Coz: Finding Code that Counts with Causal Profiling

Improving performance is a central concern for software developers. To locate optimization opportunities, developers rely on software profilers. However, these profilers only report where programs spent their time: optimizing that code may have no impact on performance. Past profilers thus both waste developer time and make it difficult for them to uncover significant optimization opportunities. This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact. Causal profiling works by running performance experiments during program execution. Each experiment calculates the impact of any potential optimization by virtually speeding up code: inserting pauses that slow down all other code running concurrently. The key insight is that this slowdown has the same relative effect as running that line faster, thus "virtually" speeding it up. We present Coz, a causal profiler, which we evaluate on a range of highly-tuned applications: Memcached, SQLite, and the PARSEC benchmark suite. Coz identifies previously unknown optimization opportunities that are both significant and targeted. Guided by Coz, we improve the performance of Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as much as 68%; in most cases, these optimizations involve modifying under 10 lines of code.Comment: Published at SOSP 2015 (Best Paper Award

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

THE ROLE OF GENE EXPRESSION NOISE IN MAMMALIAN CELL SURVIVAL

Drug resistance and metastasis remain obstacles to effective cancer treatment. A major challenge contributing to this problem is cellular heterogeneity. Even in the same environment, cells with identical genomes can display cell-to-cell differences in gene expression, also known as gene expression noise. Gene expression noise can vary in magnitude in a population or in fluctuation time scales, which is influenced by gene regulatory networks. Currently, it is unclear how gene expression noise from gene regulatory networks contributes to drug survival outcomes in mammalian cells. An isogenic cell line with a noise-modulating genetic system tuned to the same mean is required. Additionally, how modulating endogenous mean gene expression and noise in living cells influences pro-survival metastatic state transitions remains unanswered. To address these knowledge gaps, I implemented an exogenous synthetic biology approach to control noise for the drug resistance gene PuroR in drug survival while complementing with endogenous expression measurements of the pro-metastatic gene BACH1 as a correlate for metastatic survival. For exogenous control, I developed synthetic gene circuits in Chinese Hamster Ovary (CHO) cells based on positive and negative feedback that tune noise for PuroR at identical mean expression. At a decoupled noise point, isogenic cells were treated with various Puromycin concentrations. Evolution experiments revealed that noise hurts drug resistance during low drug dosage while facilitating resistance at a high Puromycin concentration. Drug adaptation for the low-noise gene circuit relied on intra-circuit mutations while the high-noise circuit did not and became re-sensitized to drug after removing circuit induction. To implement the endogenous approach, I tagged the endogenous BACH1 gene with the mCherry fluorescent protein in six HEK293 clones. Molecular perturbations such as serum starvation and long-term hemin treatment altered mean fluorescence in at least one clone. Additionally, monitoring migration after cell wounding revealed increased non-uniform fluorescence at the wound edge. The increased mean fluorescence for the potentially bistable HEK293 clone 2C10 during hemin treatment may reflect altered BACH1 state transitions. Overall, noise enhanced the probability of cells to reach an expression level that confers survival during drug treatment while hemin perturbations may induce a pro-survival metastatic transition via BACH1 expression

Recent Changes in Drug Abuse Scenario: The Novel Psychoactive Substances (NPS) Phenomenon

copyright 2019 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.Final Published versio

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976