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Introduction: Appendiceal neuroendocrine neoplasms (aNENs) are the most common malignant appendiceal neoplasms. Localized aNENs are typically managed with an appendectomy; however, right colectomy may be necessary in patients with a high risk of nodal disease. However, the role of right hemicolectomy and the optimal surveillance strategy, particularly for tumors between 1 and 2 cm, remains controversial. Material and Methods: This retrospective, observational study evaluated patients diagnosed with aNENs between January 1995 and July 2015 at two tertiary centers in Ireland and Italy. Data were extracted from a prospectively maintained registry and included clinical, pathological, and therapeutic variables, as well as follow-up outcomes. Results: Forty-three patients (41.8% male; median age 27.5 years) were included, with a median follow-up of 49 months. The median tumor size was 6.4 mm (range: 0.6–40 mm). The majority were G1 tumors (58%), and staging distribution was predominantly Stage I (60%). While no significant differences in demographics or tumor features were observed between centers, completion right hemicolectomies were more frequent in the Irish cohort (p = 0.04). Follow-up practices varied, with more intensive imaging and biochemical monitoring observed in the Italian cohort. Overall prognosis was excellent, with a single case of recurrence during the study period. Conclusions: Most aNENs are effectively managed with appendectomy alone, and routine follow-up may be unnecessary in the absence of adverse pathological features. Accurate risk stratification, driven by comprehensive histopathological assessment, is critical for optimizing management and surveillance strategies.

In dynamic networks, the presence of ties are subject both to endogenous network dependencies and spatial dependencies. Current statistical models for change over time are typically defined relative to some initial condition, thus skirting the issue of where the first network came from. Additionally, while these longitudinal network models may explain the dynamics of change in the network over time, they do not explain the change in those dynamics. We propose an extension to the longitudinal exponential random graph model that allows for simultaneous inference of the changes over time and the initial conditions, as well as relaxing assumptions of time-homogeneity. Estimation draws on recent Bayesian approaches for cross-sectional exponential random graph models and Bayesian hierarchical models. This is developed in the context of foreign direct investment relations in the global electricity industry in 1995–2003. International investment relations are known to be affected by factors related to: (i) the initial conditions determined by the geographical locations; (ii) time-dependent fluctuations in the global intensity of investment flows; and (iii) endogenous network dependencies. We rely on the well-known gravity model used in research on international trade to represent how spatial embedding and endogenous network dependencies jointly shape the dynamics of investment relations.

Bronchial neuroendocrine tumors (NET) are classified into well-differentiated typical carcinoids (TC), atypical carcinoids (AC), large cell neuroendocrine carcinomas (LCNEC), and small cell lung carcinomas (SCLC). We retrospectively reviewed and analyzed the diagnostic and therapeutic aspects, follow-up data, and outcomes of all patients diagnosed with a bronchial NET from 1995 to 2015 at our institution. Patients with LCNEC or SCLC were excluded due to the biological and clinical differences from the other bronchial NET. The clinical, laboratory, imaging, treatment, and follow-up data were collected and analyzed keeping in mind the recently published international recommendations. Forty-six patients were included in the study. Of these, 37 had a TC and 5 an AC. In 4 patients, the histological characterization was inadequate. Forty-four patients underwent surgery. Four patients developed metastatic disease. Interestingly, 14 patients had one or more other tumors diagnosed at some stage and 3 of them had three different tumors. A total of 7 patients died. The analysis of the laboratory and pathology assessment identified some inconsistencies when compared to the international recommendations. Although the treatment of bronchial NET at our institution was consistent with the successively published recommendations, it appears that the diagnostic process and the follow-up surveillance were not. We think that a systematic multidisciplinary approach might improve bronchial NET patient care. A relatively high rate of occurrence of a second, or also a third, non-NET tumor was observed, though the statistical value of such observation could not be exhaustively elucidated in this numerically limited patient population. In our opinion, the observed high rate of second malignancies in this patient cohort highlights the necessity of optimizing the follow-up of the bronchial NET patients, also considering the very good survival rate achieved with regard to the bronchial NET.

Moving from the scientific literature on the evaluation of environmental projects and programs, this study identifies how and under which conditions collaborations in environmentally sustainable projects are considered effective for the adaptive governance of SES. The method adopted is a systematic literature review based on the quantitative and qualitative analysis of 56 articles selected through specific queries on the SCOPUS database and published from 2004 to 2020. Results of the quantitative analysis identify conditions able to evaluate collaborations, highlighting the need to adopt a transdisciplinary approach analysing both social and ecological challenges and assessing both social and ecological results. Moreover, they suggest preferring using primary data involving multi-sector and multi-scale actors and enlarging the geographical context to the most vulnerable countries. The results of the qualitative analysis provide specific recommendations for collaborations being effective when related to communication, equity, foresight, and respect, which need to be further strengthened by all actors. Multiplicity in visions and approaches should be seen as a resource able to stimulate creativity in social arrangements and environmental practices, making collaborations in environmental projects instrumental for the effectiveness of adaptive governance of SES.

