Surgical Management of Pachyonychia Congenita in a 3-Year-Old

Sudduth Jack D.,Clinker Christopher,Holdaway Matthew,Marquez Jessica L.,Veith Jacob,Wright Thomas,Rockwell W. Bradford 대한성형외과학회 2023 Archives of Plastic Surgery Vol.50 No.6

Pachyonychia congenita is a rare genetic disorder characterized by hypertrophic nail plates, hyperkeratotic nail beds, and thickened hyponychium of the fingers and toes, impairing manual dexterity and resulting in poor aesthetics. The current body of literature describes various treatment modalities, but no singular approach has been defined as the gold standard. In this case, the authors employed different surgical techniques for treating pachyonychia congenita to evaluate the most effective approach. A 3-year-old boy presented with hypertrophic nail growth involving all digits of both hands and feet. Three surgical procedures were performed on the patient's fingers and toes using germinal matrix excision (GME) alone, GME plus partial sterile matrix excision (pSME), or GME plus complete sterile matrix excision (cSME). The digits treated with GME + cSME exhibited no recurrence of nail growth. Those treated with GME alone exhibited recurrence of hypertrophic nail growth, although their growth slowed. Excision of GME + cSME prevented recurrence of hypertrophic nails, while GME alone or with pSME led to slower-growing hypertrophic nails. Complete excision of the germinal and sterile matrices with skin graft closure may be a definitive treatment for pachyonychia congenita, but further studies are needed to validate these findings.

이온선택성 전극을 이용한 토양추출물의 질산 및 칼륨이온 측정

김학진 ( H. J. Kim ),K. A. Sudduth,J. W. Hummel,Kenneth A. Sudduth,John W. Hummel 한국농업기계학회 2006 바이오시스템공학 Vol.31 No.6

Soil physical property estimation from soil strength and apparent electrical conductivity sensor data

Cho, Yongjin,Sudduth, Kenneth A.,Chung, Sun-Ok Elsevier 2016 Biosystems engineering Vol.152 No.-

<P>Proximal soil sensing is an attractive approach for quantifying soil properties, but many currently available sensors do not respond to a single soil property. For example, soil strength and apparent electrical conductivity (EC<SUB>a</SUB>) sensor measurements are significantly affected by soil texture, bulk density (BD), and water content (WC). The objective of this study was to explore the potential for estimating soil texture, BD, and WC using combinations of sensor-based soil strength and EC<SUB>a</SUB> data obtained from sites with varying soil physical properties. Data collected from three research sites in Missouri included on-the-go horizontal soil strength at five depths up to 0.5 m on a 0.1-m interval, cone index measurements at the same depths, EC<SUB>a</SUB> measured by a Veris 3100, and depth-dependent, laboratory-determined soil properties. An EC<SUB>a</SUB> model inversion approach was used to generate layer EC values corresponding to the depth increments of the other variables. Fits of models using EC to estimate WC were variable (R<SUP>2</SUP> = 0.31–0.79). Best fitting BD estimation models (R<SUP>2</SUP> = 0.11–0.55) generally included EC, but soil strength was included in fewer than half of the models. BD model fits were improved considerably by adding lab-measured WC to the model (R<SUP>2</SUP> = 0.30–0.86), suggesting the need for a WC sensor. Soil clay texture fraction models based on EC and WC fit well (R<SUP>2</SUP> = 0.80–0.93). This study showed the potential of combining data from multiple mobile proximal sensors to estimate important soil physical properties.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Soil physical properties were estimated from soil sensor measurements. </LI> <LI> Properties examined were water content (WC), bulk density, and clay fraction. </LI> <LI> Best WC estimates used both soil strength and apparent electrical conductivity (EC<SUB>a</SUB>). </LI> <LI> Best bulk density estimates included lab-measured water content in the model. </LI> <LI> Clay texture fraction was well-estimated at most measurement depths by EC<SUB>a</SUB>. </LI> </UL> </P>

On-the-go Soil Strength Profile Sensor to Quantify Spatial and Vertical Variations in Soil Strength

Chung, Sun-Ok,Sudduth, Kenneth A. Korean Society for Agricultural Machinery 2005 Agricultural and Biosystems Engineering Vol.6 No.2

Because soil compaction is a concern in crop production and environmental pollution, quantification and management of spatial and vertical variability in soil compaction for soil strength) would be a useful aspect of site -specific field management. In this paper, a soil strength profile sensor (SSPS) that could take measurements continuously while traveling across the field was developed and the performance was evaluated through laboratory and field tests. The SSPS obtained data simultaneously at 5 evenly spaced depths up to 50 em using an array of load cells, each of which was interfaced with a soil-cutting tip. Means of soil strength measurements collected in adjacent, parallel transects were not significantly different, confirming the repeatability of soil strength sensing with the SSPS. Maps created with sensor data showed spatial and vertical variability in soil strength. Depth to the restrictive layer was different for different field locations, and only 5 to 16% of the tested field areas were highly compacted.

