Mean Field Network based Graph Refinement with application to Airway Tree Extraction

We present tree extraction in 3D images as a graph refinement task, of obtaining a subgraph from an over-complete input graph. To this end, we formulate an approximate Bayesian inference framework on undirected graphs using mean field approximation (MFA). Mean field networks are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters from training data using back-propagation algorithm. We demonstrate usefulness of the model to extract airway trees from 3D chest CT data. We first obtain probability images using a voxel classifier that distinguishes airways from background and use Bayesian smoothing to model individual airway branches. This yields us joint Gaussian density estimates of position, orientation and scale as node features of the input graph. Performance of the method is compared with two methods: the first uses probability images from a trained voxel classifier with region growing, which is similar to one of the best performing methods at EXACT'09 airway challenge, and the second method is based on Bayesian smoothing on these probability images. Using centerline distance as error measure the presented method shows significant improvement compared to these two methods.Comment: 10 pages. Preprin

Roles of Arrest-Defective Protein 1225 and Hypoxia-Inducible Factor 1α in Tumor Growth and Metastasis

Background Vascular endothelial growth factor A (VEGFA), a critical mediator of tumor angiogenesis, is a well-characterized target of hypoxia-inducible factor 1 (HIF-1). Murine arrest-defective protein 1A (mARD1A225) acetylates HIF-1??, triggering its degradation, and thus may play a role in decreased expression of VEGFA.Methods We generated ApcMin/+/mARD1A225 transgenic mice and quantified growth of intestinal polyps. Human gastric MKN74 and murine melanoma B16F10 cells overexpressing mARD1A225 were injected into mice, and tumor growth and metastasis were measured. VEGFA expression and microvessel density in tumors were assessed using immunohistochemistry. To evaluate the role of mARD1A 225 acetylation of Lys532 in HIF-1??, we injected B16F10-mARD1A225 cell lines stably expressing mutant HIF-1??/K532R into mice and measured metastasis. All statistical tests were two-sided, and P values less than. 05 were considered statistically significant.Results ApcMin/+/mARD1A225 transgenic mice (n = 25) had statistically significantly fewer intestinal polyps than Apc Min/+ mice (n = 21) (number of intestinal polyps per mouse: Apc Min/+ mice vs ApcMin/+/mARD1A225 transgenic mice, mean = 83.4 vs 38.0 polyps, difference = 45.4 polyps, 95% confidence interval [CI] = 41.8 to 48.6; P <. 001). The growth and metastases of transplanted tumors were also statistically significantly reduced in mice injected with mARD1A225-overexpressing cells than in mice injected with control cells (P <. 01). Moreover, overexpression of mARD1A 225 decreased VEGFA expression and microvessel density in tumor xenografts (P <. 04) and ApcMin/+ intestinal polyps (P =. 001). Mutation of lysine 532 of HIF-1?? in B16F10-mARD1A225 cells prevented HIF-1?? degradation and inhibited the antimetastatic effect of mARD1A225 (P <. 001).Conclusion mARD1A225 may be a novel upstream target that blocks VEGFA expression and tumor-related angiogenesis

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Modulation of Wnt/β-catenin signaling and proliferation by a ferrous iron chelator with therapeutic efficacy in genetically engineered mouse models of cancer

Using a screen for Wnt/β-catenin inhibitors, a family of 8-hydroxyquinolone derivatives with in vivo anti-cancer properties was identified. Analysis of microarray data for the lead compound N-((8-hydroxy-7-quinolinyl) (4-methylphenyl)methyl)benzamide (HQBA) using the Connectivity Map database suggested that it is an iron chelator that mimics the hypoxic response. HQBA chelates Fe2+ with a dissociation constant of ∼10−19 , with much weaker binding to Fe3+ and other transition metals. HQBA inhibited proliferation of multiple cell lines in culture, and blocked the progression of established spontaneous cancers in two distinct genetically engineered mouse models of mammary cancer, MMTV-Wnt1 and MMTV-PyMT mice, without overt toxicity. HQBA may inhibit an iron-dependent factor that regulates cell-type-specific β-catenin-driven transcription. It inhibits cancer cell proliferation independently of its effect on β-catenin signaling, as it works equally well in MMTV-PyMT tumors and diverse β-catenin-independent cell lines. HQBA is a promising specific intracellular Fe2+ chelator with activity against spontaneous mouse mammary cancers

