Stability of Climax Prairie and Some Environmental Changes Resulting from Breaking

Continued study of the great midcontinental grasslands of North America throughout a period of years has impressed the writers with the high degree of stability of the climax prairie. This phenomenon has been carefully considered in a study of the types of prairie in the Missouri Valley over an area of several thousand square miles centering in eastern Nebraska but including five neighboring states. Present studies on the deterioration of the prairie under the impact of grazing, and changes in edaphic and atmospheric environment with the breaking and cropping of the land have further emphasized the stabilizing influence of a cover of grassland

Seeds From Fresh Conventional Tomatoes Germinate Faster than Dried or Organic Seeds

Conventionally-grown and organically-grown tomato seeds sourced from both fresh tomatoes and store-bought packages were germinated for seven days to evaluate the effect of seed source on germination rates. Seeds from fresh Roma tomatoes were prepared by allowing them to ferment in their own pulp for 24 hours, while commercially packaged dry seeds did not require any preparation. Once prepared, the seeds were spaced evenly on wet paper towels and stored in resealable plastic bags in groups of 10 for a total sample size of 100 seeds in each of four treatments. The number of germinated seeds and the length of their roots were measured daily for seven days. 82.5% of the seeds from fresh tomatoes germinated—91% of the conventional and 74% of the organic—while only 49% of the commercially dried seeds germinated—35% of the conventional and 63% of the organic. Conventionally-grown seeds germinated on average one day faster than organically-grown seeds. In addition, the seeds from fresh tomatoes experienced significantly faster germination rates by 0.78 days and longer average growth. Our data indicate potentially higher germination success for seeds sourced from fresh tomatoes, but only when conventionally-grown

The Effect of Token Economies on Student Behavior in the Preschool Classroom: A Meta-Analysis

There has been a recent push in the literature to identify and use more evidence-based practices for positive behavioral supports for challenging student behaviors in the classroom environment. Further, interest in targeting early education environments such as preschool has been growing given the persistence of behavioral difficulties in the absence of early and effective intervention (Campbell & Ewing, 1990; Kazdin, 1987; Powell et al., 2006; Stormont, 2002). Two previous meta-analyses (Maggin et al., 2011; Soares et al., 2016) provided some initial support for effectiveness of token economies with challenging student behavior; however, the inclusion of the preschool setting was limited and both studies used older versions of design standards to evaluate the quality of studies in the literature. The present study served to extend those meta-analyses by targeting preschool classrooms. Further, the current study included the most recent What Works Clearinghouse Design Standards to evaluate whether token economies meet criteria as an evidence-based practice. Ten studies were included in the final analyses. Two sets of effect sizes were calculated: Baseline-Corrected Tau and Hedge’s g. An omnibus effect size showed an overall large effect; however, similar to previous meta-analyses, several methodological concerns were identified. Moderator analyses for several variables were conducted; however, no moderator analyses were significant. Limitations and future directions were discussed

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load

Patient satisfaction while enrolled in clinical trials: A literature review

Patient satisfaction surveys may not adequately reflect organizations that conduct research in patients who enroll in clinical trials. The purpose of this systematic literature review was to summarize the current state of knowledge of patient satisfaction while enrolled in clinical trials utilizing a widely used, validated patient satisfaction instrument. A comprehensive literature search was conducted using CINAHL, EMBASE, PsycInfo, PubMed and Web of Science. Studies were evaluated in terms of clinical trial participation; assessment conducted during or after participation; utilization of a validated instrument; a pharmacological intervention; and the paper was published in English. Only nine studies met this review’s inclusion criteria. Eight studies utilized investigator-developed patient satisfaction instruments and only one study used a widely-used, validated patient satisfaction instrument. Two studies evaluated patient satisfaction during the development of the instrument. Of the nine studies identified, only five patient satisfaction domains were common across the studies and only study evaluated the associations of patient satisfaction responses with clinical outcomes. Given the importance of patient satisfaction surveys, future studies need to focus on this subset of patients enrolled in clinical trials to evaluate a patient’s experience and its impact on protocol compliance and protocol outcomes. Future studies need to focus on domains associated with clinical trial participation and look beyond the current patients’ general expectations about healthcare accessibility, facilities, healthcare team clinical skills, and their ability to focus and listen to the patients’ concerns. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework. (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens

Open Sequence Initiative: a part submission standard to complement modern DNA assembly techniques

The discipline of synthetic biology emphasizes the application of engineering principles such as standardization, abstraction, modularity, and rational design to complex biological systems. The archetypical example of such standardization is BioBrick RFC[10], introduced in 2003 by Tom Knight at MIT. BioBricks are stored on a standard plasmid, pSB1C3, which contains prefix and suffix sequences flanking the DNA sequence specifying a biological part. The prefix and suffix sequences contain two pairs of 6 base-pair (bp) restriction enzyme sites (EcoRI+XbaI and SpeI+PstI), which can be used for both part assembly and quality control. BioBricks are intended to be well- characterized biological parts, such as genes or promoters, that function in a predictable fashion and can be readily combined to make complex systems. The rules of the RFC[10] BioBrick assembly method require that none of the restriction sites used in the prefix and suffix be present in the parts themselves. This requirement can be an onerous imposition for iGEM teams developing large, novel parts, such as genes or entire operons that are obtained by amplifying DNA sequences from environmental samples or microorganisms. While iGEM teams may use methods such as site-directed mutagenesis to remove illegal restriction sites from a part's sequence, it is certainly possible that this mutation will alter the functionality of the part – a very undesirable outcome. In addition, the mutagenesis of illegal restriction sites is an unnecessary burden on teams, given the limited time and resources available to teams during each year’s iGEM competition. Efforts spent mutagenizing sites would be better spent characterizing and improving parts. This RFC proposes an alternative submission standard to eliminate these problems

Simulating Supersonic Turbulence in Galaxy Outflows

We present three-dimensional, adaptive mesh simulations of dwarf galaxy out- flows driven by supersonic turbulence. Here we develop a subgrid model to track not only the thermal and bulk velocities of the gas, but also its turbulent velocities and length scales. This allows us to deposit energy from supernovae directly into supersonic turbulence, which acts on scales much larger than a particle mean free path, but much smaller than resolved large-scale flows. Unlike previous approaches, we are able to simulate a starbursting galaxy modeled after NGC 1569, with realistic radiative cooling throughout the simulation. Pockets of hot, diffuse gas around individual OB associations sweep up thick shells of material that persist for long times due to the cooling instability. The overlapping of high-pressure, rarefied regions leads to a collective central outflow that escapes the galaxy by eating away at the exterior gas through turbulent mixing, rather than gathering it into a thin, unstable shell. Supersonic, turbulent gas naturally avoids dense regions where turbulence decays quickly and cooling times are short, and this further enhances density contrasts throughout the galaxy- leading to a complex, chaotic distribution of bubbles, loops and filaments as observed in NGC 1569 and other outflowing starbursts.Comment: 22 pages, 13 figures, MNRAS, in pres