Soil data recency: The foundation for harmonizing soil data across time.

Sustainable soil resource management depends on reliable soil information, often derived from 'legacy soil data' or a combination of old and new soil data. However, the task of harmonizing soil data collected at different times remains a largely unexplored in the literature. Addressing this challenge requires incorporating the temporal dimension into mathematical and statistical models for spatio-temporal soil studies. This study aimed to create a comprehensive framework for harmonizing soil data across various time. We assessed the integration of historical and recent soil data, ranging from 4 to 48 years old data, using soil data recency analysis. To achieve this, we introduced an 'age of data' attribute, calculating the time difference between soil survey years and the present (e.g., 2022). We applied three machine learning models - Decision Trees (DT), Random Forest (RF), Gradient Boosting (GBM) - to a dataset containing 6339 sites and 28,149 depth-harmonized layers. The results consistently demonstrated robust performance across models, RF outperforming with an R-squared value of 0.99, RMSE of 1.41, and a concordance of 0.97. Similarly, DT and GBM also showed strong predictive power. Terrain-derived environmental covariates played a more important role than land use and land cover (LULC) change in predicting soil data recency. While LULC change showed soil organic carbon concentration variability across the different depths, it was a less important factor. Anthropogenic factors, such as LULC change and normalized difference vegetation index (NDVI), were not primary determinants of soil data recency. Variations in soil depth had no impact on predicting soil data recency. This study validated that terrain-derived covariates, especially elevation factors, effectively explain the quality of older soil data when predicting current soil attributes using the soil data recency concept. This approach has the potential to enhance real-time estimates, such as carbon budgets, and we emphasize its importance in global earth system models.

Hepatitis B virus infection among people attending the voluntary counselling and testing centre and anti-retroviral therapy clinic of St Paul's General Specialised Hospital, Addis Ababa, Ethiopia.

OBJECTIVE: To determine the prevalence of hepatitis B virus (HBV) infection and its risk factors among people attending the HIV voluntary counselling and testing (VCT) centre and anti-retroviral therapy (ART) clinic at St Paul's General Specialised Hospital. METHODS: A cross-sectional study was carried out on consecutive attendees from 24 January 2007 to 15 February 2007. Blood samples and data on sociodemographic and HBV risk factors were collected from 620 individuals (384 VCT and 236 HIV-infected ART clinic clients). Sera were screened for hepatitis B surface antigen (HBsAg), antibody to hepatitis B core antigen (anti-HBc) and hepatitis B e antigen (HBeAg). RESULTS: The prevalence of HBsAg and anti-HBc in VCT clients was 5.7% and 44.8%, respectively. Among HIV-infected persons, 3.9% were seropositive for HBsAg. There was no significant difference in HBsAg or anti-HBc seropositivity between HIV-positive and HIV-negative subjects. Anti-HBc positivity was significantly higher in men, in the age range 40-49 years, and in subjects with a history of catheterisation. CONCLUSION: This study shows a high prevalence and similar distribution of HBV infection in HIV-positive and HIV-negative people. However, with the emphasis given to HIV-positive cases, screening for HBV infection is important.

Timing of the introduction into Ethiopia of subcluster C' of HIV type 1 subtype C.

Viruses circulating in Ethiopia during the 1990s cluster with main subtype C, but a significant subcluster, C', was noted in multiple analyses. This subcluster of subtype C(C') was in a fifty-fifty equilibrium with the main subtype C (Abebe et al., AIDS Res Hum Retroviruses 2000;16:1909-1914). To analyze genetic diversification within the subcluster of HIV-1 subtype C designated C' in the course of the epidemic in Ethiopia, we analyzed 165 env gp120 V3 sequences obtained between 1988 and 1999. We observed a highly significant positive correlation between sampling years of individual sequences and their synonymous distances to the reconstructed common ancestor of the HIV-1 subtype C' subcluster. The extrapolation of the regression line of synonymous distances back to the date when no synonymous heterogeneity was present among the Ethiopian HIV-1 C' population allowed us to estimate 1982 (95% CI, 1980-1983) as the year of the onset of HIV-1 C' genetic diversification and expansion in Ethiopia. These results are in agreement with retrospective epidemiological and serological data, which demonstrated the absence of an HIV-1 epidemic in the Ethiopian population before the 1980s.

Identification of a genetic subcluster of HIV type 1 subtype C (C') widespread in Ethiopia.

Others and we have previously shown that subtype C is the predominant HIV-1 subtype and the major cause of AIDS in Ethiopia. The present study shows that subtype C in Ethiopia has a genetic subcluster, designated C', has not increased in frequency, or spread geographically, over the period 1988 (%C' = 23/53) to 1996-1997 (%C' = 26/50). There is no association of the HIV-1 subtype C or subcluster C' with geographic location, time of sample collection, or risk group in Ethiopia. Of 105 randomly collected samples representing 7 different towns in Ethiopia, all but 2 (1 subtype A from Addis Ababa, 1997 and 1 subtype D from Dessie, 1996) belong to subtype C.