Moving from the scientific literature on the evaluation of environmental projects and programs, this study identifies how and under which conditions collaborations in environmentally sustainable projects are considered effective for the adaptive governance of SES. The method adopted is a systematic literature review based on the quantitative and qualitative analysis of 56 articles selected through specific queries on the SCOPUS database and published from 2004 to 2020. Results of the quantitative analysis identify conditions able to evaluate collaborations, highlighting the need to adopt a transdisciplinary approach analysing both social and ecological challenges and assessing both social and ecological results. Moreover, they suggest preferring using primary data involving multi-sector and multi-scale actors and enlarging the geographical context to the most vulnerable countries. The results of the qualitative analysis provide specific recommendations for collaborations being effective when related to communication, equity, foresight, and respect, which need to be further strengthened by all actors. Multiplicity in visions and approaches should be seen as a resource able to stimulate creativity in social arrangements and environmental practices, making collaborations in environmental projects instrumental for the effectiveness of adaptive governance of SES.

Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as in studies on neurodevelopment or brain diseases. Furthermore, descriptive neural network analyses lack an appropriate generic null model and a unifying framework. These issues may be solved with an alternative framework based on a Bayesian generative modeling approach, i.e. Bayesian exponential random graph modeling (ERGM), which explains an observed network by the joint contribution of local network structures or features (for which we chose neurobiologically meaningful constructs such as connectedness, local clustering or global efficiency). We aimed to identify how these local network structures (or features) are evolving across the life-span, and how sensitive these features are to random and targeted lesions. To that aim we applied Bayesian exponential random graph modeling on structural networks derived from whole-brain diffusion tensor imaging-based tractography of 382 healthy adult subjects (age range: 20.2–86.2years), with and without lesion simulations. Networks were successfully generated from four local network structures that resulted in excellent goodness-of-fit, i.e. measures of connectedness, local clustering, global efficiency and intrahemispheric connectivity. We found that local structures (i.e. connectedness, local clustering and global efficiency), which give rise to the global network topology, were stable even after lesion simulations across the lifespan, in contrast to overall descriptive network changes – e.g. lower network density and higher clustering – during aging, and despite clear effects of hub damage on network topologies. Our study demonstrates the potential of Bayesian generative modeling to characterize the underlying network structures that drive the brain's global network topology at different developmental stages and/or under pathological conditions. •Bayesian ERGM can characterize brain networks based on a few local structures.•Local structures that shape the global network topology are stable across lifespan.•Local network structures are robust to simulated random and hub node damage.

Aim: Pancreatic neuroendocrine tumors (pNETs) can occur in patients with a familial syndrome either as multiple endocrine neoplasia type 1 (MEN-1) or as sporadic tumors. Endoscopic ultrasound (EUS) has become one of the first-line investigations for pNET characterization. The ultrasonographic features of pNETs may differ depending on the familial versus sporadic pathogenesis of the tumor. Therefore, the EUS findings could help and direct the definition of a pNET with an impact on the most appropriate diagnostic and therapeutic patient management. Methods: In this single-center retrospective study, we reviewed the EUS features of 94 pNETs from 37 MEN-1 patients and 15 pNETs from 11 sporadic disease patients at the time of their first EUS assessment. We analyzed the most relevant morphological and ultrasonographic characteristics of the tumors and compared the findings between the 2 patient groups. Results: Patients with MEN-1 more likely present with multiple pNETs than patients with sporadic disease. Sporadic pNETs are usually much bigger than those due to MEN-1. Moreover, pNETs are more heterogeneous in patients with sporadic disease than in those with MEN-1. No statistical difference with regard to definition of the margins, morphology, and vascularization of the pNETs appears between the 2 groups. Conclusions: Patients with sporadic disease usually present with bigger and more heterogeneous pNETs than patients with MEN-1, who tend to present with a higher number of lesions. EUS can facilitate the precise characterization of a pNET, and the ultrasonographic features of the lesion can help and distinguish MEN-1-related versus sporadic disease.

A new modelling approach for the analysis of weighted networks with ordinal/polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network connectivity structure using a hierarchical multilayer exponential random graph model (ERGM) generative process where each network layer represents a different ordinal dyadic category. The network layers are assumed to be generated by an ERGM process conditional on their closest lower network layers. A crucial advantage of the proposed method is the possibility of adopting the binary network statistics specification to describe both the between-layer and across-layer network processes and thus facilitating the interpretation of the parameter estimates associated to the network effects included in the model. The Bayesian approach provides a natural way to quantify the uncertainty associated to the model parameters. From a computational point of view, an extension of the approximate exchange algorithm is proposed to sample from the doubly-intractable parameter posterior distribution. A simulation study is carried out on artificial data and applications of the methodology are illustrated on well-known datasets. Finally, a goodness-of-fit diagnostic procedure for model assessment is proposed.

The Bergm package provides a comprehensive framework for Bayesian inference using Markov chain Monte Carlo (MCMC) algorithms. It can also supply graphical Bayesian goodness-of-fit procedures that address the issue of model adequacy. The package is simple to use and represents an attractive way of analysing network data as it offers the advantage of a complete probabilistic treatment of uncertainty. Bergm is based on the ergm package and therefore it makes use of the same model set-up and network simulation algorithms. The Bergm package has been continually improved in terms of speed performance over the last years and now offers the end-user a feasible option for carrying out Bayesian inference for networks with several thousands of nodes.

Interest in statistical network analysis has grown massively in recent decades and its perspective and methods are now widely used in many scientific areas that involve the study of various types of networks for representing structure in many complex relational systems such as social relationships, information flows, and protein interactions. Social network analysis is based on the study of social relations between actors so as to understand the formation of social structures by the analysis of basic local relations. Statistical models have started to play an increasingly important role because they give the possibility to explain the complexity of social behaviour and to investigate issues on how the global features of an observed network may be related to local network structures. In this chapter, we review some of the most recent computational advances in the rapidly expanding field of statistical social network analysis using the R open‐source software.