835 research outputs found
Observation originale après disjonction cervico-occipitale : un syndrome de Gerhardt associé à un syndrome de Riegel
An active machine learning discovery platform for membrane-disrupting and pore-forming peptides
Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentration gradients. Likewise, the delivery of drugs and intracellular proteins aided by pore-forming agents are of interest in treating malfunctioning cells. Known PFPs tend to be up to 50 residues in length, which is commensurate with the thickness of a lipid bilayer. Accordingly, few short PFPs are known. Here we show that the discovery of PFPs can be accelerated via an active machine learning approach. The approach identified 71 potential PFPs from the 25.6 billion octapeptide sequence space; 13 sequences were tested experimentally, and all were found to have the predicted membrane-disrupting ability, with 1 forming highly stable pores. Experimental verification of the predicted pore-forming ability demonstrated that a range of short peptides can form pores in membranes, while the positioning and characteristics of residues that favour pore-forming behaviour were identified. This approach identified more ultrashort (8-residues, unmodified, non-cyclic) PFPs than previously known. We anticipate our findings and methodology will be useful in discovering new pore-forming and membrane-disrupting peptides for range of applications from nanoreactors to therapeutics
Exposure factors manual
ABSTRACT: Assessing health risks associated with potential exposure to chemicals from petroleum or petrochemical operations requires the consideration of multiple exposure pathways. These pathways include ingestion of water, food, or soil, inhalation of vapors or airborne particulate, and dermal absorption from contaminated soil, water, or by direct skin contact. To estimate the exposures for each pathway, a number of variables related to exposure, that is, exposure factors, are needed. Some categories of exposure factors include physiologic factors (e.g., body weight), time-activity factors (e.g., time spent at home), and contact rate factors (e.g., soil ingestion rate). This manual is organized by exposure factor category and includes a description of selected exposure factors commonly used in risk assessments, a brief summary, and an evaluation of the current scientific data supporting a recommended point value for each factor, and available information on the known distributions. It is hoped that this information will promote consistency and quality among various risk assessment activities
APE1 and NPM1 protect cancer cells from platinum compounds cytotoxicity and their expression pattern has a prognostic value in TNBC
Eating self-efficacy as predictor of long-term weight loss and obesity-specific quality of life after sleeve gastrectomy: A prospective cohort study
Under embargo until: 15.12.2019Background: A person's confidence to control eating, eating self-efficacy (ESE), has been identified as a target for long-term weight management in nonsurgical weight loss interventions, but has to a limited extent been studied after bariatric surgery. Objective: We investigated the association between ESE, weight loss, and obesity-specific quality of life (QOL) after sleeve gastrectomy (SG). Setting: A single-center longitudinal study. Methods: Data from adult patients were collected before SG, and at mean 16 months (±standard deviation 4 mo) and 55 (±4) months postoperatively. ESE was measured by the Weight Efficacy Lifestyle Questionnaire Short-Form. Multiple regression analyses were performed with excess body mass index loss (%EBMIL) and obesity-specific QOL as dependent variables. Age, sex, and other preoperative values were covariates in all models. Results: Of 114 preoperative patients, 91 (80%) and 84 (74%) were available for follow-up 16 and 55 months after SG, respectively. Mean %EBMIL from baseline to 16 and 55 months was 76% (95% confidence interval: 71.9, 79.6) and 67% (95% confidence interval: 61.9, 72.2), respectively. Preoperative ESE scores improved significantly at both 16 and 55 months (P = .002) but did not predict postoperative %EBMIL or QOL at 55 months (β = −.08, P = .485). Greater change in ESE from 0 to 16 months predicted higher %EBMIL (β = .34, P = .013) at 55 months, and improvements in ESE from 0 to 55 months were significantly associated with higher %EBMIL (β = .46, P = .001) and obesity-specific QOL (β = .50, P < .001) 55 months after SG. Conclusion: Significant improvements in ESE were seen at 16 months, and remained high at 55 months after SG in this cohort. Patients who improved their ESE the most also experienced the highest weight loss and obesity-specific QOL 5 years postoperatively. Future research should address whether enhancement of ESE corresponds to sustained improvements in eating behavior after bariatric surgery.acceptedVersio
Generic 3D Representation via Pose Estimation and Matching
Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose estimation and wide baseline feature matching. Our
method is based upon the premise that by providing supervision over a set of
carefully selected foundational tasks, generalization to novel tasks and
abstraction capabilities can be achieved. We empirically show that the internal
representation of a multi-task ConvNet trained to solve the above core problems
generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
estimation, surface normal estimation) without the need for fine-tuning and
shows traits of abstraction abilities (e.g., cross-modality pose estimation).
In the context of the core supervised tasks, we demonstrate our representation
achieves state-of-the-art wide baseline feature matching results without
requiring apriori rectification (unlike SIFT and the majority of learned
features). We also show 6DOF camera pose estimation given a pair local image
patches. The accuracy of both supervised tasks come comparable to humans.
Finally, we contribute a large-scale dataset composed of object-centric street
view scenes along with point correspondences and camera pose information, and
conclude with a discussion on the learned representation and open research
questions.Comment: Published in ECCV16. See the project website
http://3drepresentation.stanford.edu/ and dataset website
https://github.com/amir32002/3D_Street_Vie
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