Segmentation Propagation in ImageNet

Abstract. ImageNet is a large-scale hierarchical database of object classes. We propose to automatically populate it with pixelwise segmentations, by leveraging existing manual annotations in the form of class labels and bounding-boxes. The key idea is to recursively exploit images segmented so far to guide the segmentation of new images. At each stage this propagation process expands into the images which are easiest to segment at that point in time, e.g. by moving to the semantically most related classes to those segmented so far. The propagation of segmentation occurs both (a) at the image level, by transferring existing segmentations to estimate the probability of a pixel to be foreground, and (b) at the class level, by jointly segmenting images of the same class and by importing the appearance models of classes that are already segmented. Through an experiment on 577 classes and 500k images we show that our technique (i) annotates a wide range of classes with accurate segmentations; (ii) effectively exploits the hierarchical structure of ImageNet; (iii) scales efficiently; (iv) outperforms a baseline GrabCut [1] initialized on the image center, as well as our recent segmentation transfer technique [2] on which this paper is based. Moreover, our method also delivers state-of-the-art results on the recent iCoseg dataset for co-segmentation.

In-hospital Delay Increases the Risk of Perforation in Adults with Appendicitis

Background: The influence of in-hospital delay (time between admission and operation) on outcome after appendectomy is controversial. Methods: A total of 1,827 adult patients underwent open or laparoscopic appendectomy for suspected appendicitis in eleven Swiss hospitals between 2003 and 2006. Of these, 1,675 patients with confirmed appendicitis were included in the study. Groups were defined according in-hospital delay (≤12 vs. >12h). Results: Delay>12h was associated with a significantly higher frequency of perforated appendicitis (29.7 vs. 22.7%; P=0.010) whereas a delay of 6 or 9h was not. Size of institution, time of admission, and surgical technique (laparoscopic vs. open) were independent factors influencing in-hospital delay. Admission during regular hours was associated with higher age, higher frequency of co-morbidity, and higher perforation rate compared to admission after hours. The logistic regression identified four independent factors associated with an increased perforation rate: age (≤65years vs. >65years, odds ratio (OR) 4.5, P0 vs. Charlson index=0, OR 2.3, P12 vs. ≤12h, OR 1.5, P=0.005). Perforation was associated with an increased reintervention rate (13.4 vs. 1.6%; P<0.001) and longer length of hospital stay (9.5 vs. 4.4days; P<0.001). Conclusions: In-hospital delay negatively influences outcome after appendectomy. In-hospital delay of more than 12h, age over 65years, time of admission during regular hours, and the presence of co-morbidity are all independent risk factors for perforation. Perforation was associated with a higher reintervention rate and increased length of hospital sta

Real-Time Face Pose Estimation from Single Range Images

We present a real-time algorithm to estimate the 3D pose of a previously unseen face from a single range image. Based on a novel shape signature to identify noses in range images, we generate candidates for their positions, and then generate and evaluate many pose hypotheses in parallel using modern graphics processing units (GPUs). We developed a novel error function that compares the input range image to precomputed pose images of an average face model. The algorithm is robust to large pose variations of plusmn90deg yaw, plusmn45deg pitch and plusmn30deg roll rotation, facial expression, partial occlusion, and works for multiple faces in the field of view. It correctly estimates 97.8% of the poses within yaw and pitch error of 15deg at 55.8 fps. To evaluate the algorithm, we built a database of range images with large pose variations and developed a method for automatic ground truth annotation.Engineering and Applied Science

Metabolomics guides rational development of a simplified cell culture medium for drug screening against <i>Trypanosoma brucei</i>

n vitro culture methods underpin many experimental approaches to biology and drug discovery. The modification of established cell culture methods to make them more biologically relevant or to optimize growth is traditionally a laborious task. Emerging metabolomic technology enables the rapid evaluation of intra- and extracellular metabolites and can be applied to the rational development of cell culture media. In this study, untargeted semiquantitative and targeted quantitative metabolomic analyses of fresh and spent media revealed the major nutritional requirements for the growth of bloodstream form &lt;i&gt;Trypanosoma brucei&lt;/i&gt;. The standard culture medium (HMI11) contained unnecessarily high concentrations of 32 nutrients that were subsequently removed to make the concentrations more closely resemble those normally found in blood. Our new medium, Creek's minimal medium (CMM), supports in vitro growth equivalent to that in HMI11 and causes no significant perturbation of metabolite levels for 94% of the detected metabolome (&#60;3-fold change; α = 0.05). Importantly, improved sensitivity was observed for drug activity studies in whole-cell phenotypic screenings and in the metabolomic mode of action assays. Four-hundred-fold 50% inhibitory concentration decreases were observed for pentamidine and methotrexate, suggesting inhibition of activity by nutrients present in HMI11. CMM is suitable for routine cell culture and offers important advantages for metabolomic studies and drug activity screening

