Metal Fluorides Passivate II-VI and III-V Quantum Dots.

Quantum dots (QDs) with metal fluoride surface ligands were prepared via reaction with anhydrous oleylammonium fluoride. Carboxylate terminated II-VI QDs underwent carboxylate for fluoride exchange, while InP QDs underwent photochemical acidolysis yielding oleylamine, PH3, and InF3. The final photoluminescence quantum yield (PLQY) reached 83% for InP and near unity for core-shell QDs. Core-only CdS QDs showed dramatic improvements in PLQY, but only after exposure to air. Following etching, the InP QDs were bound by oleylamine ligands that were characterized by the frequency and breadth of the corresponding ν(N-H) bands in the infrared absorption spectrum. The fluoride content (1.6-9.2 nm-2) was measured by titration with chlorotrimethylsilane and compared with the oleylamine content (2.3-5.1 nm-2) supporting the formation of densely covered surfaces. The influence of metal fluoride adsorption on the air stability of QDs is discussed.

Albumin promoter-driven FlpO expression induces efficient genetic recombination in mouse liver.

Tissue-specific gene manipulations are widely used in genetically engineered mouse models. A single recombinase system, such as the one using Alb-Cre, has been commonly used for liver-specific genetic manipulations. However, most diseases are complex, involving multiple genetic changes and various cell types. A dual recombinase system is required for conditionally modifying different genes sequentially in the same cell or inducing genetic changes in different cell types within the same organism. A FlpO cDNA was inserted between the last exon and 3'-UTR of the mouse albumin gene in a bacterial artificial chromosome (BAC-Alb-FlpO). The founders were crossed with various reporter mice to examine the efficiency of recombination. Liver cancer tumorigenesis was investigated by crossing the FlpO mice with FSF-KrasG12D mice and p53frt mice (KPF mice). BAC-Alb-FlpO mice exhibited highly efficient recombination capability in both hepatocytes and intrahepatic cholangiocytes. No recombination was observed in the duodenum and pancreatic cells. BAC-Alb-FlpO-mediated liver-specific expression of mutant KrasG12D and conditional deletion of p53 gene caused the development of liver cancer. Remarkably, liver cancer in these KPF mice manifested a distinctive mixed hepatocellular carcinoma and cholangiocarcinoma phenotype. A highly efficient and liver-specific BAC-Alb-FlpO mouse model was developed. In combination with other Cre lines, different genes can be manipulated sequentially in the same cell, or distinct genetic changes can be induced in different cell types of the same organism.NEW & NOTEWORTHY A liver-specific Alb-FlpO mouse line was generated. By coupling it with other existing CreERT or Cre lines, the dual recombinase approach can enable sequential gene modifications within the same cell or across various cell types in an organism for liver research through temporal and spatial gene manipulations.

PTEN deficiency induces an extrahepatic cholangitis-cholangiocarcinoma continuum via aurora kinase A in mice.

