Explicit tracking of uncertainty increases the power of quantitative rule-of-thumb reasoning in cell biology

"Back-of-the-envelope" or "rule-of-thumb" calculations involving rough estimates of quantities play a central scientific role in developing intuition about the structure and behaviour of physical systems, for example in so-called `Fermi problems' in the physical sciences. Such calculations can be used to powerfully and quantitatively reason about biological systems, particularly at the interface between physics and biology. However, substantial uncertainties are often associated with values in cell biology, and performing calculations without taking this uncertainty into account may limit the extent to which results can be interpreted for a given problem. We present a means to facilitate such calculations where uncertainties are explicitly tracked through the line of reasoning, and introduce a `probabilistic calculator' called Caladis, a web tool freely available at www.caladis.org, designed to perform this tracking. This approach allows users to perform more statistically robust calculations in cell biology despite having uncertain values, and to identify which quantities need to be measured more precisely in order to make confident statements, facilitating efficient experimental design. We illustrate the use of our tool for tracking uncertainty in several example biological calculations, showing that the results yield powerful and interpretable statistics on the quantities of interest. We also demonstrate that the outcomes of calculations may differ from point estimates when uncertainty is accurately tracked. An integral link between Caladis and the Bionumbers repository of biological quantities further facilitates the straightforward location, selection, and use of a wealth of experimental data in cell biological calculations.Comment: 8 pages, 3 figure

The Need for Medically Aware Video Compression in Gastroenterology

Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored. Furthermore, systems in practice rely on standard video codecs, which naively allocate bits between medically relevant frames or parts of frames. In this work, we present an empirical study of some deficiencies of classical codecs on gastroenterology videos, and motivate our ongoing work to train a learned compression model for colonoscopy videos. We show that two of the most common classical codecs, H264 and HEVC, compress medically relevant frames statistically significantly worse than medically nonrelevant ones, and that polyp detector performance degrades rapidly as compression increases. We explain how a learned compressor could allocate bits to important regions and allow detection performance to degrade more gracefully. Many of our proposed techniques generalize to medical video domains beyond gastroenterologyComment: Medical Imaging Meets NeurIPS Workshop 2022, NeurIPS 202

What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions correspond to the recipe. Our technique relies on an HMM to align the recipe steps to the (automatically generated) speech transcript. We then refine this alignment using a state-of-the-art visual food detector, based on a deep convolutional neural network. We show that our technique outperforms simpler techniques based on keyword spotting. It also enables interesting applications, such as automatically illustrating recipes with keyframes, and searching within a video for events of interest.Comment: To appear in NAACL 201

Full Resolution Image Compression with Recurrent Neural Networks

This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study "one-shot" versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%-8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.Comment: Updated with content for CVPR and removed supplemental material to an external link for size limitation

Simulator for Testing Spacecraft Separation Devices

A report describes the main features of a system for testing pyrotechnic and mechanical devices used to separate spacecraft and modules of spacecraft during flight. The system includes a spacecraft simulator [also denoted a large mobility base (LMB)] equipped with air thrusters, sensors, and data-acquisition equipment. The spacecraft simulator floats on air bearings over an epoxy-covered concrete floor. This free-flotation arrangement enables simulation of motion in outer space in three degrees of freedom: translation along two orthogonal horizontal axes and rotation about a vertical axis. The system also includes a static stand. In one application, the system was used to test a bolt-retraction system (BRS) intended for separation of the lifting-body and deorbit-propulsion stages of the X- 38 spacecraft. The LMB was connected via the BRS to the static stand, then pyrotechnic devices that actuate the BRS were fired. The separation distance and acceleration were measured. The report cites a document, not yet published at the time of reporting the information for this article, that is said to present additional detailed information

Mitochondrial heterogeneity

Cell-to-cell heterogeneity drives a range of (patho)physiologically important phenomena, such as cell fate and chemotherapeutic resistance. The role of metabolism, and particularly mitochondria, is increasingly being recognised as an important explanatory factor in cell-to-cell heterogeneity. Most eukaryotic cells possess a population of mitochondria, in the sense that mitochondrial DNA (mtDNA) is held in multiple copies per cell, where the sequence of each molecule can vary. Hence intra-cellular mitochondrial heterogeneity is possible, which can induce inter-cellular mitochondrial heterogeneity, and may drive aspects of cellular noise. In this review, we discuss sources of mitochondrial heterogeneity (variations between mitochondria in the same cell, and mitochondrial variations between supposedly identical cells) from both genetic and non-genetic perspectives, and mitochondrial genotype-phenotype links. We discuss the apparent homeostasis of mtDNA copy number, the observation of pervasive intra-cellular mtDNA mutation (we term `microheteroplasmy') and developments in the understanding of inter-cellular mtDNA mutation (`macroheteroplasmy'). We point to the relationship between mitochondrial supercomplexes, cristal structure, pH and cardiolipin as a potential amplifier of the mitochondrial genotype-phenotype link. We also discuss mitochondrial membrane potential and networks as sources of mitochondrial heterogeneity, and their influence upon the mitochondrial genome. Finally, we revisit the idea of mitochondrial complementation as a means of dampening mitochondrial genotype-phenotype links in light of recent experimental developments. The diverse sources of mitochondrial heterogeneity, as well as their increasingly recognised role in contributing to cellular heterogeneity, highlights the need for future single-cell mitochondrial measurements in the context of cellular noise studies

Hybrid expert ensembles for identifying unreliable data in citizen science

Citizen science utilises public resources for scientific research. BirdTrack is such a project established in 2004 by the British Trust for Ornithology (BTO) for the public to log their bird observations through its web or mobile applications. It has accumulated over 40 million observations. However, the veracity of these observations needs to be checked and the current process involves time-consuming interventions by human experts. This research therefore aims to develop a more efficient system to automatically identify unreliable observations from large volume of records. This paper presents a novel approach — a Hybrid Expert Ensemble System (HEES) that combines an Expert System (ES) and machine induced models to perform the intended task. The ES is built based on human expertise and used as a base member of the ensemble. Other members are decision trees induced from county-based data. The HEES uses accuracy and diversity as criteria to select its members with an aim of improving its accuracy and reliability. The experiments were carried out using the county-based data and the results indicate that (1) the performance of the expert system is reasonable for some counties but varied considerably on others. (2) An HEES is more accurate and reliable than the Expert System and also other individual models, with Sensitivity of 85% for correctly identifying unreliable observations and Specificity of 99% for reliable observations. These results demonstrated that the proposed approach has the ability to be an alternative or additional means to validate the observations in a timely and cost-effective manner and also has a potential to be applied in other citizen science projects where the huge amount of data needs to be checked effectively and efficiently