New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift

Testing theories of hierarchical structure formation requires estimating the distribution of galaxy morphologies and its change with redshift. One aspect of this investigation involves identifying galaxies with disturbed morphologies (e.g., merging galaxies). This is often done by summarizing galaxy images using, e.g., the CAS and Gini-M20 statistics of Conselice (2003) and Lotz et al. (2004), respectively, and associating particular statistic values with disturbance. We introduce three statistics that enhance detection of disturbed morphologies at high-redshift (z ~ 2): the multi-mode (M), intensity (I), and deviation (D) statistics. We show their effectiveness by training a machine-learning classifier, random forest, using 1,639 galaxies observed in the H band by the Hubble Space Telescope WFC3, galaxies that had been previously classified by eye by the CANDELS collaboration (Grogin et al. 2011, Koekemoer et al. 2011). We find that the MID statistics (and the A statistic of Conselice 2003) are the most useful for identifying disturbed morphologies. We also explore whether human annotators are useful for identifying disturbed morphologies. We demonstrate that they show limited ability to detect disturbance at high redshift, and that increasing their number beyond approximately 10 does not provably yield better classification performance. We propose a simulation-based model-fitting algorithm that mitigates these issues by bypassing annotation.Comment: 15 pages, 14 figures, accepted for publication in MNRA

Faster Cover Trees

Abstract The cover tree data structure speeds up exact nearest neighbor queries over arbitrary metric spaces On standard benchmark datasets, we reduce the number of distance computations by 10-50%. On a large-scale bioinformatics dataset, we reduce the number of distance computations by 71%. On a large-scale image dataset, our parallel algorithm with 16 cores reduces tree construction time from 3.5 hours to 12 minutes

Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models difficult to interpret, named black box. Moreover, people have difficulty to trust models that cannot be explained. In particular for machine learning, many groups are investigating new methods able to explain black box models. These methods usually look inside the black models to explain their inner work. By doing so, they allow the interpretation of the decision making process used by black box models. Among the recently proposed model interpretation methods, there is a group, named local estimators, which are designed to explain how the label of particular instance is predicted. For such, they induce interpretable models on the neighborhood of the instance to be explained. Local estimators have been successfully used to explain specific predictions. Although they provide some degree of model interpretability, it is still not clear what is the best way to implement and apply them. Open questions include: how to best define the neighborhood of an instance? How to control the trade-off between the accuracy of the interpretation method and its interpretability? How to make the obtained solution robust to small variations on the instance to be explained? To answer to these questions, we propose and investigate two strategies: (i) using data instance properties to provide improved explanations, and (ii) making sure that the neighborhood of an instance is properly defined by taking the geometry of the domain of the feature space into account. We evaluate these strategies in a regression task and present experimental results that show that they can improve local explanations

Is Aggressive Surgical Palliation of Proximal Bile Duct Cancer With Involvement of Both Main Hepatic Ducts Worthwhile?

The only curative treatment for proximal bile duct cancer with involvement of both main hepatic ducts is liver transplantation. Most patients do not fulfill the requirements for liver transplantation. Our treatment strategy in appropriate cases is palliative tumor resection and reconstruction of the biliary passage by sutureless bilioenteric anastomosis. We have treated 12 patients, 5 in combination with intraluminal and percutaneous radiotherapy. Our results indicate that this strategy leads to effective palliation in some cases provided that only microscopic residual tumor is left in-situ. Our survival times compare favourably with survival after liver transplantation

Rescue bedside laparotomy in the intensive care unit in patients too unstable for transport to the operating room

INTRODUCTION: The prognoses of critically ill patients with a requirement for emergency laparotomy and severe respiratory and/or hemodynamic instability precluding transport to the operating room (OR) are often fatal without surgery. Attempting emergency surgery at the bedside might equally result in an adverse outcome. However, risk factors and predictors that could support clinical decision making have not been identified so far. This study describes the clinical characteristics, indicative pathophysiology and outcomes in patients undergoing resuscitative laparotomy in the intensive care unit (ICU). METHODS: This was a retrospective observational study of all critically ill adult patients undergoing resuscitative laparotomy in the ICUs of a German university hospital from January 2005 to July 2013. Clinical characteristics, risk factors, and treatments were compared between survivors and non-survivors. The primary endpoint was 28-day survival. RESULTS: A total of 41 patients with a median age of 64 (21 to 83) were included. The most frequent reasons for ICU admission were sepsis, pneumonia, and pancreatic surgery. All patients were mechanically ventilated, receiving vasopressors, and were in multiple organ failure. Twenty-nine patients (70.7%) were on renal replacement therapy and two patients (4.9%) on extracorporeal membrane oxygenation. The main reasons for surgery were suspected intra-abdominal bleeding (39.0%), suspected intestinal ischemia (24.4%) or abdominal compartment syndrome (24.4%). Twenty-eight-day, ICU and hospital mortalities were 75.6%, 80.5%, and 82.9%, respectively. In six out of ten patients (60%) who survived surgery for more than 28 days, bedside laparotomy was rated as a life-saving procedure by an interdisciplinary group of the investigators. CONCLUSIONS: These findings suggest that in selected critically ill patients with a vital indication for emergency laparotomy and severe cardiopulmonary instability precluding transport to the OR, a bedside resuscitative laparotomy in the ICU can be considered as a rescue procedure, even though very high mortality is to be expected