BACKGROUND & AIMS: The PTEN-AKT pathway is frequently altered in extrahepatic cholangiocarcinoma (eCCA). We aimed to evaluate the role of PTEN in the pathogenesis of eCCA and identify novel therapeutic targets for this disease. METHODS: The Pten gene was genetically deleted using the Cre-loxp system in biliary epithelial cells. The pathologies were evaluated both macroscopically and histologically. The characteristics were further analyzed by immunohistochemistry, reverse-transcription PCR, cell culture, and RNA sequencing. Some features were compared to those in human eCCA samples. Further mechanistic studies utilized the conditional knockout of Trp53 and Aurora kinase A (Aurka) genes. We also tested the effectiveness of an Aurka inhibitor. RESULTS: We observed that genetic deletion of the Pten gene in the extrahepatic biliary epithelium and peri-ductal glands initiated sclerosing cholangitis-like lesions in mice, resulting in enlarged and distorted extrahepatic bile ducts in mice as early as 1 month after birth. Histologically, these lesions exhibited increased epithelial proliferation, inflammatory cell infiltration, and fibrosis. With aging, the lesions progressed from low-grade dysplasia to invasive carcinoma. Trp53 inactivation further accelerated disease progression, potentially by downregulating senescence. Further mechanistic studies showed that both human and mouse eCCA showed high expression of AURKA. Notably, the genetic deletion of Aurka completely eliminated Pten deficiency-induced extrahepatic bile duct lesions. Furthermore, pharmacological inhibition of Aurka alleviated disease progression. CONCLUSIONS: Pten deficiency in extrahepatic cholangiocytes and peribiliary glands led to a cholangitis-to-cholangiocarcinoma continuum that was dependent on Aurka. These findings offer new insights into preventive and therapeutic interventions for extrahepatic CCA. IMPACT AND IMPLICATIONS: The aberrant PTEN-PI3K-AKT signaling pathway is commonly observed in human extrahepatic cholangiocarcinoma (eCCA), a disease with a poor prognosis. In our study, we developed a mouse model mimicking cholangitis to eCCA progression by conditionally deleting the Pten gene via Pdx1-Cre in epithelial cells and peribiliary glands of the extrahepatic biliary duct. The conditional Pten deletion in these cells led to cholangitis, which gradually advanced to dysplasia, ultimately resulting in eCCA. The loss of Pten heightened Akt signaling, cell proliferation, inflammation, fibrosis, DNA damage, epigenetic signaling, epithelial-mesenchymal transition, cell dysplasia, and cellular senescence. Genetic deletion or pharmacological inhibition of Aurka successfully halted disease progression. This model will be valuable for testing novel therapies and unraveling the mechanisms of eCCA tumorigenesis.

A mouse model for high-efficient Flp-recombinase-mediated genetic manipulation in the pancreas.

BACKGROUND: Tissue and cell-specific gene targeting has been widely employed in biomedical research. In the pancreas, the commonly used Cre recombinase recognizes and recombines loxP sites. However, to selectively target different genes in distinct cells, a dual recombinase system is required. METHOD: We developed an alternative recombination system mediated by FLPo, which recognizes frt DNA sequences for pancreatic dual recombinase-mediated genetic manipulation. An IRES-FLPo cassette was targeted between the translation stop code and 3-UTR of the mouse pdx1 gene in a Bacterial Artificial Chromosome using recombineering technology. Transgenic BAC-Pdx1-FLPo mice were developed by pronuclear injection. RESULTS: Highly efficient recombination activity was observed in the pancreas by crossing the founder mice with Flp reporter mice. When the BAC-Pdx1-FLPo mice were bred with conditional FSF-KRasG12D and p53 F/F mice, pancreatic cancer developed in the compound mice. The characteristics of pancreatic cancer resembled those derived from conditional LSL-KRasG12D and p53 L/L mice controlled by pdx1-Cre. CONCLUSIONS: We have generated a new transgenic mouse line expressing FLPo, which enables highly efficient pancreatic-specific gene recombination. When combined with other available Cre lines, this system can be utilized to target different genes in distinct cells for pancreatic research.

Towards Accurate Reconstruction of 3D Scene Shape From A Single Monocular Image.

Despite significant progress made in the past few years, challenges remain for depth estimation using a single monocular image. First, it is nontrivial to train a metric-depth prediction model that can generalize well to diverse scenes mainly due to limited training data. Thus, researchers have built large-scale relative depth datasets that are much easier to collect. However, existing relative depth estimation models often fail to recover accurate 3D scene shapes due to the unknown depth shift caused by training with the relative depth data. We tackle this problem here and attempt to estimate accurate scene shapes by training on large-scale relative depth data, and estimating the depth shift. To do so, we propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then exploits 3D point cloud data to predict the depth shift and the camera's focal length that allow us to recover 3D scene shapes. As the two modules are trained separately, we do not need strictly paired training data. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to improve training with relative depth annotation. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot evaluation. Code is available at: https://github.com/aim-uofa/depth/.

Ptf1a Promoter-Driven Cre Expression During Spermatogenesis Causes Germline Recombination.

OBJECTIVES: Two independently developed Ptf1a-Cre mouse lines, Ptf1atm1(cre)Hnak and Ptf1atm1(Cre)Cvw, are widely used in pancreatic research. Recently, Ptf1atm1(cre)Hnak line was reported to transmit unwanted paternal recombination. We aimed to investigate whether this exists in the Ptf1atm1(Cre)Cvw line. METHODS: Ptf1atm1(Cre)Cvw mice were crossed with R26-LSL-LacZ reporter mice. DNA recombination and gene expression were examined by recombination-specific polymerase chain reaction, reverse transcription-polymerase chain reaction, and X-Gal staining. RESULTS: R26 locus recombination was detected in the pancreas as well as the testes and sperm of the double transgenic mice. Positive ptf1a mRNA expression from testes revealed that there was endogenous Ptf1a promoter activity in this extrapancreatic tissue. Of the 15 progenies that inherited LacZ from the male double transgenic mice, 4 (26.7%) were positive for complete whole-body recombination. The presence of recombination in R26 only mice suggested that the recombination occurred before meiosis. CONCLUSIONS: Paternal germline recombination exists in the Ptf1atm1(Cre)Cvw mouse line. Ptf1a promoter-driven Cre expression during spermatogenesis before meiosis is the cause of germline recombination. Therefore, when male Ptf1a-Cre mice are used in compound mice breeding, it is necessary to genotype not only floxed alleles but also recombined alleles to examine unwanted recombinations.

Pancreas-specific CHRM3 activation causes pancreatitis in mice.

Hyperstimulation of the cholecystokinin 1 receptor (CCK1R), a G protein-coupled receptor (GPCR), in pancreatic acinar cells is commonly used to induce pancreatitis in rodents. Human pancreatic acinar cells lack CCK1R but express cholinergic receptor muscarinic 3 (M3R), another GPCR. To test whether M3R activation is involved in pancreatitis, a mutant M3R was conditionally expressed in pancreatic acinar cells in mice. This mutant receptor loses responsiveness to its native ligand, acetylcholine, but can be activated by an inert small molecule, clozapine-N-oxide (CNO). Intracellular calcium and amylase were elicited by CNO in pancreatic acinar cells isolated from mutant M3R mice but not WT mice. Similarly, acute pancreatitis (AP) could be induced by a single injection of CNO in the transgenic mice but not WT mice. Compared with the cerulein-induced AP, CNO caused more widespread acinar cell death and inflammation. Furthermore, chronic pancreatitis developed at 4 weeks after 3 episodes of CNO-induced AP. In contrast, in mice with 3 recurrent episodes of cerulein-included AP, pancreas histology was restored in 4 weeks. Furthermore, the M3R antagonist ameliorated the severity of cerulein-induced AP in WT mice. We conclude that M3R activation can cause the pathogenesis of pancreatitis. This model may provide an alternative approach for pancreatitis research.

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data.

BACKGROUND: The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs. OBJECTIVE: The aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes. METHODS: Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality. RESULTS: The EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS. CONCLUSIONS: In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.

Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine.

BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate. OBJECTIVE: The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population. METHODS: Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018. RESULTS: The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer. CONCLUSIONS: We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.

An ensemble-based likelihood ratio approach for family-based genomic risk prediction.

OBJECTIVE: As one of the most popular designs used in genetic research, family-based design has been well recognized for its advantages, such as robustness against population stratification and admixture. With vast amounts of genetic data collected from family-based studies, there is a great interest in studying the role of genetic markers from the aspect of risk prediction. This study aims to develop a new statistical approach for family-based risk prediction analysis with an improved prediction accuracy compared with existing methods based on family history. METHODS: In this study, we propose an ensemble-based likelihood ratio (ELR) approach, Fam-ELR, for family-based genomic risk prediction. Fam-ELR incorporates a clustered receiver operating characteristic (ROC) curve method to consider correlations among family samples, and uses a computationally efficient tree-assembling procedure for variable selection and model building. RESULTS: Through simulations, Fam-ELR shows its robustness in various underlying disease models and pedigree structures, and attains better performance than two existing family-based risk prediction methods. In a real-data application to a family-based genome-wide dataset of conduct disorder, Fam-ELR demonstrates its ability to integrate potential risk predictors and interactions into the model for improved accuracy, especially on a genome-wide level. CONCLUSIONS: By comparing existing approaches, such as genetic risk-score approach, Fam-ELR has the capacity of incorporating genetic variants with small or moderate marginal effects and their interactions into an improved risk prediction model. Therefore, it is a robust and useful approach for high-dimensional family-based risk prediction, especially on complex disease with unknown or less known disease etiology.

