The Fiscal Effects of Aid in Ethiopia Evidence from CVAR Applications

This article explores the fiscal effects of aid in Ethiopia using the Cointegrated Vector AutoRegressive (CVAR) methodology to model complex long-run and short-run dynamics. We use national data for 1961–2010, including a measure of aid capturing flows through the budget as measured by the recipient. The data suggests three main conclusions on the long-run equilibrium. First, government long-term spending plans are based on domestic sources, treating aid as an additional source of revenue. Second, both grants and loans are positively related to tax revenue. Third, aid is positively associated with spending, with a particularly strong relation between capital expenditure and grants. Overall, our results show that aid in Ethiopia had beneficial fiscal effects

Simultaneous computer-assisted assessment of mucosal and serosal perfusion in a model of segmental colonic ischemia

BACKGROUND: Fluorescence-based enhanced reality (FLER) enables the quantification of fluorescence signal dynamics, which can be superimposed onto real-time laparoscopic images by using a virtual perfusion cartogram. The current practice of perfusion assessment relies on visualizing the bowel serosa. The aim of this experimental study was to quantify potential differences in mucosal and serosal perfusion levels in an ischemic colon segment. METHODS: An ischemic colon segment was created in 12 pigs. Simultaneous quantitative mucosal and serosal fluorescence imaging was obtained via intravenous indocyanine green injection (0.2 mg/kg), using two near-infrared camera systems, and computer-assisted FLER analysis. Lactate levels were measured in capillary blood of the colonic wall at seven regions of interest (ROIs) as determined with FLER perfusion cartography: the ischemic zone (I), the proximal and distal vascularized areas (PV, DV), and the 50% perfusion threshold proximally and distally at the mucosal and serosal side (P50M, P50S, D50M, D50S). RESULTS: The mean ischemic zone as measured (mm) for the mucosal side was significantly larger than the serosal one (56.3 ± 21.3 vs. 40.8 ± 14.9, p = 0.001) with significantly lower lactate values at the mucosal ROIs. There was a significant weak inverse correlation between lactate and slope values for the defined ROIs (r = - 0.2452, p = 0.0246). CONCLUSIONS: Mucosal ischemic zones were larger than serosal zones. These results suggest that an assessment of bowel perfusion from the serosal side only can underestimate the extent of ischemia. Further studies are required to predict the optimal resection margin and anastomotic site

Artificial Intelligence and Surgery: Ethical Dilemmas and Open Issues

Background: Artificial Intelligence (AI) applications aiming to support surgical decision-making processes are generating novel threats to ethical surgical care. To understand and address these threates, we summarize the main ethical issues that may arise from applying AI to surgery, starting from the Ethics Guidelines for Trustworthy Artificial Intelligence framework recently promoted by the European Commission. Study Design: A modified Delphi process has been employed to achieve expert consensus. Results: The main ethical issues that arise from applying AI to surgery, described in detail herein, relate to human agency, accountability for errors, technical robustness, privacy and data governance, transparency, diversity, non-discrimination, and fairness. It may be possible to address many of these ethical issues by expanding the breadth of surgical AI research to focus on implementation science. The potential for AI to disrupt surgical practice suggests that formal digital health education is becoming increasingly important for surgeons and surgical trainees. Conclusions: A multidisciplinary focus on implementation science and digital health education is desirable to balance opportunities offered by emerging AI technologies and respect for the ethical principles of a patient-centric philosophy

Cultivar-specific transcriptome prediction and annotation in Ficus carica L.

The availability of transcriptomic data sequence is a key step for functional genomics studies. Recently, a repertoire of predicted genes of a Japanese cultivar of fig (Ficus carica L.) was released. Because of the great phenotypic variability that can be found in this species, we decided to study another fig genotype, the Italian cv. Dottato, in order to perform comparative studies between the two cultivars and extend the pan genome of this species. We isolated, sequenced and assembled fig genomic DNA from young fruits of cv. Dottato. Then, putative gene sequences were predicted and annotated. Finally, a comparison was performed between cvs. Dottato and Horaishi predicted transcriptomes. Our data provide a resource (available at the Sequence Read Archive database under SRP109082) to be used for functional genomics of fig, in order to fill the gap of knowledge still existing in this species concerning plant development, defense and adaptation to the environment

A non-hybrid method for the PDF equations of turbulent flows on unstructured grids

In probability density function (PDF) methods of turbulent flows, the joint PDF of several flow variables is computed by numerically integrating a system of stochastic differential equations for Lagrangian particles. A set of parallel algorithms is proposed to provide an efficient solution of the PDF transport equation, modeling the joint PDF of turbulent velocity, frequency and concentration of a passive scalar in geometrically complex configurations. An unstructured Eulerian grid is employed to extract Eulerian statistics, to solve for quantities represented at fixed locations of the domain (e.g. the mean pressure) and to track particles. All three aspects regarding the grid make use of the finite element method (FEM) employing the simplest linear FEM shape functions. To model the small-scale mixing of the transported scalar, the interaction by exchange with the conditional mean model is adopted. An adaptive algorithm that computes the velocity-conditioned scalar mean is proposed that homogenizes the statistical error over the sample space with no assumption on the shape of the underlying velocity PDF. Compared to other hybrid particle-in-cell approaches for the PDF equations, the current methodology is consistent without the need for consistency conditions. The algorithm is tested by computing the dispersion of passive scalars released from concentrated sources in two different turbulent flows: the fully developed turbulent channel flow and a street canyon (or cavity) flow. Algorithmic details on estimating conditional and unconditional statistics, particle tracking and particle-number control are presented in detail. Relevant aspects of performance and parallelism on cache-based shared memory machines are discussed.Comment: Accepted in Journal of Computational Physics, Feb. 20, 200

SAGES consensus recommendations on an annotation framework for surgical video

Background: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration. Methods: Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups. Results: After three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established. Conclusions: While additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration

Personality and Temperament Correlates of Pain Catastrophizing in Young Adolescents

Pain catastrophizing is generally viewed as an important cognitive factor underlying chronic pain. The present study examined personality and temperament correlates of pain catastrophizing in a sample of young adolescents (N = 132). Participants completed the Pain Catastrophizing Scale for Children, as well as scales for measuring sensitivity of the behavioral inhibition and behavioral activation systems (BIS-BAS), and various reactive and regulative temperament traits. Results demonstrated that BIS, reactive temperament traits (fear and anger-frustration), and perceptual sensitivity were positively related to pain catastrophizing, whereas regulative traits (attention control, inhibitory control) were negatively associated with this cognitive factor. Further, regression analyses demonstrated that only BIS and the temperamental traits of fear and perceptual sensitivity accounted for a unique proportion of the variance in adolescents’ pain catastrophizing scores

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process