Search

The integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate the quality of modeling tools. However, a consensus is yet to be reached on which MDE tasks can derive substantial benefits from ML and how progress in these tasks should be measured. This paper introduces ModelXGlue , a dedicated benchmarking framework to empower researchers when constructing benchmarks for evaluating the application of ML to address MDE tasks. A benchmark is built by referencing datasets and ML models provided by other researchers, and by selecting an evaluation strategy and a set of metrics. ModelXGlue is designed with automation in mind and each component operates in an isolated execution environment (via Docker containers or Python environments), which allows the execution of approaches implemented with diverse technologies like Java, Python, R, etc. We used ModelXGlue to build reference benchmarks for three distinct MDE tasks: model classification, clustering, and feature name recommendation. To build the benchmarks we integrated existing third-party approaches in ModelXGlue . This shows that ModelXGlue is able to accommodate heterogeneous ML models, MDE tasks and different technological requirements. Moreover, we have obtained, for the first time, comparable results for these tasks. Altogether, it emerges that ModelXGlue is a valuable tool for advancing the understanding and evaluation of ML tools within the context of MDE., Funding Agencies|Ministerio de Ciencia e Innovacin

Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made on Artificial Intelligence (AI) techniques, questions arise for the future of MDE such as how existing MDE techniques and technologies can be improved or how other activities which currently lack dedicated support can also be automated. However, at the same time, it has to be revisited where and how models should be used to keep the engineers in the loop for creating, operating, and maintaining complex systems. To trigger dedicated research on these open points, we discuss the history of automation in MDE and present perspectives on how automation in MDE can be further improved and which obstacles have to be overcome in the medium and long term perspective.

Code comments play a crucial role in software development, as they provide programmers with practical information, allowing them to understand better the intent and semantics of the underpinning code. Nevertheless, developers tend to leave comments unchanged after updating the code, resulting in a discrepancy between the two artifacts. Such a discrepancy may trigger misunderstanding and confusion among developers, impeding various activities, including code comprehension and maintenance. Thus, it is crucial to identify if, given a code snippet, its corresponding comment is coherent and reflects well the intent behind the code. Unfortunately, existing approaches to this problem, while obtaining an encouraging performance, either rely on heavily pre-trained models, or treat input data as text, neglecting the intrinsic features contained in comments and code, including word order and synonyms. This work presents Co3D as a practical approach to the detection of code comment coherence. We pay attention to internal meaning of words and sequential order of words in text while predicting coherence in code-comment pairs. We deployed a combination of Gensim word2vec encoding and a simple recurrent neural network, a combination of Gensim word2vec encoding and an LSTM model, and CodeBERT. The experimental results show that Co3D obtains a promising prediction performance, thus outperforming well-established baselines. We conclude that depending on the context, using a simple architecture can introduce a satisfying prediction., Comment: The paper has been peer-reviewed and accepted to the 28th International Conference on Evaluation and Assessment in Software Engineering (EASE 2024)

Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless, the platform currently lacks a comprehensive categorization of PTMs designed specifically for SE, i.e., the existing tags are more suited to generic ML categories. This paper introduces an approach to address this gap by enabling the automatic classification of PTMs for SE tasks. First, we utilize a public dump of HF to extract PTMs information, including model documentation and associated tags. Then, we employ a semi-automated method to identify SE tasks and their corresponding PTMs from existing literature. The approach involves creating an initial mapping between HF tags and specific SE tasks, using a similarity-based strategy to identify PTMs with relevant tags. The evaluation shows that model cards are informative enough to classify PTMs considering the pipeline tag. Moreover, we provide a mapping between SE tasks and stored PTMs by relying on model names., Comment: Accepted at The International Conference on Evaluation and Assessment in Software Engineering (EASE), 2024 edition

Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems. With the increasing use of machine learning in software systems, researchers have been developing techniques to automatically assess the fairness of software systems. Nonetheless, a significant proportion of these techniques rely upon pre-established fairness definitions, metrics, and criteria, which may fail to encompass the wide-ranging needs and preferences of users and stakeholders. To overcome this limitation, we propose a novel approach, called MODNESS, that enables users to customize and define their fairness concepts using a dedicated modeling environment. Our approach guides the user through the definition of new fairness concepts also in emerging domains, and the specification and composition of metrics for its evaluation. Ultimately, MODNESS generates the source code to implement fair assessment based on these custom definitions. In addition, we elucidate the process we followed to collect and analyze relevant literature on fairness assessment in software engineering (SE). We compare MODNESS with the selected approaches and evaluate how they support the distinguishing features identified by our study. Our findings reveal that i) most of the current approaches do not support user-defined fairness concepts; ii) our approach can cover two additional application domains not addressed by currently available tools, i.e., mitigating bias in recommender systems for software engineering and Arduino software component recommendations; iii) MODNESS demonstrates the capability to overcome the limitations of the only two other Model-Driven Engineering-based approaches for fairness assessment.