The impact of rheumatoid foot on disability in Colombian patients with rheumatoid arthritis

<p>Abstract</p> <p>Background</p> <p>Alterations in the feet of patients with rheumatoid arthritis (RA) are a cause of disability in this population. The purpose of this research was to evaluate the impact that foot impairment has on the patients' global quality of life (QOL) based on validated scales and its relationship to disease activity.</p> <p>Methods</p> <p>This was a cross-sectional study in which 95 patients with RA were enrolled. A complete physical examination, including a full foot assessment, was done. The Spanish versions of the Health Assessment Questionnaire (HAQ) Disability Index and of the Disease Activity Score (DAS 28) were administered. A logistic regression model was used to analyze data and obtain adjusted odds ratios (AORs).</p> <p>Results</p> <p>Foot deformities were observed in 78 (82%) of the patients; hallux valgus (65%), medial longitudinal arch flattening (42%), claw toe (lesser toes) (39%), dorsiflexion restriction (tibiotalar) (34%), cock-up toe (lesser toes) (25%), and transverse arch flattening (25%) were the most frequent. In the logistic regression analysis (adjusted for age, gender and duration of disease), forefoot movement pain, subtalar movement pain, tibiotalar movement pain and plantarflexion restriction (tibiotalar) were strongly associated with disease activity and disability. The positive squeeze test was significantly associated with disability risk (AOR = 6,3; 95% CI, 1.28–30.96; P = 0,02); hallux valgus, and dorsiflexion restriction (tibiotalar) were associated with disease activity.</p> <p>Conclusion</p> <p>Foot abnormalities are associated with active joint disease and disability in RA. Foot examinations provide complementary information related to the disability as an indirect measurement of quality of life and activity of disease in daily practice.</p

JPN Guidelines for the management of acute pancreatitis: epidemiology, etiology, natural history, and outcome predictors in acute pancreatitis

Acute pancreatitis is a common disease with an annual incidence of between 5 and 80 people per 100 000 of the population. The two major etiological factors responsible for acute pancreatitis are alcohol and cholelithiasis (gallstones). The proportion of patients with pancreatitis caused by alcohol or gallstones varies markedly in different countries and regions. The incidence of acute alcoholic pancreatitis is considered to be associated with high alcohol consumption. Although the incidence of alcoholic pancreatitis is much higher in men than in women, there is no difference in sexes in the risk involved after adjusting for alcohol intake. Other risk factors include endoscopic retrograde cholangiopancreatography, surgery, therapeutic drugs, HIV infection, hyperlipidemia, and biliary tract anomalies. Idiopathic acute pancreatitis is defined as acute pancreatitis in which the etiological factor cannot be specified. However, several studies have suggested that this entity includes cases caused by other specific disorders such as microlithiasis. Acute pancreatitis is a potentially fatal disease with an overall mortality of 2.1%–7.8%. The outcome of acute pancreatitis is determined by two factors that reflect the severity of the illness: organ failure and pancreatic necrosis. About half of the deaths in patients with acute pancreatitis occur within the first 1–2 weeks and are mainly attributable to multiple organ dysfunction syndrome (MODS). Depending on patient selection, necrotizing pancreatitis develops in approximately 10%–20% of patients and the mortality is high, ranging from 14% to 25% of these patients. Infected pancreatic necrosis develops in 30%–40% of patients with necrotizing pancreatitis and the incidence of MODS in such patients is high. The recurrence rate of acute pancreatitis is relatively high: almost half the patients with acute alcoholic pancreatitis experience a recurrence. When the gallstones are not treated, the risk of recurrence in gallstone pancreatitis ranges from 32% to 61%. After recovering from acute pancreatitis, about one-third to one-half of acute pancreatitis patients develop functional disorders, such as diabetes mellitus and fatty stool; the incidence of chronic pancreatitis after acute pancreatitis ranges from 3% to 13%. Nevertheless, many reports have shown that most patients who recover from acute pancreatitis regain good general health and return to their usual daily routine. Some authors have emphasized that endocrine function disorders are a common complication after severe acute pancreatitis has been treated by pancreatic resection

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses