Flexible automated approach for quantitative liquid handling of complex biological samples

A fully automated protein precipitation technique for biological sample preparation has been developed for the quantitation of drugs in various biological matrixes. All liquid handling during sample preparation was automated using a Hamilton MicroLab Star Robotic workstation, which included the preparation of standards and controls from a Watson laboratory information management system generated work list, shaking of 96-well plates, and vacuum application. Processing time is less than 30 s per sample or approximately 45 min per 96-well plate, which is then immediately ready for injection onto an LC-MS/MS system. An overview of the process workflow is discussed, including the software development. Validation data are also provided, including specific liquid class data as well as comparative data of automated vs manual preparation using both quality controls and actual sample data. The efficiencies gained from this automated approach are described.     

Evidence for behavioural thermoregulation by the world's largest fish

Many fishes make frequent ascents to surface waters and often show prolonged surface swimming following descents to deep water. This affinity for the surface is thought to be related to the recovery of body heat lost at depth. We tested this hypothesis using data from time–depth recorders deployed on four whale sharks (Rhincodon typus). We summarized vertical movements into bouts of dives and classified these into three main types, using cluster analysis. In addition to day and night ‘bounce’ dives where sharks rapidly descended and ascended, we found a third type: single deep (mean: 340 m), long (mean: 169 min) dives, occurring in daytime with extremely long post-dive surface durations (mean: 146 min). Only sharks that were not constrained by shallow bathymetry performed these dives. We found a negative relationship between the mean surface duration of dives in the bout and the mean minimum temperature of dives in the bout that is consistent with the hypothesis that thermoregulation was a major factor driving use of the surface. The relationship broke down when sharks were diving in mean minimum temperatures around 25°C, suggesting that warmer waters did not incur a large metabolic cost for diving and that other factors may also influence surface use.     

A meta-analysis of the effects of texting on driving

Text messaging while driving is considered dangerous and known to produce injuries and fatalities. However, the effects of text messaging on driving performance have not been synthesized or summarily estimated. All available experimental studies that measured the effects of text messaging on driving were identified through database searches using variants of “driving” and “texting” without restriction on year of publication through March 2014. Of the 1476 abstracts reviewed, 82 met general inclusion criteria. Of these, 28 studies were found to sufficiently compare reading or typing text messages while driving with a control or baseline condition. Independent variables (text-messaging tasks) were coded as typing, reading, or a combination of both. Dependent variables included eye movements, stimulus detection, reaction time, collisions, lane positioning, speed and headway. Statistics were extracted from studies to compute effect sizes (r_c). A total sample of 977 participants from 28 experimental studies yielded 234 effect size estimates of the relationships among independent and dependent variables. Typing and reading text messages while driving adversely affected eye movements, stimulus detection, reaction time, collisions, lane positioning, speed and headway. Typing text messages alone produced similar decrements as typing and reading, whereas reading alone had smaller decrements over fewer dependent variables. Typing and reading text messages affects drivers’ capability to adequately direct attention to the roadway, respond to important traffic events, control a vehicle within a lane and maintain speed and headway. This meta-analysis provides convergent evidence that texting compromises the safety of the driver, passengers and other road users. Combined efforts, including legislation, enforcement, blocking technologies, parent modeling, social media, social norms and education, will be required to prevent continued deaths and injuries from texting and driving.     

Metabolite-sensitive holographic biosensors

A new type of biosensor that combines the inexpensiveness and mass-produceability of reflection holograms with the selectivity and specificity of enzymes is described. pH-sensitive holographic sensors were fabricated from ionizable monomers incorporated into thin, polymeric, hydrogel films which were transformed into volume holograms using a diffusion method coupled with holographic recording, using a frequency-doubled Nd:YAG laser (532 nm). These holograms were used as transducer systems to monitor the pH changes associated with specific enzymatic reactions to construct prototype urea- and penicillin-sensitive biosensors. The diffraction wavelength (color) of the holographic biosensors was used to characterize their shrinkage and swelling behavior as a function of analyte concentration. The potential of these sensors for the measurement of the clinically and industrially important metabolites urea and penicillin G is demonstrated.     