IoT systems' complexity and susceptibility to failures pose significant challenges in ensuring their reliable operation. Failures can be internally generated or caused by external factors, impacting both the system's correctness and its surrounding environment. To investigate these complexities, various modeling approaches have been proposed to raise the level of abstraction, facilitating automation and analysis. Failure-Logic Analysis (FLA) is a technique that helps predict potential failure scenarios by defining how a component's failure logic behaves and spreads throughout the system. However, manually specifying FLA rules can be arduous and error-prone, leading to incomplete or inaccurate specifications. In this paper, we propose adopting testing methodologies to improve the completeness and correctness of these rules. How failures may propagate within an IoT system can be observed by systematically injecting failures, while running test cases to collect evidence useful to add, complete and refine FLA rules.

Recommender systems for software engineering (RSSEs) assist software engineers in dealing with a growing information overload when discerning alternative development solutions. While RSSEs are becoming more and more effective in suggesting handy recommendations, they tend to suffer from popularity bias, i.e., favoring items that are relevant mainly because several developers are using them. While this rewards artifacts that are likely more reliable and well-documented, it would also mean that missing artifacts are rarely used because they are very specific or more recent. This paper studies popularity bias in Third-Party Library (TPL) RSSEs. First, we investigate whether state-of-the-art research in RSSEs has already tackled the issue of popularity bias. Then, we quantitatively assess four existing TPL RSSEs, exploring their capability to deal with the recommendation of popular items. Finally, we propose a mechanism to defuse popularity bias in the recommendation list. The empirical study reveals that the issue of dealing with popularity in TPL RSSEs has not received adequate attention from the software engineering community. Among the surveyed work, only one starts investigating the issue, albeit getting a low prediction performance., Comment: 13 pages, To be appeared in the 20th Mining Software Repository Proceedings

Background: Acute coronary syndromes (ACS) are a major cause of morbidity and mortality. As cytomegalovirus (CMV) may contribute to Cardio-Vascular (CV) manifestations, we sought to provide a proof-of-concept for the involvement of coronary and/or systemic CMV-reactivation as a possible ACS trigger. Methods: We prospectively enrolled consecutive patients undergoing a coronary angiography for ACS (acute-cases, N=136), or non-ACS reasons (chronic-cases, N=57). Matched coronary and peripheral blood-samples were processed for quantification of CMV-DNAemia (RT-PCR), CMVIgG/ IgM, and CMV-IgG avidity (ELISA). Peripheral-blood samples from 17 healthy subjects were included as controls. Results: Out of the 193 cases included, 18.1% were aged ≤55 years, 92.5% were Central-European, and 100% immunocompetent. CMV-IgG seroprevalence was 91.7% (95%CI: 87.8-95.6), significantly higher than in healthy-controls (52.9% [95%CI: 29.2-76.5]; p<0.001), yet consistent across age-groups (p=0.602), male/females (p=0.765), and acute/chronic-cases (p=0.157). Median (IQR) IgG titers were 110 (84-163) AU/mL, with 0.62 (0.52-0.72) avidity, supporting a long-history of infection. No acute CMV infections were found. In 22.6% (n/N=40/177) of the IgG-positive cases low-level coronary and/or systemic CMV-DNAemia (always <40 copies/mL) was detected. While no differences in peripheral CMV-DNAemia prevalence were observed nor among cases nor controls, coronary CMV-DNAemia was more frequent in acutecases without modifiable CV risk-factors (n/N=4/10; 40.0%), than in chronic-cases (n/N=6/55, 10.9%; p=0.029), or acute-cases with risk-factors (n/N=16/112, 14.3%; p=0.058). Conclusions: CMV-IgG seroprevalence was high in patients with heart diseases. CMV-DNAemia can be found, although uncommonly, in coronary circulation during an ACS, with increased prevalence in older subjects and in absence of CV risk-factors, identifying possible areas for novel interventions.

We expose the phenomenology of the massless dilaton theory in the Solar system for a non universal quadratic coupling between the scalar field which represents the dilaton, and the matter. Modified post-Newtonian equations of motion of an $N$-body system and the light time travel are derived from the action of the theory. We use the physical properties of the main planets of the Solar system to reduce the number of parameters to be tested to 3 in the linear coupling case. In the linear case, we have an universal coupling constant $\alpha_0$ and two coupling constants $\alpha_T$ and $\alpha_G$ related respectively to the telluric bodies and to the gaseous bodies. We then use the planetary ephemeris, INPOP19a, in order to constrain these constants. We succeeded to constrain the linear coupling scenario and the constraints read $\alpha_0=(1.01\pm23.7)\times 10^{-5}$, $\alpha_T=(0.00\pm24.5)\times 10^{-6}$, $\alpha_G=(-1.46\pm12.0)\times 10^{-5}$, at the 99.5 \% C.L., Comment: 29 pages, 6 figures, 5 tables. Might have minor differences with published version

Motivated by recent work on lifelong learning applications for language models (LMs) of code, we introduce CodeLL, a lifelong learning dataset focused on code changes. Our contribution addresses a notable research gap marked by the absence of a long-term temporal dimension in existing code change datasets, limiting their suitability in lifelong learning scenarios. In contrast, our dataset aims to comprehensively capture code changes across the entire release history of open-source software repositories. In this work, we introduce an initial version of CodeLL, comprising 71 machine-learning-based projects mined from Software Heritage. This dataset enables the extraction and in-depth analysis of code changes spanning 2,483 releases at both the method and API levels. CodeLL enables researchers studying the behaviour of LMs in lifelong fine-tuning settings for learning code changes. Additionally, the dataset can help studying data distribution shifts within software repositories and the evolution of API usages over time., Comment: 4+1 pages

