Modeling of stem form and volume through machine learning.

Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function for acácia negra. We used cubing data, and fit equations with Schumacher and Hall volumetric model and with Hradetzky taper function, compared to the algorithms: k nearest neighbor (k-NN), Random Forest (RF) and Artificial Neural Networks (ANN) for estimation of total volume and diameter to the relative height. Models were ranked according to error statistics, as well as their dispersion was verified. Schumacher and Hall model and ANN showed the best results for volume estimation as function of dap and height. Machine learning methods were more accurate than the Hradetzky polynomial for tree form estimations. ML models have proven to be appropriate as an alternative to traditional modeling applications in forestry measurement, however, its application must be careful because fit-based overtraining is likely.

Efficacy of HCV core antigen detection during the preseroconversion period.

BACKGROUND: The purpose of this study was to compare the performances of HCV core antigen (HCV Ag) testing with HCV RNA detection during the preseroconversion period. STUDY DESIGN AND METHODS: Six HCV antibody (HCV Ab)-negative and HCV RNA-positive blood samples from 6 donors and 135 serial samples from 28 patients who had undergone hemodialysis, collected a mean of 90 days before the detection of HCV Ab, were tested by ELISA for the detection of HCV Ag and by PCR to quantify HCV RNA. RESULTS: Five of the six donors were positive for HCV Ag. The donor with a negative HCV Ag test had the lowest viral load. In the hemodialysis patients, the 43 first specimens of the series were HCV RNA negative. Of the 92 specimens that were HCV RNA positive, 81 (88%) were positive for HCV Ag. Among the 74 samples with more than 10(5) RNA copies, 71 (96%) were HCV Ag positive. Average time from first viremic bleed to first HCV Ag-positive bleed was estimated at 2.0 days and that to first HCV Ab-positive bleed at 50.8 days. CONCLUSION: HCV Ag testing permits the detection of an HCV infection about 1.5 months earlier than the HCV Ab screening tests and an average of only 2 days later than quantitative HCV RNA detection in individual specimens.

GB virus type C/HGV markers in HCV RNA-positive French blood donors: correlation with HCV genotypes and risk factors.

BACKGROUND: Exposure to GB virus type C/HGV (GBV-C/HGV) could be determined by detection either of RNA by RT-PCR or of antibodies of the envelope protein E2. STUDY DESIGN AND METHODS: The aim of the study was to determine the proportion of the GBV-C/HGV markers of infection in a blood donor population infected with HCV and to identify GBV-C/HGV routes of transmission that are associated with HCV genotypes and risk factors. RESULTS: Among 306 HCV RNA-positive blood donors, the proportion of GBV-C/HGV RNA-positive donors and anti-E2-positive donors was 19.3 percent (95% CI = 15.0-24.2%) and 42.1 percent (95% CI = 36.6-47.9%), respectively. Exposure to GBV-C/HGV (RNA or anti-E2) was significantly associated with the risk factor of IV drug use. There was a trend toward association with HCV subtypes 1a and 3a, probably because these HCV subtypes are the most frequent in IV drug users. No correlation was observed between ALT elevation and the presence of GBV-C/HGV RNA. CONCLUSION: In persons with HCV infection, IV drug use seems to be a major route of GBV-C/HGV transmission. Precautions taken to avoid HCV infection will probably also decrease GBV-C/HGV transmission.