A morphological model for sexing nestling peregrine falcons (Falco Peregrinus Macropus) verified through genetic analysis

Adult peregrine falcons (Falco peregrinus macropus) have monotypic plumage and display strong reversed sexual dimorphism, with females significantly larger than males. Reversed sexual dimorphism is measurable among nestlings in the latter stages of their development and can therefore be used to differentiate between sexes. In the early stages of development, however, nestlings cannot be sexed with any degree of certainty because morphological differentiation between the sexes is not well developed. During this study we developed a model for sexing younger nestlings based on genetic analysis and morphometric data collected as part of a long-term banding study of this species. A discriminant function model based on morphological characteristics was developed for determining the sex of nestlings (n = 150) in the field and was shown to be 96.0% accurate. This predictive model was further tested against an independent morphometric dataset taken from a second group of nestlings (n = 131). The model correctly allocated sex to 96.2% of this second group of nestlings. Sex can reliably be determined (98.6% accurate) for nestlings that have a wing length of at least 9 cm using this model. Application of this model, therefore, allows the banding of younger nestlings and, as such, significantly increases the period of time over which banding can occur. Another important implication of this model is that by banding nestlings earlier, they are less likely to jump from the nest, therefore reducing the risk of injury to both the brood and the bander.

Morphological evolution of nanofibers during electrospinning

Electrospinning is a very useful technique to produce polymeric nanofibers. It involves fast-drawing a polymer fluid into nanofibers under a strong electric filed, and depositing randomly on an electrode collector to form non-woven nanofiber mat in most cases [1]. The fibre stretching during electrospinning is a fast and incessant process which can be divided into three consecutive stages: jet initiation, whipping instability and fibre deposition. From the initial jet to dry fibres, the fibre stretching takes place in milliseconds, so it has been hardly so far to observe fiber morphology changes by any normal methods, such as high speed photography [2-5]. In this study, we used a facile and practical approach to realize the observation of nanofiber morphology changes during electrospinning. Through a special collection device with coagulation bath, newly electrospun nanofibers can be solidified at different electro spinning distances, and by associating the fiber morphology with the electrospinning distance (d), the morphological evolution of nanofibers can be established. We used polyacrylonitrile (PAN) and polystyrene (PS) as two model polymers to demonstrate this method in present research. From experimental results, we found the massive jet-thinning happens at the initial stage of the process. The formation of uniform PAN nanofibers (7%) and the beads structure changes on beads-on-string PAN nanofibers (5%) have also been successful observed. Using the same method, we also observed PS nanofiber (10%) morphology changes to understand the beads formation 011 nanofibers during electrospinning process, and how the beads was eliminated when ionic surfactant is added into the PS solution for electrospinning.

A physarum network evolution model based on IBTM

The traditional Cellular Automation-based Physarum model reveals the process of amoebic self-organized movement and self-adaptive network formation based on bubble transportation. However, a bubble in the traditional Physarum model often transports within active zones and has little change to explore newareas.And the efficiency of evolution is very low because there is only one bubble in the system. This paper proposes an improved model, named as Improved Bubble Transportation Model (IBTM). Our model adds a time label for each grid of environment in order to drive bubbles to explore newareas, and deploysmultiple bubbles in order to improve the evolving efficiency of Physarum network.We first evaluate the morphological characteristics of IBTM with the real Physarum, and then compare the evolving time between the traditional model and IBTM. The results show that IBTM can obtain higher efficiency and stability in the process of forming an adaptive network.

A hybrid model for named entity recognition using unstructured medical text

Named entity recognition (NER) is an essential step in the process of information extraction within text mining. This paper proposes a technique to extract drug named entities from unstructured and informal medical text using a hybrid model of lexicon-based and rule-based techniques. In the proposed model, a lexicon is first used as the initial step to detect drug named entities. Inference rules are then deployed to further extract undetected drug names. The designed rules employ part of speech tags and morphological features for drug name detection. The proposed hybrid model is evaluated using a benchmark data set from the i2b2 2009 medication challenge, and is able to achieve an f-score of 66.97%.