IoT systems complexity and susceptibility to failures pose significant challenges in ensuring their reliable operation Failures can be internally generated or caused by external factors impacting both the systems correctness and its surrounding environment To investigate these complexities various modeling approaches have been proposed to raise the level of abstraction facilitating automation and analysis FailureLogic Analysis FLA is a technique that helps predict potential failure scenarios by defining how a components failure logic behaves and spreads throughout the system However manually specifying FLA rules can be arduous and errorprone leading to incomplete or inaccurate specifications In this paper we propose adopting testing methodologies to improve the completeness and correctness of these rules How failures may propagate within an IoT system can be observed by systematically injecting failures while running test cases to collect evidence useful to add complete and refine FLA rules

Since its launch in November 2022, ChatGPT has gained popularity among users, especially programmers who use it as a tool to solve development problems. However, while offering a practical solution to programming problems, ChatGPT should be mainly used as a supporting tool (e.g., in software education) rather than as a replacement for the human being. Thus, detecting automatically generated source code by ChatGPT is necessary, and tools for identifying AI-generated content may need to be adapted to work effectively with source code. This paper presents an empirical study to investigate the feasibility of automated identification of AI-generated code snippets, and the factors that influence this ability. To this end, we propose a novel approach called GPTSniffer, which builds on top of CodeBERT to detect source code written by AI. The results show that GPTSniffer can accurately classify whether code is human-written or AI-generated, and outperforms two baselines, GPTZero and OpenAI Text Classifier. Also, the study shows how similar training data or a classification context with paired snippets helps to boost classification performances.

Background: Since SARS-CoV-2 spread, evidence regarding sex differences in progression and prognosis of COVID-19 have emerged. Besides this, studies on patients' clinical characteristics have described electrolyte imbalances as one of the recurrent features of COVID-19. Methods: We performed a cross-sectional study on all patients admitted to the emergency department (ED) from 1 March to 31 May 2020 who had undergone a blood gas analysis and a nasopharyngeal swab test for SARS-CoV-2 by rtPCR. We defined positive patients as cases (n = 710) and negatives as controls (n = 619), for a total number of patients of 1.329. The study was approved by the local ethics committee Area 3 Milan. Data were automatically extracted from the hospital laboratory SQL-based repository in anonymised form. We considered as outcomes potassium (K+), sodium (Na+), chlorine (Cl−) and calcium (Ca++) as continuous and as categorical variables, in their relation with age, sex and SARS-CoV-2 infection status. Results: We observed a higher prevalence of hypokalaemia among patients positive for SARS-CoV-2 (13.7% vs 6% of negative subjects). Positive patients had a higher probability to be admitted to the ED with hypokalaemia (OR 2.75, 95% CI 1.8-4.1, P <.0001) and women were twice as likely to be affected than men (OR 2.43, 95% CI 1.67-3.54, P <.001). Odds ratios for positive patients to manifest with an alteration in serum Na+ was (OR 1.6, 95% CI 1.17-2.35, P <.001) and serum chlorine (OR 1.6, 95% CI 1.03-2.69, P <.001). Notably, OR for positive patients to be hypocalcaemic was 7.2 (95% CI 4.8-10.6, P <.0001) with a low probability for women to be hypocalcaemic (OR 0.63, 95% CI 0.4-0.8, P =.005). Conclusions: SARS-CoV-2 infection is associated with a higher prevalence of hypokalaemia, hypocalcaemia, hypochloraemia and sodium alterations. Hypokalaemia is more frequent among women and hypocalcaemia among men.

Objective: To evaluate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike (S) IgG antibody production after vaccination with BNT162b2 and the protection from symptomatic breakthrough infections in health care workers. Methods: This prospective observational study (RENAISSANCE) had as a primary end point the evaluation of serologic response to BNT162b2 14 days after a second dose. SARS-CoV-2 anti-S IgG antibodies were evaluated with LIAISON SARS-CoV-2 TrimericS IgG assay (DiaSorin S.p.A.), which is able to detect the presence of both binding and neutralizing antibodies for trimeric spike glycoprotein. Participants were recruited from February 1, 2021, to February 22, 2021. Occurrence of vaccine breakthrough infections was assessed by reverse transcription–polymerase chain reaction on symptomatic and contact cases up to June 6, 2021. Results: Of 2569 staff evaluated, only 4 were nonresponders (0.16%; 95% CI, 0.04% to 0.41%). All 4 nonresponders were severely immunosuppressed and receiving treatment with mycophenolate mofetil or mycophenolic acid. At 14 days after the second dose, 67.5% (1733) of staff had anti-S IgG titers of 2000 BAU/mL or higher; 19.2% (494), between 1500 and 2000 BAU/mL; 9.8% (251), between 1000 and 1500 BAU/mL; and 3.4% (87), 1000 BAU/mL or lower. Women had a higher probability of having higher titers than men (64.5% [1044/1618] vs 58.3% [410/703]; P=.005). This was confirmed after adjustment for age group (odds ratio, 1.275; 95% CI, 1.062 to 1.531; P=.009). Four months after the end of the vaccination program, only 13 participants (0.26%) had experienced a breakthrough SARS-CoV-2 infection, including 1 nonresponder. This was the only participant requiring hospitalization for severe COVID-19. Conclusion: The vaccination campaign among health care workers at the ASST GOM Niguarda has resulted in a marked serologic response and reduction of incident COVID-19 cases. Yet, the lack of protection should not be overlooked in immu

