Operator Sequence Alters Gene Expression Independently of Transcription Factor Occupancy in Bacteria

A canonical quantitative view of transcriptional regulation holds that the only role of operator sequence is to set the probability of transcription factor binding, with operator occupancy determining the level of gene expression. In this work, we test this idea by characterizing repression in vivo and the binding of RNA polymerase in vitro in experiments where operators of various sequences were placed either upstream or downstream from the promoter in Escherichia coli. Surprisingly, we find that operators with a weaker binding affinity can yield higher repression levels than stronger operators. Repressor bound to upstream operators modulates promoter escape, and the magnitude of this modulation is not correlated with the repressor-operator binding affinity. This suggests that operator sequences may modulate transcription by altering the nature of the interaction of the bound transcription factor with the transcriptional machinery, implying a new layer of sequence dependence that must be confronted in the quantitative understanding of gene expression

The assessment of quality of life (AQoL) instrument construction, initial validation & utility scaling

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The Australian Quality Of Life (AQoL) Instrument: Initial Validation

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IXPE Mission System Concept and Development Status

The Goal of the Imaging X-Ray Polarimetry Explorer (IXPE) Mi SMEX), is to expand understanding of high-energy astrophysical processes and sources, in support of NASAs first science objective in Astrophysics: Discover how the universe works. IXPE, an international collaboration, will conduct X-ray imaging polarimetry for multiple categories of cosmic X-ray sources such as neutron stars, stellar-mass black holes, supernova remnants and active galactic nuclei. The Observatory uses a single science operational mode capturing the X-ray data from the targets. The IXPE Observatory consists of spacecraft and payload modules built up in parallel to form the Observatory during system integration and test. The payload includes three X-ray telescopes each consisting of a polarization-sensitive, gas pixel X-ray detector, paired with its corresponding grazing incidence mirror module assembly (MMA). A deployable boom provides the correct separation (focal length) between the detector units (DU) and MMAs. These payload elements are supported by the IXPE spacecraft which is derived from the BCP-small spacecraft architecture. This paper summarizes the IXPE mission science objectives, updates the Observatory implementation concept including the payload and spacecraft ts and summarizes the mission status since last years conference

Operational Evaluatioin of Dynamic Weather Routes at American Airlines

Dynamic Weather Routes (DWR) is a search engine that continuously and automatically analyzes inflight aircraft in en route airspace and proposes simple route amendments for more efficient routes around convective weather while considering sector congestion, traffic conflicts, and active Special Use Airspace. NASA and American Airlines (AA) are conducting an operational trial of DWR at the AA System Operations Center in Fort Worth, TX. The trial includes only AA flights in Fort Worth Center airspace. Over the period from July 31, 2012 through August 31, 2012, 45% of routes proposed by DWR and evaluated by AA users - air traffic control coordinators and flight dispatchers - were rated as acceptable as proposed or with some modifications. The wind-corrected potential flying time savings for these acceptable routes totals 470 flying min, and results suggest another 1,500 min of potential savings for flights not evaluated due to staffing limitations. A sector congestion analysis shows that in only two out of 83 DWR routes rated acceptable by AA staff were the flights predicted to fly through a congested sector inside of 30 min downstream of present position. This shows that users considered sector congestion data provided by DWR automation and in nearly all cases did not accept routes through over-capacity sectors. It is estimated that 12 AA flights were given reroute clearances as a direct result of DWR for a total savings of 67 flying min

Influence of Corn Stover Harvest on Soil Quality Assessments at Multiple Locations Across the U.S.

Corn (Zea mays L.) stover has been identified as a biofuel feedstock due to its abundance and a perception that the residues are unused trash material. However, corn stover and other plant residues play a role in maintaining soil quality (health) and enhancing productivity, thus use of this abundant material as feedstock must be balanced with the need to protect the vital soil resource. Plant residues provide physical protection against erosion by wind and water, contribute to soil structure, nutrient cycling, and help sustain the soil microbiota. Replicated plots were established on productive soils at several locations (IA, IN, MN, NE, PA, SD, and SC) and a multi-year study was carried out to determine the amount of corn stover that can be removed while maintaining the current level of soil quality for each soil. These sites represented a range of soil types and climatic conditions, and have been ongoing for and least five years with some much longer studies. All sites had at least three levels of stover harvest: grain only (control), maximum removal (90-100%) and a mid-range removal rate (~50%). Data from 4 sites are presented (IA, IN, MN, and NE). The Soil Management Assessment Framework (SMAF) was used to score and assess changes in selected soil quality indicators. Data shows that removal at the highest rates resulted in some loss in soil quality with respect to soil organic carbon and bulk density. These sites were converted to no-till when the experiments were initiated, thus SOC accrual because of the shift in tillage management appeared to balance any losses due to feedstock harvest

Health literacy and the health status of men with prostate cancer

OBJECTIVE: To test the Health Literacy Questionnaire (HLQ) in a sample of men with prostate cancer and examine the components of health literacy that are most strongly associated with mental and physical health‐related quality of life in men with prostate cancer. METHOD: Members (N = 565) of a state‐wide prostate cancer support network in Queensland, Australia (Mage = 71.14, SD = 8.68) completed the HLQ along with the Medical Outcomes Study, 36‐item short‐form health survey (SF‐36). Confirmatory factor analysis was employed to assess the internal structure of the HLQ. The effects (bs) of each of the nine health literacy factors on mental and physical health status were graphed and compared using Fishers exact test for comparing parameter estimates. RESULTS: Fit indices including RMSEA (0.069, CI = 0.066‐0.072), CFI (.853), and TLI (.839), alongside item loadings and internal consistency (Cronbach alphas >0.80) for the nine‐factor model, supported the robustness of the HLQ for use in this prostate cancer sample. Health literacy factors reflecting social and health provider support, navigating health systems, finding and understanding health information, and active engagement with providers shared small to moderate associations with mental health status and little to no association with physical health status. CONCLUSION: Findings provide support for the use of the HLQ as a valid and reliable measure of health literacy in men with prostate cancer. Although further research is required to establish causality, interventions that aim to improve skills in connecting and effectively communicating with health care services and providers might lead to better mental health related quality of life for men with prostate cancer

Corn Grain, Stover Yield and Nutrient Removal Validations at Regional Partnership Sites

Corn (Zea mays L.) stover has been identified as a major feedstock for cellulosic bioenergy. This report summarizes grain and stover yield as well as N, P, and K removal at several Sun Grant Regional Partnership (SGRP) sites. National Agricultural Statistical Service (NASS) grain yields were used to assess the relevancy of plot-scale yields with county averages. Seasonal variation in weather patterns caused yields to differ substantially among sites and years. Nutrient removal estimates were significantly influenced by the sampling method (i.e. analysis of hand samples between physiologic maturity and grain harvest versus stover collected during the harvest operation). Based on ancillary studies that indicate corn stover should not be harvested if average grain yields are less than 175 bu ac-1 (11 Mg ha-1 ), these studies show that non-irrigated SGRP sites with the highest potential for sustainable corn stover harvest were located between -91º and -93º west longitude. The more eastern (-78º w longitude) and western (-96º w longitude) sites did not have sufficient yield for sustainable routine stover harvest, although with good management, corn could still be part of an overall landscape approach for sustainable feedstock production in those areas. For producers with consistently high yields (i.e. \u3e 200 bu ac-1 ) and where residue management may actually be a major problem (e.g. in irrigated areas), moderate stover harvest may actually decrease fuel use and save additional energy by reducing the amount of tillage needed to prepare subsequent seedbeds. Less intensive tillage could also preserve rhizosphere carbon and/or soil structure benefits often attributed to no-till systems

MLPerf Inference Benchmark

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark's flexibility and adaptability.Comment: ISCA 202