USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.While rice is produced in some parts of the world in an upland, rainfed culture, almost all US-produced rice is grown with flood irrigation. In the dry-seeding system commonly used in the midsouthern US, the crop is usually flooded at approximately the V-4 (early tillering) growth stage and a continuous flood is maintained until after heading. The total amount of water used in rice production is quite large, and soil, fertilizers, and pesticides can be carried in the runoff from agricultural fields. Flood depth affects most aspects of flooded rice production, and remote monitoring of the flood depth could be quite valuable to many producers. The objective of this research is to develop and test a system for monitoring water depths in rice fields and alerting the producer so that less labor and energy is required to efficiently manage flood-irrigated rice. A prototype monitoring station was designed to measure water depth in a flooded rice field and transmit the information over a wireless link. A similar sensor and circuit performed satisfactorily in a raingage in 2006. In 2007, prototype monitoring stations will be installed in production rice fields. Concurrently with sensor durability testing, tests will be conducted to determine the limits of the wireless communication system. With daily reports of the water status in each paddy, field visits can be reduced. Over-pumping should be minimized by allowing better scheduling of field visits to stop the pump, and future systems should work with automatic pump control systems to stop the pump before runoff occurs

Cropping system and landscape characteristics influence long-term grain crop profitability

Converting from standard tillage or no-tillage cropping systems to more conservation-based cropping systems that include no-tillage, cover crops, and reduced agrichemical inputs must be profitable for large-scale adoption. Therefore, research was conducted at the central Mississippi River Basin site of the USDA Long-Term Agroecosystem Research Network from 1996 to 2009 to determine how cropping systems, landscape position, and depth to claypan affected net economic return. Treatments consisted of three cropping systems {mulch-till corn (Zea mays L.)–soybean [Glycine max (L.) Merr.], MTCS; no-till corn–soybean, NTCS; no-till corn–soybean–wheat (Triticum aestivum L.) (NTCSW)–cover crop} and three landscape positions (summit, backslope, and footslope). Within each cropping system, landscape position influenced the depth to claypan and net returns, which were greatest in the summit and footslope positions. Across landscape positions, net return for NTCS was US$252 and$119 ha−1 yr−1 greater than MTCS and NTCSW, respectively. Net return of corn in MTCS and NTCSW was negative, whereas corn in NTCS averaged $97 ha−1 yr−1. Only NTCS corn exhibited a positive linear response in net return to depth to claypan. Soybean was much more profitable than corn, and both NTCS and NTCSW soybean were less influenced by landscape position and had at least$252 ha−1 yr−1 greater return than did MTCS soybean across landscape position. Results suggest that converting from MTCS to NTCS would have large positive impacts on reducing within-field variability and increasing profitability in the region, and modifications to the NTCSW system are needed to improve profitability

Water Exchange Rate across the Blood-Brain Barrier Is Associated with CSF Amyloid-β 42 in Healthy Older Adults

INTRODUCTION: We tested if water exchange across the blood-brain barrier (BBB), estimated with a noninvasive magnetic resonance imaging (MRI) technique, is associated with cerebrospinal fluid (CSF) biomarkers of Alzheimer\u27s disease (AD) and neuropsychological function. METHODS: Forty cognitively normal older adults (67–86 years old) were scanned with diffusion‐prepared, arterial spin labeling (DP‐ASL), which estimates water exchange rate across the BBB (kw). Participants also underwent CSF draw and neuropsychological testing. Multiple linear regression models were run with kw as a predictor of CSF concentrations and neuropsychological scores. RESULTS: In multiple brain regions, BBB kw was positively associated with CSF amyloid beta (Aβ)42 concentration levels. BBB kw was only moderately associated with neuropsychological performance. DISCUSSION: Our results suggest that low water exchange rate across the BBB is associated with low CSF Aβ42 concentration. These findings suggest that kw may be a promising noninvasive indicator of BBB Aβ clearance functions, a possibility which should be further tested in future research

Hierarchical Clustering Analyses of Plasma Proteins in Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based on Differential Levels of Angiogenic and Inflammatory Biomarkers