Model-based and model-free pain avoidance learning.

Background: While there is good evidence that reward learning is underpinned by two distinct decision control systems - a cognitive 'model-based' and a habitbased 'model-free' system, a comparable distinction for punishment avoidance has been much less clear. Methods: We implemented a pain avoidance task that placed differential emphasis on putative model-based and model-free processing, mirroring a paradigm and modelling approach recently developed for reward-based decision-making. Subjects performed a two-step decision-making task with probabilistic pain outcomes of different quantities. The delivery of outcomes was sometimes contingent on a rule signalled at the beginning of each trial, emulating a form of outcome devaluation. Results: The behavioural data showed that subjects tended to use a mixed strategy - favouring the simpler model-free learning strategy when outcomes did not depend on the rule, and favouring a model-based when they did. Furthermore, the data were well described by a dynamic transition model between the two controllers. When compared with data from a reward-based task (albeit tested in the context of the scanner), we observed that avoidance involved a significantly greater tendency for subjects to switch between model-free and model-based systems in the face of changes in uncertainty. Conclusion: Our study suggests a dual-system model of pain avoidance, similar to but possibly more dynamically flexible than reward-based decision-making.

Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility.

BACKGROUND: For many elderly patients, a disproportionate amount of health care resources and expenditures is spent during the last year of life, despite the discomfort and reduced quality of life associated with many aggressive medical approaches. However, few prognostic tools have focused on predicting all-cause 1-year mortality among elderly patients at a statewide level, an issue that has implications for improving quality of life while distributing scarce resources fairly. OBJECTIVE: Using data from a statewide elderly population (aged ≥65 years), we sought to prospectively validate an algorithm to identify patients at risk for dying in the next year for the purpose of minimizing decision uncertainty, improving quality of life, and reducing futile treatment. METHODS: Analysis was performed using electronic medical records from the Health Information Exchange in the state of Maine, which covered records of nearly 95% of the statewide population. The model was developed from 125,896 patients aged at least 65 years who were discharged from any care facility in the Health Information Exchange network from September 5, 2013, to September 4, 2015. Validation was conducted using 153,199 patients with same inclusion and exclusion criteria from September 5, 2014, to September 4, 2016. Patients were stratified into risk groups. The association between all-cause 1-year mortality and risk factors was screened by chi-squared test and manually reviewed by 2 clinicians. We calculated risk scores for individual patients using a gradient tree-based boost algorithm, which measured the probability of mortality within the next year based on the preceding 1-year clinical profile. RESULTS: The development sample included 125,896 patients (72,572 women, 57.64%; mean 74.2 [SD 7.7] years). The final validation cohort included 153,199 patients (88,177 women, 57.56%; mean 74.3 [SD 7.8] years). The c-statistic for discrimination was 0.96 (95% CI 0.93-0.98) in the development group and 0.91 (95% CI 0.90-0.94) in the validation cohort. The mortality was 0.99% in the low-risk group, 16.75% in the intermediate-risk group, and 72.12% in the high-risk group. A total of 99 independent risk factors (n=99) for mortality were identified (reported as odds ratios; 95% CI). Age was on the top of list (1.41; 1.06-1.48); congestive heart failure (20.90; 15.41-28.08) and different tumor sites were also recognized as driving risk factors, such as cancer of the ovaries (14.42; 2.24-53.04), colon (14.07; 10.08-19.08), and stomach (13.64; 3.26-86.57). Disparities were also found in patients' social determinants like respiratory hazard index (1.24; 0.92-1.40) and unemployment rate (1.18; 0.98-1.24). Among high-risk patients who expired in our dataset, cerebrovascular accident, amputation, and type 1 diabetes were the top 3 diseases in terms of average cost in the last year of life. CONCLUSIONS: Our study prospectively validated an accurate 1-year risk prediction model and stratification for the elderly population (≥65 years) at risk of mortality with statewide electronic medical record datasets. It should be a valuable adjunct for helping patients to make better quality-of-life choices and alerting care givers to target high-risk elderly for appropriate care and discussions, thus cutting back on futile treatment.

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning.

BACKGROUND: As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke. OBJECTIVE: The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year. METHODS: Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees and assigned a final predictive risk score to each individual. RESULTS: The 1-year incident hypertension risk model attained areas under the curve (AUCs) of 0.917 and 0.870 in the retrospective and prospective cohorts, respectively. Risk scores were calculated and stratified into five risk categories, with 4526 out of 381,544 patients (1.19%) in the lowest risk category (score 0-0.05) and 21,050 out of 41,329 patients (50.93%) in the highest risk category (score 0.4-1) receiving a diagnosis of incident hypertension in the following 1 year. Type 2 diabetes, lipid disorders, CVDs, mental illness, clinical utilization indicators, and socioeconomic determinants were recognized as driving or associated features of incident essential hypertension. The very high risk population mainly comprised elderly (age>50 years) individuals with multiple chronic conditions, especially those receiving medications for mental disorders. Disparities were also found in social determinants, including some community-level factors associated with higher risk and others that were protective against hypertension. CONCLUSIONS: With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.

Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci.

Coronary artery disease (CAD) is the leading cause of mortality and morbidity, driven by both genetic and environmental risk factors. Meta-analyses of genome-wide association studies have identified >150 loci associated with CAD and myocardial infarction susceptibility in humans. A majority of these variants reside in non-coding regions and are co-inherited with hundreds of candidate regulatory variants, presenting a challenge to elucidate their functions. Herein, we use integrative genomic, epigenomic and transcriptomic profiling of perturbed human coronary artery smooth muscle cells and tissues to begin to identify causal regulatory variation and mechanisms responsible for CAD associations. Using these genome-wide maps, we prioritize 64 candidate variants and perform allele-specific binding and expression analyses at seven top candidate loci: 9p21.3, SMAD3, PDGFD, IL6R, BMP1, CCDC97/TGFB1 and LMOD1. We validate our findings in expression quantitative trait loci cohorts, which together reveal new links between CAD associations and regulatory function in the appropriate disease context.

Prognostic and Functional Significance of MAP4K5 in Pancreatic Cancer.

OBJECTIVES: MAP4K5 plays an important role in regulating a range of cellular responses and is involved in Wnt signaling in hematopoietic cells. However, its functions in human malignancies have not been studied. The major objectives of this study are to examine the expression, functions and clinical significance of MAP4K5 in pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: The expression levels of MAP4K5, E-cadherin, vimentin, and carboxylesterase 2 (CES2) were examined by immunohistochemistry in 105 PDAC and matched non-neoplastic pancreas samples from our institution. The RNA sequencing data of 112 PDAC patients were downloaded from the TCGA data portal. Immunoblotting and RNA sequencing analysis were used to examine the expression of MAP4K5 and E-cadherin in pancreatic cancer cell lines. The effect of knockdown MAP4K5 using siRNA on the expression of CDH1 and vimentin were examined by Real-time RT-PCR in Panc-1 and AsPC-1 cells. Statistical analyses were performed using IBM SPSS Statistics. RESULTS: MAP4K5 protein is expressed at high levels specifically in the pancreatic ductal cells of 100% non-neoplastic pancreas samples, but is decreased or lost in 77.1% (81/105) of PDAC samples. MAP4K5-low correlated with the loss of E-cadherin (P = 0.001) and reduced CES2 expression (P = 0.002) in our patient populations. The expression levels of MAP4K5 mRNA directly correlated with the expression levels of CDH1 mRNA (R = 0.2490, P = 0.008) in the second cohort of 112 PDAC patients from The Cancer Genome Atlas (TCGA) RNA-seq dataset. Similar correlations between the expression of MAP4K5 and E-cadherin were observed both at protein and mRNA levels in multiple pancreatic cancer cell lines. Knockdown MAP4K5 led to decreased CDH1 mRNA expression in Panc-1 and AsPC-1 cells. MAP4K5-low correlated significantly with reduced overall survival and was an independent prognosticator in patients with stage II PDAC. CONCLUSIONS: MAP4K5 expression is decreased or lost in majority of PDACs. The strong associations between low MAP4K5 expression and loss of E-cadherin, reduced CES2 expression and decreased overall survival may suggest an important role of MAP4K5 in epithelial-to-mesenchymal transition, chemotherapy resistance and tumor progression in pancreatic cancer. Targeting impaired MAP4K5 signaling may represent a new therapeutic strategy for pancreatic cancer.

Personal photograph enhancement using internet photo collections.

Given the growth of Internet photo collections, we now have a visual index of all major cities and tourist sites in the world. However, it is still a difficult task to capture that perfect shot with your own camera when visiting these places, especially when your camera itself has limitations, such as a limited field of view. In this paper, we propose a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale Internet photo collections. Our method deploys state-of-the-art techniques for constructing initial 3D models from photo collections. The same techniques are then used to register personal photographs to these models, allowing us to augment personal 2D images with 3D information. This strong available scene prior allows us to address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. Specifically, we demonstrate automatic foreground segmentation, mono-to-stereo conversion, field-of-view expansion, photometric enhancement, and additionally automatic annotation with geolocation and tags. Our method clearly demonstrates some possible benefits of employing the rich information contained in online photo databases to efficiently enhance and augment one's own personal photographs.

Distinguishing texture edges from object boundaries in video.

One of the most fundamental problems in image processing and computer vision is the inherent ambiguity that exists between texture edges and object boundaries in real-world images and video. Despite this ambiguity, many applications in computer vision and image processing often use image edge strength with the assumption that these edges approximate object depth boundaries. However, this assumption is often invalidated by real world data, and this discrepancy is a significant limitation in many of today's image processing methods. We address this issue by introducing a simple, low-level, and patch-consistency assumption that leverages the extra information present in video data to resolve this ambiguity. Through analyzing how well patches can be modeled by simple transformations over time, we can obtain an indication of which image edges correspond to texture edges versus object boundaries. Our approach is simple to implement and has the potential to improve a wide range of image and video-based applications by suppressing the detrimental effects of strong texture edges on regularization terms. We validate our approach by presenting results on a variety of scene types and directly incorporating our augmented edge map into existing image segmentation and optical flow applications, showing results that better correspond to object boundaries.

Automatic view synthesis by image-domain-warping.

Today, stereoscopic 3D (S3D) cinema is already mainstream, and almost all new display devices for the home support S3D content. S3D distribution infrastructure to the home is already established partly in the form of 3D Blu-ray discs, video on demand services, or television channels. The necessity to wear glasses is, however, often considered as an obstacle, which hinders broader acceptance of this technology in the home. Multiviewautostereoscopic displays enable a glasses free perception of S3D content for several observers simultaneously, and support head motion parallax in a limited range. To support multiviewautostereoscopic displays in an already established S3D distribution infrastructure, a synthesis of new views from S3D video is needed. In this paper, a view synthesis method based on image-domain-warping (IDW) is presented that automatically synthesizes new views directly from S3D video and functions completely. IDW relies on an automatic and robust estimation of sparse disparities and image saliency information, and enforces target disparities in synthesized images using an image warping framework. Two configurations of the view synthesizer in the scope of a transmission and view synthesis framework are analyzed and evaluated. A transmission and view synthesis system that uses IDW is recently submitted to MPEG's call for proposals on 3D video technology, where it is ranked among the four best performing proposals.

Recent trends in hospitalization for acute myocardial infarction.

Rates of acute myocardial infarction (AMI) hospitalizations for elderly Medicare patients decreased during the previous decade. However, trends in population rates of AMI hospitalizations for all adults by subgroups have not been described. Using data from a large all-payer administrative database of hospitalizations, we calculated annual AMI hospitalization rates from 2001 through 2007. Trend analysis was performed across age, gender, and ethnicity categories using survey regression. Overall rate decreased from 314 to 222 AMI hospitalizations per 100,000 patients from 2001 through 2007, representing a 29.2% decrease. Significant decreases were observed in AMI hospitalization rate for each group by age categories (p <0.001) and by gender (p <0.001). When stratified by ethnicity and gender, age-adjusted AMI hospitalization rates in white men and women decreased by 30.8% and 31.4%, whereas black men and women had significantly slower rates of decrease of 13.6% and 12.6%, respectively. In conclusion, although the overall rate of AMI hospitalizations decreased from 2001 through 2007, the observed decrease was smaller for black patients compared to white patients across all age groups studied.