Intracerebral Abscess:An Uncommon Infection in a Psoriatic Arthritis Patient under Long-Term Certolizumab Pegol Treatment

The treatment of autoimmune disorders like psoriatic arthritis, include anti-tumor necrosis factor biological agents, such as Certolizumab Pegol, which presents notable efficacy, however at the same time elicits concerns regarding susceptibility to infections.In our case study, a 49-year-old male with psoriatic arthritis treated with Certolizumab Pegol and oral glucocorticosteroids, developed unilateral paresthesia prompting neurological evaluation.Magnetic Resonance Imaging (MRI) of the cerebrum revealed a lesion suggestive of an intracerebral abscess. Despite negative cultures, he underwent tooth extraction, received antibiotics and glucocorticosteroids with full clinical and paraclinical recovery.Rheumatologists should consider a brain abscess as a differential diagnosis in patients receiving immunosuppressive medication with the onset of neurological symptoms. Intracerebral abscesses, albeit rare as a complication, necessitate prompt diagnosis, effective treatment, and vigilant monitoring to optimize patient outcomes and quality of life

Public opinion and the morality of drug use : experimental and time-series analyses

Trends in public opinion research suggest that Americans are becoming more liberal with respect to drugs. This research is limited by the absence of a reliable and valid indicator of drug sentiment, which inhibits our understanding of how and why public opinion toward drugs evolves over time. Recent studies also suggest that Americans are morally approving of drug use in certain contexts and are supportive of “harm reduction” or other progressive drug policies viewed as solutions to the drug war, but the underlying reasons as to why are not completely understood. This dissertation uses time-series analysis and two survey experiments to improve our understanding of public opinion and moral attitudes toward drugs. Study 1 utilizes the dyad ratios algorithm and 298 administrations of 66 unique survey indicators to develop a new nationally representative measure of public sentiment toward drugs from 1969 to 2021. I find that drug sentiment has become quite liberal in recent years, and this helps explain why Americans have become less punitive over time. In Study 2, I use a factorial survey experiment (N=524) to elucidate moral attitudes toward drug use. Findings suggest that moral attitudes are shaped by attributes of the person who uses drugs, aspects inherent to the situation, and respondent-level characteristics. Finally in Study 3, I use another factorial survey experiment (N=537) to clarify our understanding of public support and willingness to fund various progressive drug policies. I find that drug policy attitudes are predominately driven by respondent-level characteristics. This dissertation has theoretical implications for scholars investigating public opinion of drugs and provides suggestions to researchers or policymakers looking to reduce negative attitudes toward people who use drugs or grow public support for progressive drug policies

Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence

We present a principled framework for inferring pixel labels in weakly-annotated image datasets. Most previous, example-based approaches to computer vision rely on a large corpus of densely labeled images. However, for large, modern image datasets, such labels are expensive to obtain and are often unavailable. We establish a large-scale graphical model spanning all labeled and unlabeled images, then solve it to infer pixel labels jointly for all images in the dataset while enforcing consistent annotations over similar visual patterns. This model requires significantly less labeled data and assists in resolving ambiguities by propagating inferred annotations from images with stronger local visual evidences to images with weaker local evidences. We apply our proposed framework to two computer vision problems, namely image annotation with semantic segmentation, and object discovery and co-segmentation (segmenting multiple images containing a common object). Extensive numerical evaluations and comparisons show that our method consistently outperforms the state-of-the-art in automatic annotation and semantic labeling, while requiring significantly less labeled data. In contrast to previous co-segmentation techniques, our method manages to discover and segment objects well even in the presence of substantial amounts of noise images (images not containing the common object), as typical for datasets collected from Internet search

Depressive Symptoms in Danish Elite Athletes Using the Major Depressive Inventory (MDI) and the Center for Epidemiological Studies Depression Scale (CES-D)

Background: The prevalence of depressive symptoms among athletes is an ongoing debate in the scientific literature. Aims: The aim of the current study was to assess the prevalence of depressive symptoms in Danish elite athletes and to evaluate the psychometric properties of the Major Depressive Inventory (MDI) and the Center for Epidemiological Studies Depression Scale (CES-D) in athletes. Methods: The total sample comprised 996 athletes from two cross-sectional studies using the MDI (n = 409) and the CES-D (n = 587). Results: Using the original cut-off points, the MDI found 8.6% and the CES-D found 22.0% at risk of depression. Using alternative cut-off points recommended in the literature, both instruments detected 10-11% of athletes at risk of depression. No statistically significant differences were found related to age, injury, and type of sport between high risk and low risk groups, whereas female gender was identified as a risk factor for higher depressive symptoms. Principal component analyses confirmed a single factor structure in both instruments with sufficient item loadings on the first component and Cronbach α values of .89 and .88. Discussion: We recommend regular screening of depressive symptoms in elite athletes, with MDI and CES-D as reliable instrument for that purpose