MartiTracks: A Geometrical Approach for Identifying Geographical Patterns of Distribution

Panbiogeography represents an evolutionary approach to biogeography, using rational cost-efficient methods to reduce initial complexity to locality data, and depict general distribution patterns. However, few quantitative, and automated panbiogeographic methods exist. In this study, we propose a new algorithm, within a quantitative, geometrical framework, to perform panbiogeographical analyses as an alternative to more traditional methods. The algorithm first calculates a minimum spanning tree, an individual track for each species in a panbiogeographic context. Then the spatial congruence among segments of the minimum spanning trees is calculated using five congruence parameters, producing a general distribution pattern. In addition, the algorithm removes the ambiguity, and subjectivity often present in a manual panbiogeographic analysis. Results from two empirical examples using 61 species of the genus Bomarea (2340 records), and 1031 genera of both plants and animals (100118 records) distributed across the Northern Andes, demonstrated that a geometrical approach to panbiogeography is a feasible quantitative method to determine general distribution patterns for taxa, reducing complexity, and the time needed for managing large data sets

Determinants of worse liver‐related outcome according to HDV infection among HBsAg positive persons living with HIV: Data from the ICONA cohort

Objectives: We aimed to study hepatitis D virus (HDV) prevalence and risk of progression to severe liver-related events (SLRE) in HBsAg positive people living with HIV (PLWH) in Italy; role of HDV-RNA copy levels, HCV coinfection and nadir CD4 counts were also investigated.Methods: People living with HIV (PLWH) from Italian Foundation cohort Naive antiretrovirals (ICONA) with available HBsAg and HDV Ab were enrolled. HBsAg, HDV Ab, HDV-RNA and HDV genotypes were tested. Primary end-point: time from first HDV screening to Severe Liver Related Events (SLRE: decompensated cirrhosis, liver transplantation, HCC). Fine-grey regression models were used to evaluate the association of HDV Ab, HDV-RNA, HDV/HCV coinfection, CD4 nadir and outcome. Secondary end-points: time to SLRE or death; HDV Ab and HDV-RNA prevalence.Results: A total of 152/809 (18.8%) HBsAg positive PLWH showed HDV Ab reactivity; 63/93 (67.7%) were HDV-RNA positive. Being male, persons who inject drugs (PWID), HCV Ab positive, with FIB-4 > 3.25 were independent factors of HDV Ab positivity. In a median follow-up of 5 years, 37 PLWH (4.1% at 5-year) developed SLRE and 97 (12.0%) reached the SLRE or death end-point. HDV-RNA positive (independently from HDV-RNA copy level) PLWH had a 4.6-fold (95%CI 2.0-10.5) higher risk of SLRE than HDV negatives. PLWH positive for both HCV Ab and HDV Ab showed the highest independent risk of SLRE (ASHR: 11.9, 95%CI: 4.6-30.9 vs. HCV neg/HDV neg). Nadir CD4 < 200/mL was associated with SLRE (ASHR: 3.9, 95% 1.0-14.5).Conclusions: One-fifth of the HBsAg positive PLWH harbour HDV infection, and are at high risk of progression to advanced liver disease. HCV contributes to worse outcomes. This population needs urgently effective treatments

Determinants of worse liver-related outcome according to HDV infection among HBsAg positive persons living with HIV: Data from the ICONA cohort

Objectives: We aimed to study hepatitis D virus (HDV) prevalence and risk of progression to severe liver-related events (SLRE) in HBsAg positive people living with HIV (PLWH) in Italy; role of HDV-RNA copy levels, HCV coinfection and nadir CD4 counts were also investigated.Methods: People living with HIV (PLWH) from Italian Foundation cohort Naive antiretrovirals (ICONA) with available HBsAg and HDV Ab were enrolled. HBsAg, HDV Ab, HDV-RNA and HDV genotypes were tested. Primary end-point: time from first HDV screening to Severe Liver Related Events (SLRE: decompensated cirrhosis, liver transplantation, HCC). Fine-grey regression models were used to evaluate the association of HDV Ab, HDV-RNA, HDV/HCV coinfection, CD4 nadir and outcome. Secondary end-points: time to SLRE or death; HDV Ab and HDV-RNA prevalence.Results: A total of 152/809 (18.8%) HBsAg positive PLWH showed HDV Ab reactivity; 63/93 (67.7%) were HDV-RNA positive. Being male, persons who inject drugs (PWID), HCV Ab positive, with FIB-4 > 3.25 were independent factors of HDV Ab positivity. In a median follow-up of 5 years, 37 PLWH (4.1% at 5-year) developed SLRE and 97 (12.0%) reached the SLRE or death end-point. HDV-RNA positive (independently from HDV-RNA copy level) PLWH had a 4.6-fold (95%CI 2.0-10.5) higher risk of SLRE than HDV negatives. PLWH positive for both HCV Ab and HDV Ab showed the highest independent risk of SLRE (ASHR: 11.9, 95%CI: 4.6-30.9 vs. HCV neg/HDV neg). Nadir CD4 < 200/mL was associated with SLRE (ASHR: 3.9, 95% 1.0-14.5).Conclusions: One-fifth of the HBsAg positive PLWH harbour HDV infection, and are at high risk of progression to advanced liver disease. HCV contributes to worse outcomes. This population needs urgently effective treatments