Hadamard tensors and lower bounds on multiparty communication complexity

We develop a new method for estimating the discrepancy of tensors associated with multiparty communication problems in the “Number on the Forehead” model of Chandra, Furst, and Lipton. We define an analog of the Hadamard property of matrices for tensors in multiple dimensions and show that any k-party communication problem represented by a Hadamard tensor must have Ω(n/2 ^k ) multiparty communication complexity. We also exhibit constructions of Hadamard tensors. This allows us to obtain Ω(n/2 ^k ) lower bounds on multiparty communication complexity for a new class of explicitly defined Boolean functions.     

(Approximate) Uncertain Skylines

Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discrete set of locations, we improve the best known exact solution. We also suggest why we believe our solution might be optimal. Next, we describe simple, near-linear time approximation algorithms for computing the probability of each point lying on the skyline. In addition, some of our methods can be adapted to construct data structures that can efficiently determine the probability of a query point lying on the skyline.     

Multi-Fault Tolerance for Cartesian Data Distributions

Faults are expected to play an increasingly important role in how algorithms and applications are designed to run on future extreme-scale systems. Algorithm-based fault tolerance is a promising approach that involves modifications to the algorithm to recover from faults with lower overheads than replicated storage and a significant reduction in lost work compared to checkpoint-restart techniques. Fault-tolerant linear algebra algorithms employ additional processors that store parities along the dimensions of a matrix to tolerate multiple, simultaneous faults. Existing approaches assume regular data distributions (blocked or block-cyclic) with the failures of each data block being independent. To match the characteristics of failures on parallel computers, we extend these approaches to mapping parity blocks in several important ways. First, we handle parity computation for generalized Cartesian data distributions with each processor holding arbitrary subsets of blocks in a Cartesian-distributed array. Second, techniques to handle correlated failures, i.e., multiple processors that can be expected to fail together, are presented. Third, we handle the colocation of parity blocks with the data blocks and do not require them to be on additional processors. Several alternative approaches, based on graph matching, are presented that attempt to balance the memory overhead on processors while guaranteeing the same fault tolerance properties as existing approaches that assume independent failures on regular blocked data distributions. Evaluation of these algorithms demonstrates that the additional desirable properties are provided by the proposed approach with minimal overhead.     

Mariposa: a wide-area distributed database system

The requirements of wide-area distributed database systems differ dramatically from those of local-area network systems. In a wide-area network (WAN) configuration, individual sites usually report to different system administrators, have different access and charging algorithms, install site-specific data type extensions, and have different constraints on servicing remote requests. Typical of the last point are production transaction environments, which are fully engaged during normal business hours, and cannot take on additional load. Finally, there may be many sites participating in a WAN distributed DBMS. In this world, a single program performing global query optimization using a cost-based optimizer will not work well. Cost-based optimization does not respond well to site-specific type extension, access constraints, charging algorithms, and time-of-day constraints. Furthermore, traditional cost-based distributed optimizers do not scale well to a large number of possible processing sites. Since traditional distributed DBMSs have all used cost-based optimizers, they are not appropriate in a WAN environment, and a new architecture is required. We have proposed and implemented an economic paradigm as the solution to these issues in a new distributed DBMS called Mariposa. In this paper, we present the architecture and implementation of Mariposa and discuss early feedback on its operating characteristics. Edited by Henry F. Korth and Amith Sheth. Received November 1994 / Revised June 1995 / Accepted September 14, 1995     

Speed Scaling of Processes with Arbitrary Speedup Curves on a Multiprocessor

We consider the setting of a multiprocessor where the speeds of the m processors can be individually scaled. Jobs arrive over time and have varying degrees of parallelizability. A nonclairvoyant scheduler must assign the processes to processors, and scale the speeds of the processors. We consider the objective of energy plus flow time. We assume that a processor running at speed s uses power s ^α for some constant α>1. For processes that may have side effects or that are not checkpointable, we show an W(m^(a-1)/a2)\Omega(m^{(\alpha -1)/\alpha^{2}}) bound on the competitive ratio of any randomized algorithm. For checkpointable processes without side effects, we give an O(log m)-competitive algorithm. Thus for processes that may have side effects or that are not checkpointable, the achievable competitive ratio grows quickly with the number of processors, but for checkpointable processes without side effects, the achievable competitive ratio grows slowly with the number of processors. We then show a lower bound of Ω(log ^1/α m) on the competitive ratio of any randomized algorithm for checkpointable processes without side effects.