Berkeley Systems (United States)

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterised sequences with the few experimentally characterised ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates protein nascent families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage can scale approximately linearly with input size, while the biological benchmarks showed that the generated protein families closely resemble manually curated families found in widely used databases.

To simulate body organ motion due to breathing, heart beats, or peristaltic movements, we designed a low-cost, miniaturized SPRK (Stewart Platform Research Kit) to translate and rotate phantom tissue. This platform is 20cm x 20cm x 10cm to fit in the workspace of a da Vinci Research Kit (DVRK) surgical robot and costs $250, two orders of magnitude less than a commercial Stewart platform. The platform has a range of motion of +/- 1.27 cm in translation along x, y, and z directions and has motion modes for sinusoidal motion and breathing-inspired motion. Modular platform mounts were also designed for pattern cutting and debridement experiments. The platform's positional controller has a time-constant of 0.2 seconds and the root-mean-square error is 1.22 mm, 1.07 mm, and 0.20 mm in x, y, and z directions respectively. All the details, CAD models, and control software for the platform is available at github.com/BerkeleyAutomation/sprk.

Real-time sensing and computing technologies are increasingly used in the delivery of real-time health behavior interventions. Auditory signals play a critical role in many of these interventions, impacting not only behavioral response but also treatment adherence and participant retention. Yet, few behavioral interventions that employ auditory feedback report the characteristics of sounds used and even fewer design signals specifically for their intervention. This paper describes a four-step process used in developing and selecting auditory warnings for a behavioral trial designed to reduce indoor secondhand smoke exposure. In step one, relevant information was gathered from ergonomic and behavioral science literature to assist a panel of research assistants in developing criteria for intervention-specific auditory feedback. In step two, multiple sounds were identified through internet searches and modified in accordance with the developed criteria, and two sounds were selected that best met those criteria. In step three, a survey was conducted among 64 persons from the primary sampling frame of the larger behavioral trial to compare the relative aversiveness of sounds, determine respondents' reported behavioral reactions to those signals, and assess participant's preference between sounds. In the final step, survey results were used to select the appropriate sound for auditory warnings. Ultimately, a single-tone pulse, 500 milliseconds (ms) in length that repeats every 270 ms for 3 cycles was chosen for the behavioral trial. The methods described herein represent one example of steps that can be followed to develop and select auditory feedback tailored for a given behavioral intervention.

It is well established that transit passengers dislike transferring, in part because of the inherent risk that the connecting vehicle will be missed, a risk that can increase overall travel time. Despite the problems that missed transfers cause, such transfers across a system are rarely tracked in transit performance monitoring programs. The likelihood of a missed transfer depends on combinations of several factors and thus is hard to estimate. In practice, transit systems are most often evaluated according to the performance of individual vehicles, stops, and routes, not the interactions between them. This paper describes a systems approach to quantify the effects of travel time reliability, schedule adherence, and schedule design on missed transit connections, and the resulting travel time distributions. To determine the effects of vehicle interactions on transfers and the role that transfers play in travel time, a series of simulations based on automatic passenger counting data from the bus system in San Diego, California, was performed. Travel times on two transfer trips in downtown San Diego were simulated. The effects of passenger arrival rate, on-time vehicle performance, and schedule design on the likelihood of a transfer being missed were investigated in a sensitivity analysis. This research is expected to lead to a better understanding of the passenger effects of schedule adherence on transfer trips. Practically speaking, this methodology could aid in the identification of pairs of buses whose chronic schedule deviations at a particular location are likely causing missed transfers.

Multi-vehicle systems are naturally encountered in civil and military applications. Cooperation amongst individual “miniaturized” vehicles allows for flexibility to accomplish missions that a single large vehicle may not readily be able to accomplish. While accomplishing a mission, motion planning algorithms are required to efficiently utilize a common resource (such as the total fuel in the collection of vehicles) or to minimize a collective cost function (such as the maximum time taken by the vehicles to reach their intended destination). The objective of this chapter is to present a constant factor approximation algorithm for planning the path of each vehicle in a collection of vehicles, where the motion of each vehicle must satisfy yaw rate constraints.

This paper presents a mixed-signal frequency-domain implementation of the autocorrelation receiver used in the detection of ultra-wideband communication signals that are modulated with transmitted-reference signaling. The digital receiver architecture is based on samples provided by an analog-to-digital converter (ADC) in the frequency domain. Among the advantages of the new receiver are the relaxation of the conversion speed achieved by the inherent parallel architecture of the frequency-domain ADC and the flexibility and simplicity of an all-digital receiver architecture that follows the mixed-signal ADC radio front-end. The paper provides the symbol detection formulas and the corresponding system implementation and makes comparisons with the conventional time-domain implementations. Additionally, a time synchronization technique for the new receiver structure is proposed, which makes the receiver insensitive to any time offset at the pulse level. Furthermore, as a particular case of great practical interest, we consider a single bit for the ADC implementation, and we show with experimental results that the resultant frequency-domain mono-bit digital receiver provides a performance gain that increases with the number of frequency samples, when compared with its time-domain counterpart.

The effects of contaminants on the electrodes of plasma opening switches have been studied. The contaminants introduce backscattered and sputtered neutral atoms into the carbon plasma that is impinging on the cathode. The process has been investigated with the TRIM.SP code. The distribution of density of these neutral atoms from the cathode surface has been obtained and found to be substantial compared to the density of the incident carbon plasma. The dependence of the sputtering yield on the composition of the contaminants has also been studied. >