Metastatic Esophageal Carcinoma Cells Exhibit Reduced Adhesion Strength and Enhanced Thermogenesis

Despite continuous improvements in multimodal therapeutic strategies, esophageal carcinoma maintains a high mortality rate. Metastases are a major life-limiting component; however, very little is known about why some tumors have high metastatic potential and others not. In this study, we investigated thermogenic activity and adhesion strength of primary tumor cells and corresponding metastatic cell lines derived from two patients with metastatic adenocarcinoma of the esophagus. We hypothesized that the increased metastatic potential of the metastatic cell lines correlates with higher thermogenic activity and decreased adhesion strength. Our data show that patient-derived metastatic esophageal tumor cells have a higher thermogenic profile as well as a decreased adhesion strength compared to their corresponding primary tumor cells. Using two paired esophageal carcinoma cell lines of primary tumor and lymph nodes makes the data unique. Both higher specific thermogenesis profile and decreased adhesion strength are associated with a higher metastatic potential. They are in congruence with the clinical patient presentation. Understanding these functional, biophysical properties of patient derived esophageal carcinoma cell lines will enable us to gain further insight into the mechanisms of metastatic potential of primary tumors and metastases. Microcalorimetric evaluation will furthermore allow for rapid assessment of new treatment options for primary tumor and metastases aimed at decreasing the metastatic potential

Robotic rectal resection preserves anorectal function: Systematic review and meta-analysis.

AbstractBackgroundImproving survival rates in rectal cancer patients has generated a growing interest in functional outcomes after total mesorectal excision (TME). The well‐established low anterior resection syndrome (LARS) score assesses postoperative anorectal impairment after TME. Our meta‐analysis is the first to compare bowel function after open, laparoscopic, transanal, and robotic TME.MethodsAll studies reporting functional outcomes after rectal cancer surgery (LARS score) were included, and were compared with a consecutive series of robotic TME (n = 48).ResultsThirty‐two publications were identified, including 5 565 patients. Anorectal function recovered significantly better within one year after robotic TME (3.8 [95%CI –9.709–17.309]) versus laparoscopic TME (26.4 [95%CI 19.524–33.286]), p = 0.006), open TME (26.0 [95%CI 24.338–29.702], p = 0.002) and transanal TME (27.9 [95%CI 22.127–33.669], p = 0.003).ConclusionsRobotic TME enables better recovery of anorectal function compared to other techniques. Further prospective, high‐quality studies are needed to confirm the benefits of robotic surgery

Identification of Recessively Inherited Genetic Variants Potentially Linked to Pancreatic Cancer Risk

Although 21 pancreatic cancer susceptibility loci have been identified in individuals of European ancestry through genome-wide association studies (GWASs), much of the heritability of pancreatic cancer risk remains unidentified. A recessive genetic model could be a powerful tool for identifying additional risk variants. To discover recessively inherited pancreatic cancer risk loci, we performed a re-analysis of the largest pancreatic cancer GWAS, the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including 8,769 cases and 7,055 controls of European ancestry. Six single nucleotide polymorphisms (SNPs) showed associations with pancreatic cancer risk according to a recessive model of inheritance. We replicated these variants in 3,212 cases and 3,470 controls collected from the PANcreatic Disease ReseArch (PANDoRA) consortium. The results of the meta-analyses confirmed that rs4626538 (7q32.2), rs7008921 (8p23.2) and rs147904962 (17q21.31) showed specific recessive effects (p10-3), although none of the six SNPs reached the conventional threshold for genome-wide significance (p < 5×10-8). Additional bioinformatic analysis explored the functional annotations of the SNPs and indicated a possible relationship between rs36018702 and expression of the BCL2L11 and BUB1 genes, which are known to be involved in pancreatic biology. Our findings, while not conclusive, indicate the importance of considering non-additive genetic models when performing GWAS analysis. The SNPs associated with pancreatic cancer in this study could be used for further meta-analysis for recessive association of SNPs and pancreatic cancer risk and might be a useful addiction to improve the performance of polygenic risk scores