Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets

Seroepidemiology of Toxoplasma gondii infection in pregnant women in a public hospital in northern Mexico

BACKGROUND: Toxoplasma gondii (T. gondii) infection in pregnant women represents a risk for congenital disease. There is scarce information about the epidemiology of T. gondii infection in pregnant women in Mexico. Therefore, we sought to determine the prevalence of T. gondii infection and associated socio-demographic, clinical and behavioural characteristics in a population of pregnant women of Durango City, Mexico. METHODS: Three hundred and forty three women seeking prenatal care in a public hospital of Durango City in Mexico were examined for T. gondii infection. All women were tested for anti-T. gondii IgM and IgG antibodies by using IMx Toxo IgM and IMx Toxo IgG 2.0 kits (Abbott Laboratories, Abbott Park, IL, USA), respectively. Socio-demographic, clinical and behavioural characteristics from each participant were also obtained. RESULTS: Twenty one out of the 343 (6.1%) women had IgG anti-T. gondii antibodies. None of the 343 women had IgM anti-T. gondii antibodies. Multivariate analysis using logic regression showed that T. gondii infection was associated with living in a house with soil floor (adjusted OR = 7.16; 95% CI: 1.39–36.84), residing outside of Durango State (adjusted OR = 4.25; 95% CI: 1.72–10.49), and turkey meat consumption (adjusted OR = 3.85; 95% CI: 1.30–11.44). Other characteristics as cat contact, gardening, and food preferences did not show any association with T. gondii infection. CONCLUSION: The prevalence of T. gondii infection in pregnant women of Durango City is low as compared with those reported in other regions of Mexico and the majority of other countries. Poor housing conditions as soil floors, residing in other Mexican States, and turkey meat consumption might contribute to acquire T. gondii infection

A global research priority agenda to advance public health responses to fatty liver disease

BACKGROUND & AIMS: An estimated 38% of adults worldwide have non-alcoholic fatty liver disease (NAFLD). From individual impacts to widespread public health and economic consequences, the implications of this disease are profound. This study aimed to develop an aligned, prioritised fatty liver disease research agenda for the global health community. METHODS: Nine co-chairs drafted initial research priorities, subsequently reviewed by 40 core authors and debated during a three-day in-person meeting. Following a Delphi methodology, over two rounds, a large panel (R1 n = 344, R2 n = 288) reviewed the priorities, via Qualtrics XM, indicating agreement using a four-point Likert-scale and providing written feedback. The core group revised the draft priorities between rounds. In R2, panellists also ranked the priorities within six domains: epidemiology, models of care, treatment and care, education and awareness, patient and community perspectives, and leadership and public health policy. RESULTS: The consensus-built fatty liver disease research agenda encompasses 28 priorities. The mean percentage of 'agree' responses increased from 78.3 in R1 to 81.1 in R2. Five priorities received unanimous combined agreement ('agree' + 'somewhat agree'); the remaining 23 priorities had >90% combined agreement. While all but one of the priorities exhibited at least a super-majority of agreement (>66.7% 'agree'), 13 priorities had 90% combined agreement. CONCLUSIONS: Adopting this multidisciplinary consensus-built research priorities agenda can deliver a step-change in addressing fatty liver disease, mitigating against its individual and societal harms and proactively altering its natural history through prevention, identification, treatment, and care. This agenda should catalyse the global health community's efforts to advance and accelerate responses to this widespread and fast-growing public health threat. IMPACT AND IMPLICATIONS: An estimated 38% of adults and 13% of children and adolescents worldwide have fatty liver disease, making it the most prevalent liver disease in history. Despite substantial scientific progress in the past three decades, the burden continues to grow, with an urgent need to advance understanding of how to prevent, manage, and treat the disease. Through a global consensus process, a multidisciplinary group agreed on 28 research priorities covering a broad range of themes, from disease burden, treatment, and health system responses to awareness and policy. The findings have relevance for clinical and non-clinical researchers as well as funders working on fatty liver disease and non-communicable diseases more broadly, setting out a prioritised, ranked research agenda for turning the tide on this fast-growing public health threat

A global action agenda for turning the tide on fatty liver disease

BACKGROUND AND AIMS: Fatty liver disease is a major public health threat due to its very high prevalence and related morbidity and mortality. Focused and dedicated interventions are urgently needed to target disease prevention, treatment, and care. APPROACH AND RESULTS: We developed an aligned, prioritized action agenda for the global fatty liver disease community of practice. Following a Delphi methodology over 2 rounds, a large panel (R1 n = 344, R2 n = 288) reviewed the action priorities using Qualtrics XM, indicating agreement using a 4-point Likert-scale and providing written feedback. Priorities were revised between rounds, and in R2, panelists also ranked the priorities within 6 domains: epidemiology, treatment and care, models of care, education and awareness, patient and community perspectives, and leadership and public health policy. The consensus fatty liver disease action agenda encompasses 29 priorities. In R2, the mean percentage of "agree" responses was 82.4%, with all individual priorities having at least a super-majority of agreement (> 66.7% "agree"). The highest-ranked action priorities included collaboration between liver specialists and primary care doctors on early diagnosis, action to address the needs of people living with multiple morbidities, and the incorporation of fatty liver disease into relevant non-communicable disease strategies and guidance. CONCLUSIONS: This consensus-driven multidisciplinary fatty liver disease action agenda developed by care providers, clinical researchers, and public health and policy experts provides a path to reduce fatty liver disease prevalence and improve health outcomes. To implement this agenda, concerted efforts will be needed at the global, regional, and national levels

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services