A Risk Analysis of Carbon Sequestration in Claypan Soil with Conservation Tillage Systems and Nitrogen Fertilizers for Grain Sorghum and Soybean

Replaced with revised version of paper 02/15/06.carbon sequestration, carbon credits, nitrogen, risk, tillage, Crop Production/Industries, Risk and Uncertainty, Q12,

Education for expanding computer curriculums : a symposium

https://egrove.olemiss.edu/aicpa_assoc/1587/thumbnail.jp

A–C Estrogens as Potent and Selective Estrogen Receptor-Beta Agonists (SERBAs) to Enhance Memory Consolidation under Low-Estrogen Conditions

Estrogen receptor-beta (ERβ) is a drug target for memory consolidation in postmenopausal women. Herein is reported a series of potent and selective ERβ agonists (SERBAs) with in vivo efficacy that are A–C estrogens, lacking the B and D estrogen rings. The most potent and selective A–C estrogen is selective for activating ER relative to seven other nuclear hormone receptors, with a surprising 750-fold selectivity for the β over α isoform and with EC50s of 20–30 nM in cell-based and direct binding assays. Comparison of potency in different assays suggests that the ER isoform selectivity is related to the compound’s ability to drive the productive conformational change needed to activate transcription. The compound also shows in vivo efficacy after microinfusion into the dorsal hippocampus and after intraperitoneal injection (0.5 mg/kg) or oral gavage (0.5 mg/kg). This simple yet novel A–C estrogen is selective, brain penetrant, and facilitates memory consolidation

Calibration of the APEX Model to Simulate Management Practice Effects on Runoff, Sediment, and Phosphorus Loss

Process-based computer models have been proposed as a tool to generate data for Phosphorus (P) Index assessment and development. Although models are commonly used to simulate P loss from agriculture using managements that are different from the calibration data, this use of models has not been fully tested. The objective of this study is to determine if the Agricultural Policy Environmental eXtender (APEX) model can accurately simulate runoff, sediment, total P, and dissolved P loss from 0.4 to 1.5 ha of agricultural fields with managements that are different from the calibration data. The APEX model was calibrated with field-scale data from eight different managements at two locations (management-specific models). The calibrated models were then validated, either with the same management used for calibration or with different managements. Location models were also developed by calibrating APEX with data from all managements. The management-specific models resulted in satisfactory performance when used to simulate runoff, total P, and dissolved P within their respective systems, with r2 \u3e 0.50, Nash– Sutcliffe efficiency \u3e 0.30, and percent bias within ±35% for runoff and ±70% for total and dissolved P. When applied outside the calibration management, the management-specific models only met the minimum performance criteria in one-third of the tests. The location models had better model performance when applied across all managements compared with management-specific models. Our results suggest that models only be applied within the managements used for calibration and that data be included from multiple management systems for calibration when using models to assess management effects on P loss or evaluate P Indices

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images

Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Statistical normalization techniques for magnetic resonance imaging☆☆☆

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Changes in reflectin protein phosphorylation are associated with dynamic iridescence in squid

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of The Royal Society for personal use, not for redistribution. The definitive version was published in Journal of The Royal Society Interface 6 (2010): 549-560, doi:10.1098/rsif.2009.0299.Many cephalopods exhibit remarkable dermal iridescence, a component of their complex, dynamic camouflage and communication. In the species Euprymna scolopes, the light-organ iridescence is static and is due to reflectin protein-based platelets assembled into lamellar thin-film reflectors called iridosomes, contained within iridescent cells called iridocytes. Squid in the family Loliginidae appear to be unique in that the dermis possesses a dynamic iridescent component, with reflective, colored structures that are assembled and disassembled under the control of the muscarinic cholinergic system and the associated neurotransmitter acetylcholine (Mathger et al. 2004). Here we present the sequences and characterization of three new members of the reflectin family associated with the dynamically changeable iridescence in Loligo and not found in static Euprymna iridophores. In addition, we show that application of genistein, a protein tyrosine kinase inhibitor, suppresses acetylcholine- and calcium-induced iridescence in Loligo. We further demonstrate that two of these novel reflectins are extensively phosphorylated in concert with the activation of iridescence by exogenous acetylcholine. This phosphorylation and the correlated iridescence can be blocked with genistein. Our results suggest that tyrosine phosphorylation of reflectin proteins is involved in the regulation of dynamic iridescence in Loligo.We gratefully acknowledge support from Anteon contract F33615-03-D-5408 to the Marine Biological Laboratory, Woods Hole, MA and grant # W911NF-06-1-0285 from the Army Research Office to D.E.M