To complement RT-qPCR testing for diagnosis of severe acute respiratory syndrome coro-navirus 2 (SARS-CoV-2) infections, many countries have introduced the use of rapid antigen tests. As they generally display lower real-life performances than expected, their correct positioning as frontline screening is still controversial. Despite the lack of data from daily clinical use, third generation microfluidic assays (such as the LumiraDx SARS-CoV-2 Ag test) have recently been sug-gested to have similar performances to RT-qPCR and have been proposed as alternative diagnostic tools. By analyzing 960 nasopharyngeal swabs from 960 subjects at the emergency department ad-missions of a tertiary COVID-19 hospital, LumiraDx assay demonstrated a specificity of 97% (95% CI: 96–98), and a sensitivity of 85% (95% CI: 82–89) in comparison with RT-qPCR, which increases to 91% (95% CI: 86–95) for samples with a cycle threshold ≤ 29. Fifty false-negative LumiraDx-results were confirmed by direct quantification of genomic SARS-CoV-2 RNA through droplet-digital PCR (median (IQR) load = 5880 (1657–41,440) copies/mL). Subgenomic N and E RNAs were detected in 52% (n = 26) and 56% (n = 28) of them, respectively, supporting the presence of active viral replica-tion. Overall, the LumiraDx test complies with the minimum performance requirements of the WHO. Yet, the risk of a misrecognition of patients with active COVID-19 persists, and the need for confirmatory RT-qPCR should not be amended.

Although children and young adults with Hodgkin's lymphoma usually have a favorable prognosis, patients with primary refractory disease and some subsets of relapsed patients still have a dismal outcome. Brentuximab vedotin (BV) in combination with bendamustine may represent a suitable salvage therapy; data on 32 patients aged less than 25 years were retrospectively analyzed. Patients received up to six cycles of treatment of BV 1.8 mg/kg on day 1 and bendamustine 90–120 mg/m2 on days 2 and 3. At the end of treatment, the overall response rate was 81%. The 3-year overall and progression-free survivals are 78.1% and 67%, respectively.

Background: Diffuse intrinsic pontine glioma (DIPG) is a fatal disease with a median overall survival (OS) of less than 12 months after diagnosis. Radiotherapy (RT) still remains the mainstay treatment. Several other therapeutic strategies have been attempted in the last years without a significant effect on OS. Although radiological imaging is the gold standard for DIPG diagnosis, the urgent need to improve the survival has led to the reconsideration of biopsy with the aim to better understand the molecular profile of DIPG and support personalized treatment. Methods: In this study, we present a single-center experience in treating DIPG patients at disease progression combining targeted therapies with standard of care. Biopsy was proposed to all patients at diagnosis or disease progression. First-line treatment included RT and nimotuzumab/vinorelbine or temozolomide. Immunohistochemistry-targeted research included study of mTOR/p-mTOR pathway and BRAFv600E. Molecular analyses included polymerase chain reaction, followed by Sanger sequences and/or next-generation sequencing. Results: Based on the molecular profile, targeted therapy was administered in 9 out of 25 patients, while the remaining 16 patients were treated with standard of care. Personalized treatment included inhibition of the PI3K/AKT/mTOR pathway (5/9), PI3K/AKT/mTOR pathway and BRAFv600E (1/9), ACVR1 (2/9) and PDGFRA (1/9); no severe side effects were reported during treatment. Response to treatment was evaluated according to Response Assessment in Pediatric Neuro-Oncology criteria, and the overall response rate within the cohort was 66%. Patients treated with targeted therapies were compared with the control cohort of 16 patients. Clinical and pathological characteristics of the two cohorts were homogeneous. Median OS in the personalized treatment and control cohort was 20.26 and 14.18 months, respectively (p = 0.032). In our experience, the treatment associated with the best OS was everolimus. Conclu

GitHub is the world's largest host of source code, with more than 150M repositories. However, most of these repositories are not labeled or inadequately so, making it harder for users to find relevant projects. There have been various proposals for software application domain classification over the past years. However, these approaches lack a well-defined taxonomy that is hierarchical, grounded in a knowledge base, and free of irrelevant terms. This work proposes GitRanking, a framework for creating a classification ranked into discrete levels based on how general or specific their meaning is. We collected 121K topics from GitHub and considered $60\%$ of the most frequent ones for the ranking. GitRanking 1) uses active sampling to ensure a minimal number of required annotations; and 2) links each topic to Wikidata, reducing ambiguities and improving the reusability of the taxonomy. Our results show that developers, when annotating their projects, avoid using terms with a high degree of specificity. This makes the finding and discovery of their projects more challenging for other users. Furthermore, we show that GitRanking can effectively rank terms according to their general or specific meaning. This ranking would be an essential asset for developers to build upon, allowing them to complement their annotations with more precise topics. Finally, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked with a minimum number of annotations ($\sim$ 15). This paper is the first collective attempt to build a ground-up taxonomy of software domains., Comment: 11 pages, 6 figures, 3 tables

Privacy and ethics of citizens are at the core of the concerns raised by our increasingly digital society. Profiling users is standard practice for software applications triggering the need for users, also enforced by laws, to properly manage privacy settings. Users need to manage software privacy settings properly to protect personally identifiable information and express personal ethical preferences. AI technologies that empower users to interact with the digital world by reflecting their personal ethical preferences can be key enablers of a trustworthy digital society. We focus on the privacy dimension and contribute a step in the above direction through an empirical study on an existing dataset collected from the fitness domain. We find out which set of questions is appropriate to differentiate users according to their preferences. The results reveal that a compact set of semantic-driven questions (about domain-independent privacy preferences) helps distinguish users better than a complex domain-dependent one. This confirms the study's hypothesis that moral attitudes are the relevant piece of information to collect. Based on the outcome, we implement a recommender system to provide users with suitable recommendations related to privacy choices. We then show that the proposed recommender system provides relevant settings to users, obtaining high accuracy., Comment: The paper consists of 37 pages, 11 figures

Model Driven Engineering (MDE) has been widely applied in software development, aiming to facilitate the coordination among various stakeholders. Such a methodology allows for a more efficient and effective development process. Nevertheless, modeling is a strenuous activity that requires proper knowledge of components, attributes, and logic to reach the level of abstraction required by the application domain. In particular, metamodels play an important role in several paradigms, and specifying wrong entities or attributes in metamodels can negatively impact on the quality of the produced artifacts as well as other elements of the whole process. During the metamodeling phase, modelers can benefit from assistance to avoid mistakes, e.g., getting recommendations like meta-classes and structural features relevant to the metamodel being defined. However, suitable machinery is needed to mine data from repositories of existing modeling artifacts and compute recommendations. In this work, we propose MemoRec, a novel approach that makes use of a collaborative filtering strategy to recommend valuable entities related to the metamodel under construction. Our approach can provide suggestions related to both metaclasses and structured features that should be added in the metamodel under definition. We assess the quality of the work with respect to different metrics, i.e., success rate, precision, and recall. The results demonstrate that MemoRec is capable of suggesting relevant items given a partial metamodel and supporting modelers in their task., Comment: Accepted for publication at the International Journal on Software and Systems Modeling (SoSyM)

During the development of a software project, developers often need to upgrade third-party libraries (TPLs), aiming to keep their code up-to-date with the newest functionalities offered by the used libraries. In most cases, upgrading used TPLs is a complex and error-prone activity that must be carefully carried out to limit the ripple effects on the software project that depends on the libraries being upgraded. In this paper, we propose EvoPlan as a novel approach to the recommendation of different upgrade plans given a pair of library-version as input. In particular, among the different paths that can be possibly followed to upgrade the current library version to the desired updated one, EvoPlan is able to suggest the plan that can potentially minimize the efforts being needed to migrate the code of the clients from the library's current release to the target one. The approach has been evaluated on a curated dataset using conventional metrics used in Information Retrieval, i.e., precision, recall, and F-measure. The experimental results show that EvoPlan obtains an encouraging prediction performance considering two different criteria in the plan specification, i.e., the popularity of migration paths and the number of open and closed issues in GitHub for those projects that have already followed the recommended migration paths.

We present here the new results obtained with the INPOP planetary ephemerides and BepiColombo radio-science simulations. We give new constraints for the classic General Relativity tests in terms of violation of the PPN parameters $\beta$ and $\gamma$ and the time variation of the gravitational constant G. We also present new limits for the mass of the graviton and finally we obtain new acceptable intervals for the dilaton parameters $\alpha_{0}$, $\alpha_{T}$ and $\alpha_{G}$. Besides these tests of gravitation, we also study the possibility of detecting the Sun core rotation., Comment: arXiv admin note: substantial text overlap with arXiv:2111.04499

During the development phase, software programmers usually introduce code that contains issues intentionally left for additional treatment. To allow for future fixing, they mark such code using textual comments, resulting in Self-Admitted Technical Debt (SATD). Detecting SATD contained in source code has become crucial in the development cycle since it helps program-mers locate issues that need to be solved, thus improving code quality. We introduce PILOT, a technical debt detector built on top of a combination of different natural language processing (NLP) and machine learning (ML) techniques. First, the semantic among SATD comments is captured using feature extraction steps. Then, neural network algorithms are applied to classify comments, represented as vectors. We built a PILOT prototype with a feed-forward neural network and evaluated it using real-world datasets as proof of concept. The empirical evaluation shows that PILOT obtains an encouraging performance and outperforms a well-established baseline. We anticipate that our tool will come in handy, as once being embedded in the IDE, it can help developers recognize SATD manifested in their code, allowing them to conveniently identify and fix issues.

Background: Valproic acid (VPA) is an antiepileptic drug, used for focal and generalized seizure. VPA treatment resulted in significant weight gain but there are no systematic data about the prevalence of this side effect. The aim of the study is to evaluate the weight gain of a paediatric population with epilepsy. Methods: We enrolled 38 patients, 17 female and 21 male with a mean age of 8.2±4.4 years. We evaluated data about height, weight and BMI at beginning of treatment and at 24, 36 and 48 months of follow-up. Results: There is a statistical significant difference between the percentile value of weight and BMI at baseline and at 36 and 48 months of follow-up (P value <0.01) but there isn't statistical significant difference between the percentile value of height (P value 0.22 and 0.18). Conclusions: We believe that a nutritional support should be guaranteed to the pediatric patients with epilepsy that begin the VPA therapy.

Luigi Di Ruscio tisse un roman protéiforme sur ses années de jeunesse dans l'Italie de la Seconde Guerre mondiale jusqu'aux années 1960, entre fascisme, résistance et reconstruction. Poète autodidacte, il met en scène joies buissonnières et déboires scolaires, jeunesse vagabonde et furibonde avec son compagnon d'infortune Moscatritata, vendeur de christs en plâtre pulve?risés de faux bronze. Instituteurs et curés, éditeurs et critiques : toutes les autorités seront bafouées dans un texte qui se moque des règles, qui se joue de la langue et des mots ; le roman de formation du jeune écrivain se fait alors manifeste poétique et autobiographie littéraire. Né à Fermo dans les Marches en 1930 et mort à Oslo en 2011, Luigi Di Ruscio publie son premier recueil de poe?sie a? 23 ans, Non possiamo abituarci a morire (Nous ne nous habituerons pas a? mourir, pre?face de Franco Fortini). Son second recueil, Le streghe s'arrotano le dentiere (préfacé par Quasimodo), paraît 13 ans plus tard. Entre-temps, le poète a émigré a? Oslo et trouvé un emploi dans une usine sidérurgique. Il a rencontré Mary Standberg, avec qui il aura quatre enfants. Employé à l'usine pendant près de quarante ans, Di Ruscio retrouvait sa machine à écrire chaque soir pour composer ses dix recueils de poe?sie et trois ouvrages en prose : Palmiro, Christs pulvérisés et La Neige noire d'Oslo.

Luigi Di Ruscio tisse un roman protéiforme sur ses années de jeunesse dans l'Italie de la Seconde Guerre mondiale jusqu'aux années 1960, entre fascisme, résistance et reconstruction. Poète autodidacte, il met en scène joies buissonnières et déboires scolaires, jeunesse vagabonde et furibonde avec son compagnon d'infortune Moscatritata, vendeur de christs en plâtre pulve?risés de faux bronze. Instituteurs et curés, éditeurs et critiques : toutes les autorités seront bafouées dans un texte qui se moque des règles, qui se joue de la langue et des mots ; le roman de formation du jeune écrivain se fait alors manifeste poétique et autobiographie littéraire. Né à Fermo dans les Marches en 1930 et mort à Oslo en 2011, Luigi Di Ruscio publie son premier recueil de poe?sie a? 23 ans, Non possiamo abituarci a morire (Nous ne nous habituerons pas a? mourir, pre?face de Franco Fortini). Son second recueil, Le streghe s'arrotano le dentiere (préfacé par Quasimodo), paraît 13 ans plus tard. Entre-temps, le poète a émigré a? Oslo et trouvé un emploi dans une usine sidérurgique. Il a rencontré Mary Standberg, avec qui il aura quatre enfants. Employé à l'usine pendant près de quarante ans, Di Ruscio retrouvait sa machine à écrire chaque soir pour composer ses dix recueils de poe?sie et trois ouvrages en prose : Palmiro, Christs pulvérisés et La Neige noire d'Oslo.

Luigi Di Ruscio tisse un roman protéiforme sur ses années de jeunesse dans l'Italie de la Seconde Guerre mondiale jusqu'aux années 1960, entre fascisme, résistance et reconstruction. Poète autodidacte, il met en scène joies buissonnières et déboires scolaires, jeunesse vagabonde et furibonde avec son compagnon d'infortune Moscatritata, vendeur de christs en plâtre pulve?risés de faux bronze. Instituteurs et curés, éditeurs et critiques : toutes les autorités seront bafouées dans un texte qui se moque des règles, qui se joue de la langue et des mots ; le roman de formation du jeune écrivain se fait alors manifeste poétique et autobiographie littéraire. Né à Fermo dans les Marches en 1930 et mort à Oslo en 2011, Luigi Di Ruscio publie son premier recueil de poe?sie a? 23 ans, Non possiamo abituarci a morire (Nous ne nous habituerons pas a? mourir, pre?face de Franco Fortini). Son second recueil, Le streghe s'arrotano le dentiere (préfacé par Quasimodo), paraît 13 ans plus tard. Entre-temps, le poète a émigré a? Oslo et trouvé un emploi dans une usine sidérurgique. Il a rencontré Mary Standberg, avec qui il aura quatre enfants. Employé à l'usine pendant près de quarante ans, Di Ruscio retrouvait sa machine à écrire chaque soir pour composer ses dix recueils de poe?sie et trois ouvrages en prose : Palmiro, Christs pulvérisés et La Neige noire d'Oslo.