Comparison of Remote Sensing and Crop Growth Models for Estimating Within-Field LAI Variability

Hong, Suk-Young,Sudduth, Kenneth-A.,Kitchen, Newell-R.,Fraisse, Clyde-W.,Palm, Harlan-L.,Wiebold, William-J. The Korean Society of Remote Sensing 2004 大韓遠隔探査學會誌 Vol.20 No.3

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.

Characterization of Cone Index and Tillage Draft Data to Define Design Parameters for an On-the-go Soil Strength Profile Sensor

Chung S. O.,Sudduth Kenneth A. Korean Society for Agricultural Machinery 2004 Agricultural and Biosystems Engineering Vol.5 No.1

Precision agriculture aims to minimize costs and environmental damage caused by agriculture and to maximize crop yield and profitability, based on information collected at within-field locations. In this process, quantification of soil physical properties, including soil strength, would be useful. To quantify and manage variability in soil strength, there is need for a strength sensor that can take measurements continuously while traveling across the field. In this paper, preliminary analyses were conducted using two datasets available with current technology, (1) cone penetrometer readings collected at different compaction levels and for different soil textures and (2) tillage draft (TD) collected from an entire field. The objective was to provide information useful for design of an on-the-go soil strength profile sensor and for interpretation of sensor test results. Analysis of cone index (CI) profiles led to the selection of a 0.5-m design sensing depth, 10-MPa maximum expected soil strength, and 0.1-MPa sensing resolution. Compaction level, depth, texture, and water content of the soil all affected CI. The effects of these interacting factors on data obtained with the soil strength sensor should be investigated through experiments. Spatial analyses of CI and TD indicated that the on-the-go soil strength sensor should acquire high spatial-resolution, high-frequency ($\ge$ 4 Hz) measurements to capture within-field spatial variability.

Disposable Nitrate-Selective Optical Sensor Based on Fluorescent Dye

Kim, Gi-Young,Sudduth, Kenneth A.,Grant, Sheila A.,Kitchen, Newell R. Korean Society for Agricultural Machinery 2012 바이오시스템공학 Vol.37 No.3

Purpose: This study was performed to develop a simple, disposable thin-film optical nitrate sensor. Methods: The sensor was fabricated by applying a nitrate-selective polymer membrane on the surface of a thin polyester film. The membrane was composed of polyvinylchloride (PVC), plasticizer, fluorescent dye, and nitrate-selective ionophore. Fluorescence intensity of the sensor increased on contact with a nitrate solution. The fluorescence response of the optical nitrate sensor was measured with a commercial fluorospectrometer. Results: The optical sensor exhibited linear response over four concentration decades. Conclusions: Nitrate ion concentrations in plant nutrient solutions can be determined by direct optical measurements without any conditioning before measurements.

Relating Hyperspectral Image Bands and Vegetation Indices to Corn and Soybean Yield

Jang Gab-Sue,Sudduth Kenneth A.,Hong Suk-Young,Kitchen Newell R.,Palm Harlan L. The Korean Society of Remote Sensing 2006 大韓遠隔探査學會誌 Vol.22 No.3

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

Sensing Nitrate and Potassium Ions in Soil Extracts Using Ion-Selective Electrodes

Kim, H.J.,Sudduth Kenneth A.,Hummel John W. Korean Society for Agricultural Machinery 2006 바이오시스템공학 Vol.31 No.6

Automated sensing of soil macronutrients would allow more efficient mapping of soil nutrient spatial variability for variable-rate nutrient management. The capabilities of ion-selective electrodes for sensing macronutrients in soil extracts can be affected by the presence of other ions in the soil itself as well as by high concentrations of ions in soil extractants. Adoption of automated, on-the-go sensing of soil nutrients would be enhanced if a single extracting solution could be used for the concurrent extraction of multiple soil macronutrients. This paper reports on the ability of the Kelowna extractant to extract macronutrients (N, P, and K) from US Corn Belt soils and whether previously developed PVC-based nitrate and potassium ion-selective electrodes could determine the nitrate and potassium concentrations in soil extracts obtained using the Kelowna extractant. The extraction efficiencies of nitrate-N and phosphorus obtained with the Kelowna solution for seven US Corn Belt soils were comparable to those obtained with IM KCI and Mehlich III solutions when measured with automated ion and ICP analyzers, respectively. However, the potassium levels extracted with the Kelowna extractant were, on average, 42% less than those obtained with the Mehlich III solution. Nevertheless, it was expected that Kelowna could extract proportional amounts of potassium ion due to a strong linear relationship ($r^2$ = 0.96). Use of the PVC-based nitrate and potassium ion-selective electrodes proved to be feasible in measuring nitrate-N and potassium ions in Kelowna - soil extracts with almost 1 : 1 relationships and high coefficients of determination ($r^2$ > 0.9) between the levels of nitrate-N and potassium obtained with the ion-selective electrodes and standard analytical instruments.

Estimation of Within-Field LAI Variability with Remote Sensing Techniques and Crop Growth Models

Suk-Young Hong,Kenneth Sudduth,Newell Kitchen,Yi-Hyun Kim,Jee-Min Lee,Sang-Kyu Rim 한국토양비료학회 2005 한국토양비료학회 학술발표회 초록집 Vol.2005 No.9