Phylogenetic structure of Guinea-Bissau ethnic groups for mitochondrial DNA and Y chromosome genetic systems

The maternal and paternal genetic profile of Guineans is markedly sub-Saharan West African, with the majority of lineages belonging to L0-L3 mtDNA sub-clusters and E3a-M2 and E1-M33 Y chromosome haplogroups. Despite the sociocultural differences among Guinea-Bissau ethnic groups,marked by the supposedly strict admixture barriers, their genetic pool remains largely common. Their extant variation coalesces at distinct timeframes, from the initial occupation of the area to later inputs of people. Signs of recent expansion in mtDNA haplogroups L2a-L2c and NRY E3a-M2 suggest population growth in the equatorial western fringe, possibly supported by an early local agricultural centre, and to which the Mandenka and the Balanta people may relate. Non-West African signatures are traceable in less frequent extant haplogroups, fitting well with the linguistic and historical evidence regarding particular ethnic groups: the Papel and Felupe-Djola people retain traces of their putative East African relatives; U6 and M1b among Guinea-Bissau Bak-speakers indicate partial diffusion to Sahel of North African lineages; U5b1b lineages in Fulbe and Papel represent a link to North African Berbers, emphasizing the great importance of post-glacial expansions; exact matches of R1b-P25 and E3b1-M78 with Europeans likely trace back to the times of the slave trade.

Graphical constraints: a graphical user interface for constraint problems

A constraint satisfaction problem is a classical artificial intelligence paradigm characterized by a set of variables (each variable with an associated domain of possible values), and a set of constraints that specify relations among subsets of these variables. Solutions are assignments of values to all variables that satisfy all the constraints. Many real world problems may be modelled by means of constraints. The range of problems that can use this representation is very diverse and embraces areas like resource allocation, scheduling, timetabling or vehicle routing. Constraint programming is a form of declarative programming in the sense that instead of specifying a sequence of steps to execute, it relies on properties of the solutions to be found, which are explicitly defined by constraints. The idea of constraint programming is to solve problems by stating constraints which must be satisfied by the solutions. Constraint programming is based on specialized constraint solvers that take advantage of constraints to search for solutions. The success and popularity of complex problem solving tools can be greatly enhanced by the availability of friendly user interfaces. User interfaces cover two fundamental areas: receiving information from the user and communicating it to the system; and getting information from the system and deliver it to the user. Despite its potential impact, adequate user interfaces are uncommon in constraint programming in general. The main goal of this project is to develop a graphical user interface that allows to, intuitively, represent constraint satisfaction problems. The idea is to visually represent the variables of the problem, their domains and the problem constraints and enable the user to interact with an adequate constraint solver to process the constraints and compute the solutions. Moreover, the graphical interface should be capable of configure the solver’s parameters and present solutions in an appealing interactive way. As a proof of concept, the developed application – GraphicalConstraints – focus on continuous constraint programming, which deals with real valued variables and numerical constraints (equations and inequalities). RealPaver, a state-of-the-art solver in continuous domains, was used in the application. The graphical interface supports all stages of constraint processing, from the design of the constraint network to the presentation of the end feasible space solutions as 2D or 3D boxes.