Luigi Di Ruscio tisse un roman protéiforme sur ses années de jeunesse dans l'Italie de la Seconde Guerre mondiale jusqu'aux années 1960, entre fascisme, résistance et reconstruction. Poète autodidacte, il met en scène joies buissonnières et déboires scolaires, jeunesse vagabonde et furibonde avec son compagnon d'infortune Moscatritata, vendeur de christs en plâtre pulve?risés de faux bronze. Instituteurs et curés, éditeurs et critiques : toutes les autorités seront bafouées dans un texte qui se moque des règles, qui se joue de la langue et des mots ; le roman de formation du jeune écrivain se fait alors manifeste poétique et autobiographie littéraire. Né à Fermo dans les Marches en 1930 et mort à Oslo en 2011, Luigi Di Ruscio publie son premier recueil de poe?sie a? 23 ans, Non possiamo abituarci a morire (Nous ne nous habituerons pas a? mourir, pre?face de Franco Fortini). Son second recueil, Le streghe s'arrotano le dentiere (préfacé par Quasimodo), paraît 13 ans plus tard. Entre-temps, le poète a émigré a? Oslo et trouvé un emploi dans une usine sidérurgique. Il a rencontré Mary Standberg, avec qui il aura quatre enfants. Employé à l'usine pendant près de quarante ans, Di Ruscio retrouvait sa machine à écrire chaque soir pour composer ses dix recueils de poe?sie et trois ouvrages en prose : Palmiro, Christs pulvérisés et La Neige noire d'Oslo.

Climate change affects human health; however, there have been no large-scale, systematic efforts to quantify the heat-related human health impacts that have already occurred due to climate change. Here, we use empirical data from 732 locations in 43 countries to estimate the mortality burdens associated with the additional heat exposure that has resulted from recent human-induced warming, during the period 1991–2018. Across all study countries, we find that 37.0% (range 20.5–76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change and that increased mortality is evident on every continent. Burdens varied geographically but were of the order of dozens to hundreds of deaths per year in many locations. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public health impacts of climate change. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

Background: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. Methods: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. Results: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 ºC decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 ºC) to continental (19.3 ºC), temperate (21.7 ºC), arid (24.5 ºC), and tropical (26.5 ºC). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 ºC for a 1 ºC rise in a community's annual mean temperature, and by 1 ºC for a 1 ºC rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 ºC rise in a community's annual mean temperature and by 1.3 for a 1 ºC rise in its SD. Conclusions: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation. Copyright © 2021 The Authors.

Background: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. Methods: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature–mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature–mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. Findings: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967–5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58–11·07) of all deaths (8·52% [6·19–10·47] were cold-related and 0·91% [0·56–1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60–87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000–03 to 2016–19, the global cold-related excess death ratio changed by −0·51 percentage points (95% eCI −0·61 to −0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13–0·31), leading to

Climate change affects human health; however, there have been no large-scale, systematic efforts to quantify the heat-related human health impacts that have already occurred due to climate change. Here, we use empirical data from 732 locations in 43 countries to estimate the mortality burdens associated with the additional heat exposure that has resulted from recent human-induced warming, during the period 1991–2018. Across all study countries, we find that 37.0% (range 20.5–76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change and that increased mortality is evident on every continent. Burdens varied geographically but were of the order of dozens to hundreds of deaths per year in many locations. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public health impacts of climate change.

We give here a detailed description of the latest INPOP planetary ephemerides INPOP20a. We test the sensitivity of the Sun oblateness determination obtained with INPOP to different models for the Sun core rotation. We also present new evaluations of possible GRT violations with the PPN parameters $\beta$,$\gamma$ and $\dot{\mu}/\mu$. With a new method for selecting acceptable alternative ephemerides we provide conservative limits of about $7.16 \times 10^{-5}$ and $7.49 \times 10^{-5}$ for $\beta-1$ and $\gamma-1$ respectively using the present day planetary data samples. We also present simulations of Bepi-Colombo range tracking data and their impact on planetary ephemeris construction. We show that the use of future BC range observations should improve these estimates, in particular $\gamma$. Finally, interesting perspectives for the detection of the Sun core rotation seem to be reachable thanks to the BC mission and its accurate range measurements in the GRT frame., Comment: Proceedings of the IAU Symposium 364 "Multi-scale dynamics of space objects"

Background: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. Methods: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. Results: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 ºC decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 ºC) to continental (19.3 ºC), temperate (21.7 ºC), arid (24.5 ºC), and tropical (26.5 ºC). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 ºC for a 1 ºC rise in a community's annual mean temperature, and by 1 ºC for a 1 ºC rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 ºC rise in a community's annual mean temperature and by 1.3 for a 1 ºC rise in its SD. Conclusions: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation.

Background: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. Methods: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature–mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature–mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. Findings: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967–5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58–11·07) of all deaths (8·52% [6·19–10·47] were cold-related and 0·91% [0·56–1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60–87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000–03 to 2016–19, the global cold-related excess death ratio changed by −0·51 percentage points (95% eCI −0·61 to −0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13–0·31), leading

Climate change affects human health; however, there have been no large-scale, systematic efforts to quantify the heat-related human health impacts that have already occurred due to climate change. Here, we use empirical data from 732 locations in 43 countries to estimate the mortality burdens associated with the additional heat exposure that has resulted from recent human-induced warming, during the period 1991–2018. Across all study countries, we find that 37.0% (range 20.5–76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change and that increased mortality is evident on every continent. Burdens varied geographically but were of the order of dozens to hundreds of deaths per year in many locations. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public health impacts of climate change.

