An evaluation of new parsimony-based versus parametric inference methods in biogeography: a case study using the globally distributed plant family Sapindaceae

Aim  Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony-based approaches. Here, we compare a parametric method, dispersal-extinction-cladogenesis (DEC), against a parsimony-based method, dispersal-vicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes-DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study.Location  World-wide.Methods  Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes-DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years.Results  Despite differences in the underlying biogeographical model, Bayes-DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events - which in Bayes-DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node.Main conclusions  By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes-DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide-spanning spatial and temporal model proposed here could prove useful for testing large-scale biogeographical patterns in plants.

Early detection of microcirculatory perfusion changes with a high resolution, real time laser Doppler imaging camera--frostbite case study.

A 41-year-old male presented with severe frostbite that was monitored clinically and with a new laser Doppler imaging (LDI) camera that records arbitrary microcirculatory perfusion units (1-256 arbitrary perfusion units (APU's)). LDI monitoring detected perfusion differences in hand and foot not seen visually. On day 4-5 after injury, LDI showed that while fingers did not experience any significant perfusion change (average of 31±25 APUs on day 5), the patient's left big toe did (from 17±29 APUs day 4 to 103±55 APUs day 5). These changes in regional perfusion were not detectable by visual examination. On day 53 postinjury, all fingers with reduced perfusion by LDI were amputated, while the toe could be salvaged. This case clearly demonstrates that insufficient microcirculatory perfusion can be identified using LDI in ways which visual examination alone does not permit, allowing prognosis of clinical outcomes. Such information may also be used to develop improved treatment approaches.

Emotional processing in a ten-session general psychiatric treatment for borderline personality disorder: a case study.

This study examines the effects of a borderline-specific treatment, called general psychiatric management, on emotional change, outcome and therapeutic alliance of an outpatient presenting with borderline personality disorder. Based on the sequential model of emotional processing, emotional states were assessed in a 10-session setting. The case showed an increase in expressions of distress and no change in therapeutic alliance and tended towards general deterioration. Results suggest emotional processing may play a lesser role in general psychiatric management in early phase treatment than previously hypothezised. Copyright © 2015 John Wiley & Sons, Ltd.

Case study of intracerebral plasmacytoma as an initial presentation of multiple myeloma.

Cerebral involvement is an uncommon complication of multiple myeloma. We report on a 64-year-old man hospitalized for a partial seizure. MRI showed two intracerebral lesions, which proved to be plasmacytomas. After complete staging, we retained the diagnosis of immunoglobulin G lambda-type multiple myeloma with CNS involvement. Cytogenetic analysis of plasma cells detected a deletion in the p53 gene at 17p13.1. Despite cranial radiotherapy and systemic chemotherapy, the patient's disease progressed rapidly and he died five months after diagnosis. What makes this case unusual is that overt multiple myeloma had been absent before cerebral involvement was discovered. It confirms the extremely poor prognosis of patients with CNS myeloma even in the presence of aggressive treatment. Cytogenetic abnormalities could be a marker of chromosomal and genetic instability, conferring to multiple myeloma a more aggressive profile.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Positive effects of an angiotensin II type 1 receptor antagonist in Camurati-Engelmann disease: a single case observation.

Camurati-Engelmann disease is characterized by hyperostosis of the long bones and the skull, muscle atrophy, severe limb pain, and progressive joint contractures in some patients. It is caused by heterozygous mutations in the transforming growth factor β1 (TGFβ1) believed to result in improper folding of the latency-associated peptide domain of TGFβ1 and thus in increased or deregulated bioactivity. Losartan, an angiotensin II type 1 receptor antagonist, has been found to downregulate the expression of TGFβ type 1 and 2 receptors. Clinical trials with losartan have shown a benefit in Marfan syndrome, while trials are underway for Duchenne muscular dystrophy and other myopathies associated with TGFβ1 signaling. We hypothesized that due to its anti-TGFβ1 activity, losartan might be beneficial in Camurati-Engelmann disease. This report concerns a boy who presented at age 13 years with severe limb pain and difficulty in walking. Clinical and radiographic evaluation results were compatible with Camurati-Engelmann disease and the diagnosis was confirmed by mutation analysis (c.652C > T [p.Arg218Cys]). The boy underwent an experimental treatment with losartan at a dosage of 50 mg/day, orally. During the treatment period of 18 months, the intensity and frequency of limb pain decreased significantly (as shown by a pain diary), and muscle strength improved, allowing the boy to resume walking and climbing stairs. No obvious side effects were observed. We cautiously conclude that TGFβ1 inhibition with losartan deserves further evaluation in the clinical management of Camurati-Engelmann disease.

Geostatistical determination of heavy-metal distribution in a contaminated site - A case study

A two stage sampling strategy is necessary in order to optimize the study of distribution of pollution in soils and groundwater. First, detailed sampling from a limited area coupled with statistical analysis of the data are used to determine the microvariability of the parameter(s). The results from this detailed analysis are then used to calculate the optimal spacing between samples for the larger scale study. This two stage sampling strategy can result in significant financial savings during subsequent soil or groundwater remediation. This combined sampling and statistical analysis approach is illustrated with an example from a heavy metal contaminated site.