Determinants of worse liver‐related outcome according to HDV infection among HBsAg positive persons living with HIV: Data from the ICONA cohort

Objectives: We aimed to study hepatitis D virus (HDV) prevalence and risk of progression to severe liver-related events (SLRE) in HBsAg positive people living with HIV (PLWH) in Italy; role of HDV-RNA copy levels, HCV coinfection and nadir CD4 counts were also investigated.Methods: People living with HIV (PLWH) from Italian Foundation cohort Naive antiretrovirals (ICONA) with available HBsAg and HDV Ab were enrolled. HBsAg, HDV Ab, HDV-RNA and HDV genotypes were tested. Primary end-point: time from first HDV screening to Severe Liver Related Events (SLRE: decompensated cirrhosis, liver transplantation, HCC). Fine-grey regression models were used to evaluate the association of HDV Ab, HDV-RNA, HDV/HCV coinfection, CD4 nadir and outcome. Secondary end-points: time to SLRE or death; HDV Ab and HDV-RNA prevalence.Results: A total of 152/809 (18.8%) HBsAg positive PLWH showed HDV Ab reactivity; 63/93 (67.7%) were HDV-RNA positive. Being male, persons who inject drugs (PWID), HCV Ab positive, with FIB-4 > 3.25 were independent factors of HDV Ab positivity. In a median follow-up of 5 years, 37 PLWH (4.1% at 5-year) developed SLRE and 97 (12.0%) reached the SLRE or death end-point. HDV-RNA positive (independently from HDV-RNA copy level) PLWH had a 4.6-fold (95%CI 2.0-10.5) higher risk of SLRE than HDV negatives. PLWH positive for both HCV Ab and HDV Ab showed the highest independent risk of SLRE (ASHR: 11.9, 95%CI: 4.6-30.9 vs. HCV neg/HDV neg). Nadir CD4 < 200/mL was associated with SLRE (ASHR: 3.9, 95% 1.0-14.5).Conclusions: One-fifth of the HBsAg positive PLWH harbour HDV infection, and are at high risk of progression to advanced liver disease. HCV contributes to worse outcomes. This population needs urgently effective treatments

New taxa and combinations in Moraceae and Cecropiaceae from Central and South America

Volume: 6Start Page: 230End Page: 25

Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

Mapping forest types and tree species at regional scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we assess the potential of a U‐net convolutional network, a recent deep learning algorithm, to identify and segment (1) natural forests and eucalyptus plantations, and (2) an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView‐3 satellite in the Brazilian Atlantic rainforest region. The networks for forest types and Cecropia trees were trained with 7611 and 1568 red‐green‐blue (RGB) images, respectively, and their dense labeled masks. Eighty per cent of the images were used for training and 20% for validation. The U‐net network segmented forest types with an overall accuracy >95% and an intersection over union (IoU) of 0.96. For C. hololeuca, the overall accuracy was 97% and the IoU was 0.86. The predictions were produced over a 1600 km2 region using WorldView‐3 RGB bands pan‐sharpened at 0.3 m. Natural and eucalyptus forests compose 79 and 21% of the region's total forest cover (82 250 ha). Cecropia crowns covered 1% of the natural forest canopy. An index to describe the level of disturbance of the natural forest fragments based on the spatial distribution of Cecropia trees was developed. Our work demonstrates how a deep learning algorithm can support applications such as vegetation, tree species distributions and disturbance mapping on a regional scale