Background: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. Methods: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. Results: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 °C decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 °C) to continental (19.3 °C), temperate (21.7 °C), arid (24.5 °C), and tropical (26.5 °C). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 °C for a 1 °C rise in a community's annual mean temperature, and by 1 °C for a 1 °C rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 °C rise in a community's annual mean temperature and by 1.3 for a 1 °C rise in its SD. Conclusions: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation.

Background: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. Methods: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. Results: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 °C decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 °C) to continental (19.3 °C), temperate (21.7 °C), arid (24.5 °C), and tropical (26.5 °C). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 °C for a 1 °C rise in a community's annual mean temperature, and by 1 °C for a 1 °C rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 °C rise in a community's annual mean temperature and by 1.3 for a 1 °C rise in its SD. Conclusions: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation.

Background: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. Methods: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. Results: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 °C decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 °C) to continental (19.3 °C), temperate (21.7 °C), arid (24.5 °C), and tropical (26.5 °C). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 °C for a 1 °C rise in a community's annual mean temperature, and by 1 °C for a 1 °C rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 °C rise in a community's annual mean temperature and by 1.3 for a 1 °C rise in its SD. Conclusions: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation.

Gravity, geodesy and fundamental physics with BepiColombo’s MORE investigation for understanding Mercury's interior.

BACKGROUND: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. METHODS: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. FINDINGS: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to

BACKGROUND: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. METHODS: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. FINDINGS: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to

BACKGROUND: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. METHODS: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. FINDINGS: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to

Background: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. Methods: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. Results: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 °C decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 °C) to continental (19.3 °C), temperate (21.7 °C), arid (24.5 °C), and tropical (26.5 °C). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 °C for a 1 °C rise in a community’s annual mean temperature, and by 1 °C for a 1 °C rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 °C rise in a community’s annual mean temperature and by 1.3 for a 1 °C rise in its SD. Conclusions: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation., Environmental Epidemiology, 5(5), e169; 2021

Background: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. Methods: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature–mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature–mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. Findings: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967–5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58–11·07) of all deaths (8·52% [6·19–10·47] were cold-related and 0·91% [0·56–1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60–87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000–03 to 2016–19, the global cold-related excess death ratio changed by −0·51 percentage points (95% eCI −0·61 to −0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13–0·31), l

Massimo Bellini,1 Ginevra Berti,1 Leonilde Bonfrate,2 Flavia Ciranni,1 Agostino Di Ciaula,2 Mirko Di Ruscio,3 Alessandra Dell’Era,4,5 Christian Lambiase,1 Antonino Noto,2 Andrea Pancetti,1 Piero Portincasa,2 Francesco Rettura1 1Gastrointestinal Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy; 2Clinica Medica A. Murri, Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro Medical School, Bari, Italy; 3IBD Unit, IRCCS Ospedale Sacro Cuore-Don Calabria, Negrar di Valpolicella, Verona, Italy; 4Department of Biomedical and Clinical Sciences, “Luigi Sacco”, University of Milan, Milan, Italy; 5Gastroenterology and Endoscopy Unit – ASST Fatebenefratelli Sacco, Milan, ItalyCorrespondence: Francesco RetturaGastrointestinal Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, ItalyTel +39 50 997411Fax +39 50 997412Email rettura.fra@gmail.comAbstract: Irritable bowel syndrome is a chronic functional gastrointestinal disorder characterized by recurrent chronic abdominal pain and impaired bowel habits, which affects daily activity and work productivity, and is associated with a significant healthcare economic burden as well as an impaired quality of life and psycho-affective profile. Management of patients is a great challenge for physicians; at the present, the therapeutic strategy aimed to treat the different symptoms, and no medical therapy is proven to modify the natural history of the disease. GELSECTAN® (xyloglucan, pea protein and tannins, xylo-oligosaccharides) is a medical device with both protective and prebiotic actions on the intestinal mucosa, able to restore intestinal permeability and to improve gastrointestinal symptoms, controlling diarrhoea, abdominal pain and bloating in adult patients with irritable bowel syndrome. We report and discuss four cases of different patients with irritable bowel

Domain-specific modelling helps tame the complexity of today's application domains by formalizing concepts and their relationships in modelling languages. While meta-editors are widely-used frameworks for implementing graphical editors for such modelling languages, they are best suitable for defining {novel} topological notations, i.e., syntaxes where the model layout does not contribute to the model semantics. In contrast, many engineering fields, e.g., railways systems or electrical engineering, use notations that, on the one hand, are standard and, on the other hand, are demanding more expressive power than topological syntaxes. In this paper, we discuss the problem of enhancing the expressiveness of modelling editors towards geometric/positional syntaxes. Several potential solutions are experimentally implemented on the jjodel web-based platform with the aim of identifying challenges and opportunities.

Background: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. Methods: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature–mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature–mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. Findings: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967–5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58–11·07) of all deaths (8·52% [6·19–10·47] were cold-related and 0·91% [0·56–1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60–87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000–03 to 2016–19, the global cold-related excess death ratio changed by −0·51 percentage points (95% eCI −0·61 to −0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13–0·31), leading to