The effects of day and night temperature on Chrysanthemum morifolium: investigating the safe limits for temperature integration

The impact of day and night temperatures on pot chrysanthemum (cultivars ‘Covington’ and ‘Irvine’) was assessed by exposing cuttings, stuck in weeks 39, 44, and 49, to different temperature regimes in short-days. Glasshouse heating setpoints of 12°, 15°, 18°, and 21°C, were used during the day, with venting at 2°C above these set-points. Night temperatures were then automatically manipulated to ensure that all of the treatments achieved similar mean diurnal temperatures. Plants were grown according to commercial practice and the experiment was repeated over 2 years. Increasing the day temperature from approx. 19°C to 21°C, and compensating by reducing the night temperature, did not have a significant impact on flowering time, although plant height was increased.This suggests that a temperature integration strategy which involves higher vent temperatures, and exploiting solar gain to give higher than normal day temperatures, should have minimal impact on crop scheduling. However, lowering the day-time temperature to approx. 16°C, and compensating with a warmer night, delayed flowering by up to 2 weeks. Therefore, a strategy whereby, in Winter, more heat is added at night under a thermally-efficient blackout screen may result in flowering delays.Transfers between the temperature regimes showed that the flowering delays were proportional to the amount of time spent in a low day-time temperature regime. Plants flowered at the same time, irrespective of whether they were transferred on a 1-, 2-, or 4-week cycle

Marketing Organic Milk in the United States: Findings from the Agricultural Resource Management Survey of 2005

Marketing,

IMRT QA using machine learning: A multi-institutional validation.

PurposeTo validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process

Influences of thermal environment on fish growth

Indexación: Scopus.Thermoregulation in ectothermic animals is influenced by the ability to effectively respond to thermal variations. While it is known that ectotherms are affected by thermal changes, it remains unknown whether physiological and/or metabolic traits are impacted by modifications to the thermal environment. Our research provides key evidence that fish ectotherms are highly influenced by thermal variability during development, which leads to important modifications at several metabolic levels (e.g., growth trajectories, microstructural alterations, muscle injuries, and molecular mechanisms). In Atlantic salmon (Salmo salar), a wide thermal range (ΔT 6.4°C) during development (posthatch larvae to juveniles) was associated with increases in key thermal performance measures for survival and growth trajectory. Other metabolic traits were also significantly influenced, such as size, muscle cellularity, and molecular growth regulators possibly affected by adaptive processes. In contrast, a restricted thermal range (ΔT 1.4°C) was detrimental to growth, survival, and cellular microstructure as muscle growth could not keep pace with increased metabolic demands. These findings provide a possible basic explanation for the effects of thermal environment during growth. In conclusion, our results highlight the key role of thermal range amplitude on survival and on interactions with major metabolism-regulating processes that have positive adaptive effects for organisms.http://onlinelibrary.wiley.com/doi/10.1002/ece3.3239/ful

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis

A Preliminary Discussion of the Kinematics of BHB and RR Lyrae Stars near the North Galactic Pole

The radial velocity dispersion of 67 RR Lyrae variable and blue horizontal branch (BHB) stars that are more than 4 kpc above the galactic plane at the North Galactic Pole is 110 km/sec and shows no trend with Z (the height above the galactic plane). Nine stars with Z < 4 kpc show a smaller velocity dispersion (40 +/-9 km/sec) as is to be expected if they mostly belong to a population with a flatter distribution. Both RR Lyrae stars and BHB stars show evidence of stream motion; the most significant is in fields RR2 and RR3 where 24 stars in the range 4.0 < Z < 11.0 kpc have a mean radial velocity of -59 +/- 16 km/sec. Three halo stars in field RR 2 appear to be part of a moving group with a common radial velocity of -90 km/sec. The streaming phenomenon therefore occurs over a range of spatial scales. The BHB and RR Lyrae stars in our sample both have a similar range of metallicity (-1.2 < [Fe/H] < -2.2). Proper motions of BHB stars in fields SA 57 (NGP) and the Anticenter field (RR 7) (both of which lie close to the meridional plane of the Galaxy) show that the stars that have Z 4 kpc have a Galactic V motion that is < -200 km/sec and which is characteristic of the halo. Thus the stars that have a flatter distribution are really halo stars and not members of the metal-weak thick-disk.Comment: Accepted for publication in the March 1996 AJ. 15 pages, AASTeX V4.0 latex format (including figures), 2 eps figures, 2 separate AASTeX V4.0 latex table

Star Formation History and Extinction in the central kpc of M82-like Starbursts

We report on the star formation histories and extinction in the central kpc region of a sample of starburst galaxies that have similar far infrared (FIR), 10 micron and K-band luminosities as those of the archetype starburst M82. Our study is based on new optical spectra and previously published K-band photometric data, both sampling the same area around the nucleus. Model starburst spectra were synthesized as a combination of stellar populations of distinct ages formed over the Hubble time, and were fitted to the observed optical spectra and K-band flux. The model is able to reproduce simultaneously the equivalent widths of emission and absorption lines, the continuum fluxes between 3500-7000 Ang, the K-band and the FIR flux. We require a minimum of 3 populations -- (1) a young population of age < 8 Myr, with its corresponding nebular emission, (2) an intermediate-age population (age < 500 Myr), and (3) an old population that forms part of the underlying disk or/and bulge population. The contribution of the old population to the K-band luminosity depends on the birthrate parameter and remains above 60% in the majority of the sample galaxies. Even in the blue band, the intermediate age and old populations contribute more than 40% of the total flux in all the cases. A relatively high contribution from the old stars to the K-band nuclear flux is also apparent from the strength of the 4000 Ang break and the CaII K line. The extinction of the old population is found to be around half of that of the young population. The contribution to the continuum from the relatively old stars has the effect of diluting the emission equivalent widths below the values expected for young bursts. The mean dilution factors are found to be 5 and 3 for the Halpha and Hbeta lines respectively.Comment: 20 pages, uses emulateapj.cls. Scheduled to appear in ApJ Jan 1, 200

A Predictive Algorithm For Wetlands In Deep Time Paleoclimate Models

Methane is a powerful greenhouse gas produced in wetland environments via microbial action in anaerobic conditions. If the location and extent of wetlands are unknown, such as for the Earth many millions of years in the past, a model of wetland fraction is required in order to calculate methane emissions and thus help reduce uncertainty in the understanding of past warm greenhouse climates. Here we present an algorithm for predicting inundated wetland fraction for use in calculating wetland methane emission fluxes in deep time paleoclimate simulations. The algorithm determines, for each grid cell in a given paleoclimate simulation, the wetland fraction predicted by a nearest neighbours search of modern day data in a space described by a set of environmental, climate and vegetation variables. To explore this approach, we first test it for a modern day climate with variables obtained from observations and then for an Eocene climate with variables derived from a fully coupled global climate model (HadCM3BL-M2.2). Two independent dynamic vegetation models were used to provide two sets of equivalent vegetation variables which yielded two different wetland predictions. As a first test the method, using both vegetation models, satisfactorily reproduces modern data wetland fraction at a course grid resolution, similar to those used in paleoclimate simulations. We then applied the method to an early Eocene climate, testing its outputs against the locations of Eocene coal deposits. We predict global mean monthly wetland fraction area for the early Eocene of 8 to 10 × 106km2 with corresponding total annual methane flux of 656 to 909 Tg, depending on which of two different dynamic global vegetation models are used to model wetland fraction and methane emission rates. Both values are significantly higher than estimates for the modern-day of 4 × 106km2 and around 190Tg (Poulter et. al. 2017, Melton et. al., 2013