Minimal information for studies of extracellular vesicles 2018 (MISEV2018):a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines

The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points

You Can Load a Die But You Can't Bias a Coin

Dice can be loaded|that is, one can easily alter a die so that the probabilities of landing on the six sides are dramatically unequal. However, it is not possible to bias a coin ip|that is, one cannot, for example, weight a coin so that it is substantially more likely to land \heads" than \tails" when ipped and caught in the hand in the usual manner. Coin tosses can be biased only if the coin is allowed to bounce or be spun rather than simply ipped in the air

A Class Project in Survey Sampling

atistics, University of California, Berkeley 1 Organization This is a major endeavor that takes the better part of a semester to complete, although the e#ort required from each student is limited to a few hours over one or two weeks. To prepare, we choose the topic of study before the semester starts and collect background materials (newspaper clippings, research papers, and reports) to frame the problem, contact authorities on the topic for advice, and arrange guest lectures for our class. We break the project into smaller manageable tasks: questionnaire design, sampling plan development, data collection, analysis, and report writing. Groups of students work on di#erent tasks, which the instructor oversees. We introduce the project early in the semester, and set deadlines for completion of the main pieces. If the class is large, more than one group works on the same task. For example, two groups may design questionnaires, the advantage being tat we merge the best parts of each in

MFCAD++ Dataset. Dataset for paper: "Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition, Computer-Aided Design"

Machining feature recognition dataset for deep learning consisting of B-Rep CAD models labelled on each B-Rep face with a machining feature. Dataset for paper: "Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition, Computer-Aided Design" - The MFCAD++ dataset is a machining feature recognition dataset containing B-Rep CAD models. - Each CAD model has been saved as a STEP file. - The CAD models were automatically generated using the PythonOCC CAD software. - For each CAD model, a machining feature class label is given on each B-Rep face. - These labels can easily be extracted from the STEP files. - The labels are given in "feature_labels.txt". - The dataset has been split into "train", "val" and "test" directories using a 70:15:15 split as per the original paper. - These splits are given in the files: "train.txt", "val.txt" and "test.txt". - There are training_set=41766, val_set=8950 & test_set=8949 samples, with 59665 samples in total. - For more information on hierachical B-Rep graphs store in H5DF files see "h5_structure.h5". If used please reference the paper: Colligan AR, Robinson TR, Nolan DC, Hua Y, Cao W. Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition, Computer-Aided Desig