The Role of Dilation and Internal Urethrotomy as a Risk Factor of Failure in Patients Who Undergoing One-Stage Bulbar Oral Graft Urethroplasty

Purpose: To test the hypothesis if dilation or direct visual internal urethrotomy (DVIU) are predictive of urethroplasty failure. Retrospective study, from 1999 to 2010, including184 patients (median age 37 years) who underwent ventral onlay oral graft urethroplasty for bulbar strictures. Exclusion criteria were traumatic strictures, lichen sclerosus, failed hypospadias repair, failed urethroplasty, panurethral strictures, and incomplete medical charts. Pre-operative evaluation included clinical history, physical examination, urine culture, residual urine measurement, uroflowmetry, urethrography, ultrasound and urethroscopy. Surgery was considered a failure when any post-operative instrumentation was needed. Median follow-up was 48 months. Out of 184 patients, 38 (20.7%) had not undergone previous treatment, 7 (3.8%) had undergone dilation, 81 (44%) DVIU and 58 (31.5%) DVIU associated with dilation. Out of 184 patients, 157 (85.3%) were successful and 27 (14.7%) failures. Out of 38 patients who had not undergone previous treatment, 33 (86.8%) were successful;out of 7 patients who had undergone dilation, 6 (85.7%) were successful;out of 81 patients who had undergone DVIU, 72 (88.9%) were successful;out of 58 patients who had undergone DVIU and dilation, 46 (79.3%) were successful. According to the number of previous DVIU, ventral graft urethroplasty for bulbar strictures showed high failure rate in patients who had undergone more than four DVIU associated or not with dilation.

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

X-ray diffraction(XRD)data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns.We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic,physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database(ICSD)and experimental data.As a test case,115 thin-film metalhalides spanning three dimensionalities and seven space groups are synthesized and classified.After testing various algorithms,we develop and implement an all convolutional neural network,with cross-validated accuracies for dimensionality and space group classification of 93 and 89%,respectively.We propose average class activation maps,computed from a global average pooling layer,to allow high model interpretability by human experimentalists,elucidating the root causes of misclassification.Finally,we systematically evaluate the maximum XRD pattern step size(data acquisition rate)before loss of predictive accuracy occurs,and determine it to be 0.16°2θ,which enables an XRD pattern to be obtained and classified in 5.5 min or less.

Determination of surface color of‘all yellow’mango cultivars using computer vision

Image processing techniques are increasingly applied in sorting applications of agricultural products.This work has assessed the use of image processing for inspecting surface color of two Thai mango cultivars.A computer vision system(CVS)was developed and experiments were conducted to monitor peel color change during the ripening process.Conversion of RGB to CIE-LAB values was done via image processing and prediction models were developed to estimate color parameters from CVS data.Performance evaluations showed insufficient prediction for L values(R2=0.42-0.58),but better results for A and B values(R2=0.90-0.95 and 0.80-0.82,respectively).Compared to the calculated color values hue angle and chroma,a yellowness index computed from intermediate XYZ values was found to be much more adept at accurately predicting peel color from CVS data.Correlations were strong for both cultivars(R2=0.93 for‘Nam Dokmai’and R2=0.95 for‘Maha Chanok’).Results from classification analysis indicated satisfactory results for classifying fruits according to ripeness based on yellowness.Success rates of true positives in the categories unripe,ripe and overripe ranged 72%-92%for‘Nam Dokmai’and 98%-100%for‘Maha Chanok’.Therefore,it was shown that the CVS was capable of producing accurate color values for the two mango cultivars investigated.The findings of this study can be incorporated for development of a robust system for quality prediction and establishment of a CVS for automatic grading and sorting of mangos.

Monitoring physiological responses to water stress in two maize varieties by infrared thermography

Water stress is one of the main causes of yield reductions in crops,especially in arid and semi-arid regions where the water supply is limited.Plant water status is frequently assessed by pre-dawn leaf water potential(ΨPD)or leaf stomata conductance(gL)measurements,in support of advanced irrigation scheduling.However,both methods are time and labour consuming.A non-invasive approach to water status detection is the use of infrared thermography(IRT).This experiment was conducted in a greenhouse on two potted maize varieties under irrigated and non-irrigated conditions,and the measurements began when the crop had reached its twelve leaf stage.In order to establish the IRT measurements for detecting the water status of maize,an IRT-based crop water stress index(CWSI)was calculated and compared with simultaneously measuredΨPD and gL data.Good correlations were found between CWSI and gL data(r2=0.71&0.81),as well between CWSI andΨPD data(r2=0.53&0.81).These results highlight the appropriateness of infrared thermal imagery to detect and differentiate between the crop water statuses of different genotypes.

Influence of colour changes and moisture content during banana drying on laser backscattering

Pre-drying treatments are frequently employed to preserve fruit quality.The objective of this research was to monitor colour changes of banana during drying by laser backscattering and to determine the influence of the fruit discolouration on the light distribution into banana tissue.Moreover,to examine the influence of drying on the laser backscatter,the relationship between moisture content and relative laser area of banana slices was analyzed with different degrees of colour degradation.The experiments were conducted at drying air temperature of 63℃with various pre-treatments like chilling,soaking in ascorbic/citric acid and dipping in distilled water.An untreated sample was used as a control.A laser diode emitting at 670 nm with 3 mW power was used as light source.The backscattering relative laser area was used as an indicator for the light absorption into the tissue.The high result achieved on coefficient of determination R^(2)(>0.93)confirmed linear relationship between relative laser area and moisture content.Treatment with ascorbic acid gave the best prediction of the moisture content with the standard error of 5.7 and 8.8 for the estimated intercept and slope.The results showed a significant difference of lightness(L*values)during drying according to the different treatments.As a result,colour degradation did not have a significant influence on the absorption of light at 670 nm wavelength.