemic lineage characterised by eight mutations over the 658 bp analysed (1.2%). In the Alps and Apennines, this species has shifted uphill more than 3 m per year since the end of the 19th century and more than 22 m per year since 1995. Species distribution models suggested that these mountain populations will experience a generalised loss of climatic suitability, which, according to our projections, could lead to the extinction of the Apennine population in a few decades. 4. Erebia pandrose has the potential to become a flagship species for advertising the risk of losing unique fractions of genetic diversity for mountain species.

Recently, Artificial intelligence (AI) algorithms have received increasable interest in various application domains including in Air Transportation Management (ATM). Different AI in particular Machine Learning (ML) algorithms are used to provide decision support in autonomous decision-making tasks in the ATM domain e.g., predicting air transportation traffic and optimizing traffic flows. However, most of the time these automated systems are not accepted or trusted by the intended users as the decisions provided by AI are often opaque, non-intuitive and not understandable by human operators. Safety is the major pillar to air traffic management, and no black box process can be inserted in a decision-making process when human life is involved. To address this challenge related to transparency of the automated system in the ATM domain, we investigated AI methods in predicting air transportation traffic conflict and optimizing traffic flows based on the domain of Explainable Artificial Intelligence (XAI). Here, AI models’ explainability in terms of understanding a decision i.e., post hoc interpretability and understanding how the model works i.e., transparency can be provided for air traffic controllers. In this paper, we report our research directions and our findings to support better decision making with AI algorithms with extended transparency.

Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

This systematic literature review set out to investigate the clinical outcomes of autogenous tooth root blocks used for ridge augmentation: survival rates, block resorption, implant survival, post-surgical complications, and histology findings. This review followed PRISMA guidelines. An automated search was made in four databases, supplemented by a manual search for relevant articles published before December 2020. The quality of evidence provided was assessed with the Newcastle–Ottawa Quality Assessment Scale and the Joanna Briggs Institute Critical Appraisal tool. Seven articles fulfilled the inclusion criteria and underwent analysis. The articles included a total of 136 patients, who received 118 autogenous tooth root blocks and 26 autogenous bone blocks showing block survival rates of 99.15% and 100%, respectively. Tooth root blocks presented a mean bone gain that was similar to autologous bone blocks but showed less resorption. The implant survival rate was 98.32% for autogenous tooth root blocks. Reconstruction of alveolar crests by means of autogenous tooth root blocks appears to be a satisfactory option for single-tooth gaps and low grades of bone atrophy in terms of the survival of the bone block and the implants placed subsequently. More research providing long-term data is needed to confirm these findings., Depto. de Especialidades Clínicas Odontológicas, Fac. de Odontología, TRUE, pub

There is mounting evidence of widespread declines in the diversity and abundance of insects from across the globe (Sanchez-Bayo and Wyckhuys 2019, Seibold et al. 2019, van Klink et al. 2020, Wagner 2020). This gives a stark warning for the perilous state of biodiversity (Diaz et al. 2019), and demonstrates that addressing the gap in knowledge of the status of insects is vital (Cardoso et al. 2020, Samways et al. 2020). Insects are estimated to comprise more than half of all described species and are a dominant component of biodiversity in most ecosystems (Bar-On et al. 2018). Insects also provide a crucial role in the functioning of ecosystems. They are not only related to the supply of many ecosystem services such as pollination, biological control, soil fertility regulation and diverse cultural ecosystem services but also to disservices such as damage to crops and spread of diseases to livestock and humans (Gutierrez-Arellano and Mulligan 2018, Noriega et al. 2018). There is a pressing need to assess the status of insects to set and evaluate conservation targets. At the Convention on Biological Diversity (CBD) meeting in Nagoya (Japan), the Strategic Plan for Biodiversity 2011-2020 was adopted. It proposed five goals and 20 "Aichi" biodiversity targets. In line with this plan, a new EU biodiversity strategy was adopted by the European Commission in May 2011. This strategy provided a framework for the EU to meet its biodiversity targets and global commitments as a party to the CBD. The Headline Target in the existing EU Biodiversity Strategy 2020 is to halt the loss of biodiversity and the degradation of ecosystem services in the EU by 2020, and restore them, in so far as feasible, while stepping up the EU contribution to averting global biodiversity loss. Under Target 3A the EU is committed to increasing the contribution of agriculture to biodiversity recovery. Further, the EU Biodiversity Strategy 2030 includes the development of a coherent framework for monitori