Web-based bug tracking solutions : applied to business

Estágio realizado na Wipro RetailTese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Unraveling the genetic background of individuals with a clinical familial hypercholesterolemia phenotype

Familial hypercholesterolemia (FH) is a common genetic disorder of lipid metabolism caused by pathogenic/likely pathogenic variants in LDLR, APOB, and PCSK9 genes. Variants in FH-phenocopy genes (LDLRAP1, APOE, LIPA, ABCG5, and ABCG8), polygenic hypercholesterolemia, and hyperlipoprotein (a) [Lp(a)] can also mimic a clinical FH phenotype. We aim to present a new diagnostic tool to unravel the genetic background of clinical FH phenotype. Biochemical and genetic study was performed in 1,005 individuals with clinical diagnosis of FH, referred to the Portuguese FH Study. A next-generation sequencing panel, covering eight genes and eight SNPs to determine LDL-C polygenic risk score and LPA genetic score, was validated, and used in this study. FH was genetically confirmed in 417 index cases: 408 heterozygotes and 9 homozygotes. Cascade screening increased the identification to 1,000 FH individuals, including 11 homozygotes. FH-negative individuals (phenotype positive and genotype negative) have Lp(a) >50 mg/dl (30%), high polygenic risk score (16%), other monogenic lipid metabolism disorders (1%), and heterozygous pathogenic variants in FH-phenocopy genes (2%). Heterozygous variants of uncertain significance were identified in primary genes (12%) and phenocopy genes (7%). Overall, 42% of our cohort was genetically confirmed with FH. In the remaining individuals, other causes for high LDL-C were identified in 68%. Hyper-Lp(a) or polygenic hypercholesterolemia may be the cause of the clinical FH phenotype in almost half of FH-negative individuals. A small part has pathogenic variants in ABCG5/ABCG8 in heterozygosity that can cause hypercholesterolemia and should be further investigated. This extended next-generation sequencing panel identifies individuals with FH and FH-phenocopies, allowing to personalize each person’s treatment according to the affected pathway

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved