Estimating Agricultural Production Functions from Experimental Data for Different Crops in Relation to Irrigation, Fertilization and Soil Management in Northern Utah

Estimates of agricultural production functions from experimental data for four different crops in relation to six variable inputs are calculated by this study. There are four basic sections in the study. The first section covers the review of production function concepts and the procedures and problems that specifically pertain to this study. Also the importance of joint economic-agronomic research efforts, methodologies and applications of agricultural production functions are cited. The second section includes the presentation data and postulated functional relationships in estimating production functions. Model building programs are used in developing three dimensional figures, which aid in the selection of the appropriate model. A multiple regression model using linear, non-linear and interaction terms is employed in deriving three production function for each crop. The problem of selecting a best model from the above three models is solved on the basis of economic theory, observed biologic physical production process, projected three dimensional production surfaces and statistical analyses. The polynomial form was selected as the best model for each crop. The third section of this study analyzes the results and the economic implications. Optimal rates of input use are determined. Qualification of these results are required because of the non significant statistical relationships including the F values of the regression coefficients and relatively low coefficient of determination (R2), and, also, because some optimal inputs values did not seem reasonable relative to observed rates. Further statistical analyses are carried out to determine the confidence interval for each input\u27s marginal productivity and this results in unbounded solutions. As an alternative, the above confidence interval problem is rephrased as a system of equalities and solved simultaneously to obtain optimal input levels at the marginal productivities maximum and minimum values and these estimates are shown not to be confidence intervals. Finally, in the fourth section of this study, summary and conclusions are given. Also, limitation and recommendations to the study are discussed

Designing a fuzzy scheduler for hard real-time systems

In hard real-time systems, tasks have to be performed not only correctly, but also in a timely fashion. If timing constraints are not met, there might be severe consequences. Task scheduling is the most important problem in designing a hard real-time system, because the scheduling algorithm ensures that tasks meet their deadlines. However, the inherent nature of uncertainty in dynamic hard real-time systems increases the problems inherent in scheduling. In an effort to alleviate these problems, we have developed a fuzzy scheduler to facilitate searching for a feasible schedule. A set of fuzzy rules are proposed to guide the search. The situation we are trying to address is the performance of the system when no feasible solution can be found, and therefore, certain tasks will not be executed. We wish to limit the number of important tasks that are not scheduled

Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the "truth" under the influence of their varying skill-levels and biases. Blindly treating these noisy labels as the ground truth limits the accuracy of learning algorithms in the presence of strong disagreement. This problem is critical for applications in domains such as medical imaging where both the annotation cost and inter-observer variability are high. In this work, we present a method for simultaneously learning the individual annotator model and the underlying true label distribution, using only noisy observations. Each annotator is modeled by a confusion matrix that is jointly estimated along with the classifier predictions. We propose to add a regularization term to the loss function that encourages convergence to the true annotator confusion matrix. We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators. Despite the simplicity of the idea, experiments on image classification tasks with both simulated and real labels show that our method either outperforms or performs on par with the state-of-the-art methods and is capable of estimating the skills of annotators even with a single label available per image.Comment: CVPR 2019, code snippets include

Alignment and Composition of Laminin-Polycaprolactone Nanofiber Blends Enhance Peripheral Nerve Regeneration

Peripheral nerve transection occurs commonly in traumatic injury, causing deficits distal to the injury site. Conduits for repair currently on the market are hollow tubes; however, they often fail due to slow regeneration over long gaps. To facilitate increased regeneration speed and functional recovery, the ideal conduit should provide biochemically relevant signals and physical guidance cues, thus playing an active role in regeneration. To that end, laminin and lamininpolycaprolactone (PCL) blend nanofibers were fabricated to mimic peripheral nerve basement membrane. In vitro assays established 10% (wt) laminin content is sufficient to retain neurite-promoting effects of laminin. In addition, modified collector plate design to introduce an insulating gap enabled the fabrication of aligned nanofibers. The effects of laminin content and fiber orientation were evaluated in rat tibial nerve defect model. The lumens of conduits were filled with nanofiber meshes of varying laminin content and alignment to assess changes in motor and sensory recovery. Retrograde nerve conduction speed at 6 weeks was significantly faster in animals receiving aligned nanofiber conduits than in those receiving random nanofiber conduits. Animals receiving nanofiber-filled conduits showed some conduction in both anterograde and retrograde directions, whereas in animals receiving hollow conduits, no impulse conduction was detected. Aligned PCL nanofibers significantly improved motor function; aligned laminin blend nanofibers yielded the best sensory function recovery. In both cases, nanofiber-filled conduits resulted in better functional recovery than hollow conduits. These studies provide a firm foundation for the use of naturalsynthetic blend electrospun nanofibers to enhance existing hollow nerve guidance conduits

Clinal variation in body size and sexual dimorphism in an Indian fruit bat, \u3ci\u3eCynopterus sphinx \u3c/i\u3e (Chiroptera: Pteropodidae)

Geographic variation in body size and sexual dimorphism of the short-nosed fruit bat (Cynopterus sphinx ) was investigated in peninsular India. Bats were sampled at 12 localities along a 1,200 km latitudinal transect that paralleled the eastern flanks of the Western Ghats. The geographic pattern of variation in external morphology of C. sphinx conforms to the predictions of Bergmann’s Rule, as indicated by a steep, monotonic cline of increasing body size from south to north. This study represents one of the first conclusively documented examples of Bergmann’s Rule in a tropical mammal and confirms that latitudinal clines in body size are not exclusively restricted to temperate zone homeotherms. Body size was indexed by a multivariate axis derived from principal components analysis of linear measurements that summarize body and wing dimensions. Additionally, length of forearm was used as a univariate index of structural size to examine geographic variation in a more inclusive sample of bats across the latitudinal transect. Multivariate and univariate size metrics were strongly and positively correlated with body mass, and exhibited highly concordant patterns of clinal variation. Stepwise multiple regression on climatological variables revealed that increasing size of male and female C. sphinx was associated with decreasing minimum temperature, increasing relative humidity, and increasing seasonality. Although patterns of geographic size variation were highly concordant between the sexes, C. sphinx also exhibited a latitudinal cline in the magnitude and direction of sexual size dimorphism. The size differential reversed direction across the latitudinal gradient, as males averaged larger in the north, and females averaged larger in the south. The degree of female-biased size dimorphism across the transect was negatively correlated with body size of both sexes. Canonical discriminant analysis revealed that male- and female-biased size dimorphism were based on contrasting sets of external characters. Available data on geographic variation in the degree of polygyny in C. sphinx suggests that sexual selection on male size may play a role in determining the geographic pattern of sexual size dimorphism

Sensor technologies for quality control in engineered tissue manufacturing

The use of engineered cells, tissues, and organs has the opportunity to change the way injuries and diseases are treated. Commercialization of these groundbreaking technologies has been limited in part by the complex and costly nature of their manufacture. Process-related variability and even small changes in the manufacturing process of a living product will impact its quality. Without real-time integrated detection, the magnitude and mechanism of that impact are largely unknown. Real-time and non-destructive sensor technologies are key for in-process insight and ensuring a consistent product throughout commercial scale-up and/or scale-out. The application of a measurement technology into a manufacturing process requires cell and tissue developers to understand the best way to apply a sensor to their process, and for sensor manufacturers to understand the design requirements and end-user needs. Furthermore, sensors to monitor component cells’ health and phenotype need to be compatible with novel integrated and automated manufacturing equipment. This review summarizes commercially relevant sensor technologies that can detect meaningful quality attributes during the manufacturing of regenerative medicine products, the gaps within each technology, and sensor considerations for manufacturing

High aspect ratio nanoimprinted grooves of poly(lactic-co-glycolic acid) control the length and direction of retraction fibers during fibroblast cell division

Retraction fibers (RFs) determine orientation of the cell division axis and guide the spreading of daughter cells. Long and unidirectional RFs, which are especially apparent during mitosis of cells in three-dimensional (3D) environments, enable improved control over cell fate, following division. However, 3D gel environments lack the cues necessary for predetermining the orientation of RFs to direct tissue architecture. While patterning of focal adhesion regions by microcontact printing can determine orientation of the RFs through enhancing focal adhesion numbers along particular directions, the RFs remain short due to the two-dimensional culture environment. Herein, the authors demonstrate that nanoimprinted grooves of polylactic acid glycolic acid (PLGA) with a high aspect ratio (A.R. of 2.0) can provide the cues necessary to control the direction of RFs, as well as enable the maintenance of long and unidirectional RFs as observed within 3D cultures, while the same is not possible with PLGA grooves of lower A.R. (1.0 or lower). Based on enhanced levels of contact guidance of premitotic fibroblast protrusions at high A.R. grooves and deeper levels of focal adhesion due to filopodia extensions into these grooves, it is suggested that submicron (800 nm width) PLGA grooves with A.R. of 2 are capable of supporting mechanical forces from cell protrusions to a greater depth, thereby enabling the maintenance of the protrusions as long and unidirectional RFs during cell division. Given the scalability and versatility of nanoimprint techniques, the authors envision a platform for designing nanostructures to direct tissue regeneration and developmental biology.This is the publisher’s final pdf. The article is copyrighted by the American Vacuum Society and published by Springer. It can be found at: http://link.springer.com/journal/13758See supplementary material at http://dx.doi.org/10.1116/1.4936589 for movies on cell trajectories during division on the grooves, as well as for alignment and elongation of pre-mitotic protrusions on PLGA grooves

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer