Development of biomimetic microengineered hydrogel fibers for tendon regeneration

Musculoskeletal diseases are one of the leading causes of disability worldwide. Tendon injuries are responsible for substantial morbidity, pain and disability. Tissue engineering strategies aim at translating tendon structure into biomimetic materials. The main goal of the present study is to develop microengineered hydrogel fibers through the combination of microfabrication and chemical interactions between oppositely charged polyelectrolytes. For this, methacrylated hyaluronic acid (MeHA) and chondroitin sulfate (MeCS) were combined with chitosan (CHT). Hydrogel fibers were obtained by injecting polymer solutions (either MeHA or MeHA/MeCS and CHT) in separate microchannels that join at a y-junction, with the materials interacting upon contact at the interface. To evaluate cell behavior, human tendon derived cells (hTDCs) were isolated from tendon surplus samples during orthopedic surgeries and seeded on top of the fibers. hTDCs adhered to the surface of the fibers, remaining viable, and were found to be expressing CD44, the receptor for hyaluronic acid. The synthesis of hydrogel fibers crosslinkable through both physical and chemical mechanisms combined with microfabrication technology allows the development of biomimetic structures with parallel fibers being formed towards the replication of tendon tissue architecture.

Discrimination of bacteriophage infected cells using locked nucleic acid fluorescent in situ hybridization (LNA-FISH)

Bacteriophage-host interaction studies in biofilm structures are still challenging due to the technical limitations of traditional methods. The aim of this study was to provide a direct fluorescence in situ hybridization (FISH) method based on locked nucleic acid (LNA) probes, which targets the phage replication phase, allowing the study of population dynamics during infection. Bacteriophages specific for two biofilm-forming bacteria, Pseudomonas aeruginosa and Acinetobacter, were selected. Four LNA probes were designed and optimized for phage-specific detection and for bacterial counterstaining. To validate the method, LNA-FISH counts were compared with the traditional plaque forming unit (PFU) technique. To visualize the progression of phage infection within a biofilm, colony-biofilms were formed and infected with bacteriophages. A good correlation (r=0.707) was observed between LNA-FISH and PFU techniques. In biofilm structures, LNA-FISH provided a good discrimination of the infected cells and also allowed the assessment of the spatial distribution of infected and non-infected populations.

Avaliação quantitativa da geodiversidade: desenvolvimento de instrumentos metodológicos com aplicação ao ordenamento do território

Tese de Doutoramento em Ciências (Especialidade de Geologia)

Concise server-wide causality management for eventually consistent data stores

Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of net work partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conflicts. Another aspect of eventual consistency is detecting write conflicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a nonnegligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, perkey metadata cannot be permanently removed without coordination. Weintroduceanewcausalitymanagementframeworkforeventuallyconsistentdatastores,thatleveragesnodelogicalclocks(BitmappedVersion Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new efficient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.