Molecular systematics of swifts of the genus Chaetura (Aves: Apodiformes: Apodidae)

Phylogenetic relationships among swifts of the morphologically conservative genus Chaetura were studied using mitochondrial and nuclear DNA sequences. Taxon sampling included all species and 21 of 30 taxa (species and subspecies) within Chaetura. Our results indicate that Chaetura is monophyletic and support the division of the genus into the two subgenera previously identified using plumage characters. However, our genetic data, when considered in combination with phenotypic data, appear to be at odds with the current classification of some species of Chaetura. We recommend that C. viridipennis, currently generally treated as specifically distinct from C. chapmani, be returned to its former status as C. chapmani viridipennis, and that C. andrei, now generally regarded as synonymous with C. vauxi aphanes, again be recognized as a valid species. Widespread Neotropical species C. spinicaudus is paraphyletic with respect to more range-restricted species C. fumosa, C. egregia, and C. martinica. Geographically structured genetic variation within some other species of Chaetura, especially notable in C. cinereiventris, suggests that future study may lead to recognition of additional species in this genus. Biogeographic analysis indicated that Chaetura originated in South America and identified several dispersal events to Middle and North America following the formation of the Isthmus of Panama

Tropical Cyclone Landfall Frequency and Large-Scale Environmental Impacts along Karstic Coastal Regions (Yucatan Peninsula, Mexico)

Tropical cyclones (TCs) are natural systems that develop over ocean basins and are key components of the atmospheric activity during the warm season. However, there are still knowledge gaps about the combined positive and negative TC impacts on the structure and function of coastal socio-ecosystems. Using remote sensing tools, we analyzed the frequency, trajectory, and intensity of 1894 TCs from 1851-2019 to identify vulnerable hotspots across the Yucatan Peninsula (YP), Mexico. A total of 151 events hit the YP, with 96% of landings on the eastern coast. We focused on three major hurricanes (Emily and Wilma, 2005; Dean, 2007) and one tropical storm (Stan, 2005) to determine the impacts on cumulative precipitation, vegetation change, and coastal phytoplankton (Chl-a) distribution across the YP. Despite a short inland incursion, Wilma\u27s environmental damage was coupled to strong winds (157-241 km/h), slow motion (4-9 km/h), and heavy precipitation (up to 770 mm). Because of an extensive footprint, Wilma caused more vegetation damage (29%) than Dean (20%), Emily (7%), and Stan (2%). All TCs caused a Chl-aincrease associated to submarine discharge and upwelling off the peninsula coastlines. Disaster risk along the coast underscores negative economic impacts and positive ecological benefits at the regional scale

High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-linear input-output dependency. We first show that, with particular choices of kernel functions, non-redundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments with thousands of features.Comment: 18 page

Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $k$ shapelets, which are used to produce a transformed dataset, where each of the $k$ features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Transcription of toll-like receptors 2, 3, 4 and 9, FoxP3 and Th17 cytokines in a susceptible experimental model of canine Leishmania infantum infection

Canine leishmaniosis (CanL) due to Leishmania infantum is a chronic zoonotic systemic disease resulting from complex interactions between protozoa and the canine immune system. Toll-like receptors (TLRs) are essential components of the innate immune system and facilitate the early detection of many infections. However, the role of TLRs in CanL remains unknown and information describing TLR transcription during infection is extremely scarce. The aim of this research project was to investigate the impact of L. infantum infection on canine TLR transcription using a susceptible model. The objectives of this study were to evaluate transcription of TLRs 2, 3, 4 and 9 by means of quantitative reverse transcription polymerase chain reaction (qRT-PCR) in skin, spleen, lymph node and liver in the presence or absence of experimental L. infantum infection in Beagle dogs. These findings were compared with clinical and serological data, parasite densities in infected tissues and transcription of IL-17, IL-22 and FoxP3 in different tissues in non-infected dogs (n = 10), and at six months (n = 24) and 15 months (n = 7) post infection. Results revealed significant down regulation of transcription with disease progression in lymph node samples for TLR3, TLR4, TLR9, IL-17, IL-22 and FoxP3. In spleen samples, significant down regulation of transcription was seen in TLR4 and IL-22 when both infected groups were compared with controls. In liver samples, down regulation of transcription was evident with disease progression for IL-22. In the skin, upregulation was seen only for TLR9 and FoxP3 in the early stages of infection. Subtle changes or down regulation in TLR transcription, Th17 cytokines and FoxP3 are indicative of the silent establishment of infection that Leishmania is renowned for. These observations provide new insights about TLR transcription, Th17 cytokines and Foxp3 in the liver, spleen, lymph node and skin in CanL and highlight possible markers of disease susceptibility in this model

The Role of RANK-Ligand Inhibition in Cancer: The Story of Denosumab

The bone is a very common site of metastasis in patients with advanced cancer. Skeletal metastases are most common in breast and prostate cancer, but virtually any advanced cancer may disseminate to the bone. On the basis of recent advances in the understanding of bone remodeling processes, denosumab, a fully human monoclonal antibody against RANK-L, has been developed. Phase III clinical trials have demonstrated that denosumab is well tolerated and effective in the treatment of bone loss and prevention of skeletal-related events in patients with bone metastases

Comparison of unidimensional and bidimensional measurements in metastatic non-small cell lung cancer

Tumour response evaluation after chemotherapy has become crucial in the development of many drugs. In contrast to the standard bidimensional WHO criteria, the recently described Response Evaluation Criteria In Solid Tumors are based on unidimensional measurements. The aim of the present study was to compare both methods in patients with metastatic non-small cell lung cancer. One hundred and sixty-four patients treated with two cisplatin-paclitaxel-based chemotherapy schedules between June 1994 and December 2000 were analysed. The measurements were reviewed by an independent panel of radiologists. Patient characteristics were: median age of 55 years (range 24–77 years) and a male to female ratio of 129 : 35. Adenocarcinoma and squamous carcinoma were the most common histologies. Vinorelbine was the third drug used in 77 patients and gemcitabine in 87. The ratio unidimensional/bidimensional was as follows: response 85 : 85; stable disease 32 : 32; progression 47 : 42 and not assessable 0 : 5. Kappa for agreement between responders was 0.951 (95% CI: 0.795–1.0) (P<0.001). Both WHO criteria and Response Evaluation Criteria In Solid Tumors give similar results in assessing tumour response in patients with non-small cell lung cancer after chemotherapy. The unidimensional measurement could replace the more complex bidimensional one