Agglomerative hierarchical clustering analysis (HCA) is a commonly used unsupervised machine learning approach for identifying informative natural clusters of observations. HCA is performed by calculating a pairwise dissimilarity matrix and then clustering similar observations until all observations are grouped within a cluster. Verifying the empirical clusters produced by HCA is complex and not well studied in biomedical applications. Here, we demonstrate the comparability of a novel HCA technique with one that was used in previous biomedical applications while applying both techniques to plasma angiogenic (FGF, FLT, PIGF, Tie-2, VEGF, VEGF-D) and inflammatory (MMP1, MMP3, MMP9, IL8, TNFα) protein data to identify informative subsets of individuals. Study subjects were diagnosed with mild cognitive impairment due to cerebrovascular disease (MCI-CVD). Through comparison of the two HCA techniques, we were able to identify subsets of individuals, based on differences in VEGF (p \u3c 0.001), MMP1 (p \u3c 0.001), and IL8 (p \u3c 0.001) levels. These profiles provide novel insights into angiogenic and inflammatory pathologies that may contribute to VCID

WTEC panel report on European nuclear instrumentation and controls

Control and instrumentation systems might be called the 'brain' and 'senses' of a nuclear power plant. As such they become the key elements in the integrated operation of these plants. Recent developments in digital equipment have allowed a dramatic change in the design of these instrument and control (I&C) systems. New designs are evolving with cathode ray tube (CRT)-based control rooms, more automation, and better logical information for the human operators. As these new advanced systems are developed, various decisions must be made about the degree of automation and the human-to-machine interface. Different stages of the development of control automation and of advanced digital systems can be found in various countries. The purpose of this technology assessment is to make a comparative evaluation of the control and instrumentation systems that are being used for commercial nuclear power plants in Europe and the United States. This study is limited to pressurized water reactors (PWR's). Part of the evaluation includes comparisons with a previous similar study assessing Japanese technology

Restoring Rivers One Reach at a Time: Results from a Survey of U.S. River Restoration Practitioners

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72915/1/j.1526-100X.2007.00244.x.pd

Lithium Treatment of APPSwDI/NOS2−/− Mice Leads to Reduced Hyperphosphorylated Tau, Increased Amyloid Deposition and Altered Inflammatory Phenotype

Lithium is an anti-psychotic that has been shown to prevent the hyperphosphorylation of tau protein through the inhibition of glycogen-synthase kinase 3-beta (GSK3β). We recently developed a mouse model that progresses from amyloid pathology to tau pathology and neurodegeneration due to the genetic deletion of NOS2 in an APP transgenic mouse; the APPSwDI/NOS2−/− mouse. Because this mouse develops tau pathology, amyloid pathology and neuronal loss we were interested in the effect anti-tau therapy would have on amyloid pathology, learning and memory. We administered lithium in the diets of APPSwDI/NOS2−/− mice for a period of eight months, followed by water maze testing at 12 months of age, immediately prior to sacrifice. We found that lithium significantly lowered hyperphosphorylated tau levels as measured by Western blot and immunocytochemistry. However, we found no apparent neuroprotection, no effect on spatial memory deficits and an increase in histological amyloid deposition. Aβ levels measured biochemically were unaltered. We also found that lithium significantly altered the neuroinflammatory phenotype of the brain, resulting in enhanced alternative inflammatory response while concurrently lowering the classical inflammatory response. Our data suggest that lithium may be beneficial for the treatment of tauopathies but may not be beneficial for the treatment of Alzheimer's disease

Current Advances in Internet of Underground Things

The latest developments in Internet of Underground Things are covered in this chapter. First, the IOUT Architecture is discussed followed by the explanation of the challenges being faced in this paradigm. Moreover, a comprehensive coverage of the different IOUT components is presented that includes communications, sensing, and system integration with the cloud. An in-depth coverage of the applications of the IOUT in various disciplines is also surveyed. These applications include areas such as decision agriculture, pipeline monitoring, border control, and oil wells

Translational models for vascular cognitive impairment: a review including larger species.

BACKGROUND: Disease models are useful for prospective studies of pathology, identification of molecular and cellular mechanisms, pre-clinical testing of interventions, and validation of clinical biomarkers. Here, we review animal models relevant to vascular cognitive impairment (VCI). A synopsis of each model was initially presented by expert practitioners. Synopses were refined by the authors, and subsequently by the scientific committee of a recent conference (International Conference on Vascular Dementia 2015). Only peer-reviewed sources were cited. METHODS: We included models that mimic VCI-related brain lesions (white matter hypoperfusion injury, focal ischaemia, cerebral amyloid angiopathy) or reproduce VCI risk factors (old age, hypertension, hyperhomocysteinemia, high-salt/high-fat diet) or reproduce genetic causes of VCI (CADASIL-causing Notch3 mutations). CONCLUSIONS: We concluded that (1) translational models may reflect a VCI-relevant pathological process, while not fully replicating a human disease spectrum; (2) rodent models of VCI are limited by paucity of white matter; and (3) further translational models, and improved cognitive testing instruments, are required

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of