Uncovering Bugs in Distributed Storage Systems during Testing (not in Production!)

Testing distributed systems is challenging due to multiple sources of nondeterminism. Conventional testing techniques, such as unit, integration and stress testing, are ineffective in preventing serious but subtle bugs from reaching production. Formal techniques, such as TLA+, can only verify high-level specifications of systems at the level of logic-based models, and fall short of checking the actual executable code. In this paper, we present a new methodology for testing distributed systems. Our approach applies advanced systematic testing techniques to thoroughly check that the executable code adheres to its high-level specifications, which significantly improves coverage of important system behaviors. Our methodology has been applied to three distributed storage systems in the Microsoft Azure cloud computing platform. In the process, numerous bugs were identified, reproduced, confirmed and fixed. These bugs required a subtle combination of concurrency and failures, making them extremely difficult to find with conventional testing techniques. An important advantage of our approach is that a bug is uncovered in a small setting and witnessed by a full system trace, which dramatically increases the productivity of debugging

Statistical Analysis of Variation in the Human Plasma Proteome

Quantifying the variation in the human plasma proteome is an essential prerequisite for disease-specific biomarker detection. We report here on the longitudinal and individual variation in human plasma characterized by two-dimensional difference gel electrophoresis (2-D DIGE) using plasma samples from eleven healthy subjects collected three times over a two week period. Fixed-effects modeling was used to remove dye and gel variability. Mixed-effects modeling was then used to quantitate the sources of proteomic variation. The subject-to-subject variation represented the largest variance component, while the time-within-subject variation was comparable to the experimental variation found in a previous technical variability study where one human plasma sample was processed eight times in parallel and each was then analyzed by 2-D DIGE in triplicate. Here, 21 protein spots had larger than 50% CV, suggesting that these proteins may not be appropriate as biomarkers and should be carefully scrutinized in future studies. Seventy-eight protein spots showing differential protein levels between different individuals or individual collections were identified by mass spectrometry and further characterized using hierarchical clustering. The results present a first step toward understanding the complexity of longitudinal and individual variation in the human plasma proteome, and provide a baseline for improved biomarker discovery

High lubricity meets load capacity: cartilage mimicking bilayer structure by brushing up stiff hydrogels from subsurface

Natural articular cartilage has ultralow friction even at high squeezing pressure. Biomimicking cartilage with soft materials has been and remains a grand challenge in the fields of materials science and engineering. Inspired by the unique structural features of the articular cartilage, as well as by its remarkable lubrication mechanisms dictated by the properties of the superficial layers, a novel archetype of cartilage‐mimicking bilayer material by robustly entangling thick hydrophilic polyelectrolyte brushes into the subsurface of a stiff hydrogel substrate is developed. The topmost soft polymer layer provides effective aqueous lubrication, whereas the stiffer hydrogel layer used as a substrate delivers the load‐bearing capacity. Their synergy is capable of attaining low friction coefficients (order 0.010) under heavily loaded conditions (order 10 MPa contact pressure) in water environment, a performance incredibly close to that of natural articular cartilage. The bioinspired material can maintain low friction even when subjected to 50k reciprocating cycles under high contact pressure, with almost no wear observed on the sliding track. These findings are theoretically explained and compounded by multiscale simulations used to shed light on the mechanisms responsible for this remarkable performance. This work opens innovative technology routes for developing cartilage‐mimicking ultralow friction soft materials

A cartilage-inspired lubrication system

Articular cartilage is an example of a highly efficacious water-based, natural lubrication system that is optimized to provide low friction and wear protection at both low and high loads and sliding velocities. One of the secrets of cartilage\u27s superior tribology comes from a unique, multimodal lubrication strategy consisting of both a fluid pressurization mediated lubrication mechanism and a boundary lubrication mechanism supported by surface bound macromolecules. Using a reconstituted network of highly interconnected cellulose fibers and simple modification through the immobilization of polyelectrolytes, we have recreated many of the mechanical and chemical properties of cartilage and the cartilage lubrication system to produce a purely synthetic material system that exhibits some of the same lubrication mechanisms, time dependent friction response, and high wear resistance as natural cartilage tissue. Friction and wear studies demonstrate how the properties of the cellulose fiber network can be used to control and optimize the lubrication and wear resistance of the material surfaces and highlight what key features of cartilage should be duplicated in order to produce a cartilage-mimetic lubrication system

The Effect of Keyboard-Based Word Processing on Students With Different Working Memory Capacity During the Process of Academic Writing

This study addresses the current debate about the beneficial effects of text processing software on students with different working memory (WM) during the process of academic writing, especially with regard to the ability to display higher-level conceptual thinking. A total of 54 graduate students (15 male, 39 female) wrote one essay by hand and one by keyboard. Our results show a beneficial effect of text processing software, in terms of both the qualitative and quantitative writing output. A hierarchical cluster analysis was used to detect distinct performance groups in the sample. These performance groups mapped onto three differing working memory profiles. The groups with higher mean WM scores manifested superior writing complexity using a keyboard, in contrast to the cluster with the lowest mean WM. The results also point out that more revision during the writing process itself does not inevitably reduce the quality of the final output