Mapping job complexity and skills into wages

We use algorithmic and network-based tools to build and analyze the bipartite network connecting jobs with the skills they require. We quantify and represent the relatedness between jobs and skills by using statistically validated networks. Using the fitness and complexity algorithm, we compute a skill-based complexity of jobs. This quantity is positively correlated with the average salary, abstraction, and non-routinarity level of jobs. Furthermore, coherent jobs - defined as the ones requiring closely related skills - have, on average, lower wages. We find that salaries may not always reflect the intrinsic value of a job, but rather other wage-setting dynamics that may not be directly related to its skill composition. Our results provide valuable information for policymakers, employers, and individuals to better understand the dynamics of the labor market and make informed decisions about their careers

Burnout en trabajadores de la salud. Una comparación entre médicos, enfermeras, cargos administrativos y técnicos

Introduction: Healthcare workers (HCW) report higher levels of anxiety, depression, burnout, compared to the general population. The severe global health crisis caused by the coronavirus SARS-CoV-2 brought even more burden to HCW. Objective: To assessed burnout as a whole and in its different domains among HCW in a medical centerMethods: We performed a cross-sectional study examining the association between demographic characteristics, healthcare position and feeling burned out. Data was collected through an anonymous online survey. We utilized the Maslach Survey for Medical Personnel in Spanish. Descriptive analyses summarized age, gender, job role, number of jobs, time in the organization and working in a COVID-19 exposed area. Ji2 tests were used to analyze association between variables and burnout. A multivariate logistic regression analysis was used to identify independent predictors of any of the burnout domains: emotional exhaustion (EE), depersonalization (DP) and/or personal accomplishment (PA). Results: 185/852 subjects answered the survey (21.7%); 79 subjects reported EE (42.7%), 61 (32.9%) DP and 31 (16.7%) PA; 98 (52.4%) had at least one component of high burnout for the dimensions analyzed. Logistics regression shows that female gender (OR= 2.21; 95% CI: 1.12-4.39), administrative positions (OR= 18.61; 95% CI: 4.28-80.93), physicians (OR= 13.27; 95% CI 3.55-49.86), and nurses (OR= 6.55; 95% CI: 1.58-27.14) were strongly associated with the presence of any burnout domain. Conclusions: The overall burnout prevalence was in range with international studies. Female workers, administrative positions, physicians and nurses were identified as independent predictors of burnout.Introducción: Los trabajadores de la salud (TS) informan niveles más altos de ansiedad, depresión y agotamiento, en comparación con la población general. La grave crisis sanitaria mundial provocada por el coronavirus SARS-CoV-2 supuso una carga aún mayor para los trabajadores sanitarios.Objetivo: Evaluar el burnout en su conjunto y en sus diferentes dominios entre los TS en un centro médicoMétodos: Realizamos un estudio transversal donde se examinó la asociación entre las características demográficas, el puesto de atención médica y la sensación de agotamiento. Los datos se recopilaron a través de una encuesta anónima en línea. Utilizamos la Encuesta en español Maslach para Personal Médico. Los análisis resumieron la edad, el género, el rol laboral, la cantidad de trabajos, el tiempo en la organización y el trabajo en un área expuesta a COVID-19. Se utilizaron pruebas de Ji2 para analizar la asociación entre las variables y el burnout. Se utilizó un análisis de regresión logística multivariante para identificar predictores independientes de cualquiera de los dominios del burnout: agotamiento emocional (AE), despersonalización (DP) y/o realización personal (RP).Resultados: Respondieron la encuesta 185/852 sujetos (21,7%); 79 sujetos reportaron AE (42,7%), 61 (32,9%) DP y 31 (16,7%) RP; 98 (52,4%) tenían al menos un componente de burnout alto para las dimensiones analizadas. La regresión logística mostró que el género femenino (OR= 2,21; IC 95%: 1,12-4,39), cargos administrativos (OR= 18,61; IC 95%: 4,28-80,93), médicos (OR= 13,27; IC 95% 3,55-49,86), y enfermeros (OR= 6,55; IC 95%: 1,58-27,14) se asociaron fuertemente con la presencia de algún dominio de burnout.Conclusion: La prevalencia del agotamiento estuvo en el rango de los estudios internacionales. Trabajadoras, cargos administrativos, médicos y enfermeras fueron identificados como predictores independientes de burnout

Burnout analysis in healthcare workers. A one center cross sectional comparison between physicians, nurses, administrative positions and technicians

Introduction: Healthcare workers (HCW) report higher levels of anxiety, depression, burnout, compared to the general population. The severe global health crisis caused by the coronavirus SARS-CoV-2 brought even more burden to HCW. Objective: To assessed burnout as a whole and in its different domains among HCW in a medical center Methods: We performed a cross-sectional study examining the association between demographic characteristics, healthcare position and feeling burned out. Data was collected through an anonymous online survey. We utilized the Maslach Survey for Medical Personnel in Spanish. Descriptive analyses summarized age, gender, job role, number of jobs, time in the organization and working in a COVID-19 exposed area. Ji2 tests were used to analyze association between variables and burnout. A multivariate logistic regression analysis was used to identify independent predictors of any of the burnout domains: emotional exhaustion (EE), depersonalization (DP) and/or personal accomplishment (PA). Results: 185/852 subjects answered the survey (21.7%); 79 subjects reported EE (42.7%), 61 (32.9%) DP and 31 (16.7%) PA; 98 (52.4%) had at least one component of high burnout for the dimensions analyzed. Logistics regression shows that female gender (OR= 2.21; 95% CI: 1.12-4.39), administrative positions (OR= 18.61; 95% CI: 4.28-80.93), physicians (OR= 13.27; 95% CI 3.55-49.86), and nurses (OR= 6.55; 95% CI: 1.58-27.14) were strongly associated with the presence of any burnout domain. Conclusions: The overall burnout prevalence was in range with international studies. Female workers, administrative positions, physicians and nurses were identified as independent predictors of burnout

MindTheDApp: A Toolchain for Complex Network-Driven Structural Analysis of Ethereum-Based Decentralized Applications

This paper presents MindTheDApp, a toolchain designed specifically for the structural analysis of Ethereum-based Decentralized Applications (DApps), with a distinct focus on a complex network-driven approach. Unlike existing tools, our toolchain combines the power of ANTLR4 and Abstract Syntax Tree (AST) traversal techniques to transform the architecture and interactions within smart contracts into a specialized bipartite graph. This enables advanced network analytics to highlight operational efficiencies within the DApp’s architecture. The bipartite graph generated by the proposed tool comprises two sets of nodes: one representing smart contracts, interfaces, and libraries, and the other including functions, events, and modifiers. Edges in the graph connect functions to smart contracts they interact with, offering a granular view of interdependencies and execution flow within the DApp. This network-centric approach allows researchers and practitioners to apply complex network theory in understanding the robustness, adaptability, and intricacies of decentralized systems. Our work contributes to the enhancement of security in smart contracts by allowing the visualisation of the network, and it provides a deep understanding of the architecture and operational logic within DApps. Given the growing importance of smart contracts in the blockchain ecosystem and the emerging application of complex network theory in technology, our toolchain offers a timely contribution to both academic research and practical applications in the field of blockchain technology

Tumor monocyte content predicts immunochemotherapy outcomes in esophageal adenocarcinoma

For inoperable esophageal adenocarcinoma (EAC), identifying patients likely to benefit from recently approved immunochemotherapy (ICI+CTX) treatments remains a key challenge. We address this using a uniquely designed window-of-opportunity trial (LUD2015-005), in which 35 inoperable EAC patients received first-line immune checkpoint inhibitors for four weeks (ICI-4W), followed by ICI+CTX. Comprehensive biomarker profiling, including generation of a 65,000-cell single-cell RNA-sequencing atlas of esophageal cancer, as well as multi-timepoint transcriptomic profiling of EAC during ICI-4W, reveals a novel T cell inflammation signature (INCITE) whose upregulation correlates with ICI-induced tumor shrinkage. Deconvolution of pre-treatment gastro-esophageal cancer transcriptomes using our single-cell atlas identifies high tumor monocyte content (TMC) as an unexpected ICI+CTX-specific predictor of greater overall survival (OS) in LUD2015-005 patients and of ICI response in prevalent gastric cancer subtypes from independent cohorts. Tumor mutational burden is an additional independent and additive predictor of LUD2015-005 OS. TMC can improve patient selection for emerging ICI+CTX therapies in gastro-esophageal cancer