Changes to the Fossil Record of Insects through Fifteen Years of Discovery

The first and last occurrences of hexapod families in the fossil record are compiled from publications up to end-2009. The major features of these data are compared with those of previous datasets (1993 and 1994). About a third of families (>400) are new to the fossil record since 1994, over half of the earlier, existing families have experienced changes in their known stratigraphic range and only about ten percent have unchanged ranges. Despite these significant additions to knowledge, the broad pattern of described richness through time remains similar, with described richness increasing steadily through geological history and a shift in dominant taxa, from Palaeoptera and Polyneoptera to Paraneoptera and Holometabola, after the Palaeozoic. However, after detrending, described richness is not well correlated with the earlier datasets, indicating significant changes in shorter-term patterns. There is reduced Palaeozoic richness, peaking at a different time, and a less pronounced Permian decline. A pronounced Triassic peak and decline is shown, and the plateau from the mid Early Cretaceous to the end of the period remains, albeit at substantially higher richness compared to earlier datasets. Origination and extinction rates are broadly similar to before, with a broad decline in both through time but episodic peaks, including end-Permian turnover. Origination more consistently exceeds extinction compared to previous datasets and exceptions are mainly in the Palaeozoic. These changes suggest that some inferences about causal mechanisms in insect macroevolution are likely to differ as well

Automated non-contact detection of central apneas using video

Central apneas occurring in the aftermath of epileptic seizures may lead to sudden death. Contact-sensors currently used to detect apneas are not always suitable or tolerated. We developed a robust automated non-contact algorithm for real-time detection of central apneas using video cameras. One video recording with simulated apneas and nine with real-life apneas associated with epileptic seizures, each recorded from 3 to 4 angles, were used to develop the algorithm. Videos were preprocessed using optical flow, from which translation, dilatation and shear rates were extracted. Presence of breathing motions was quantified in the time-frequency spectrum by calculating the relative power in the respiratory range (0.1–1 Hz). Sigmoid modulation was calculated over different scales to quantify sigmoid-like drops in respiratory range power. Each sigmoid modulation maximum constitutes a possible apnea event. Two event features were calculated to enable distinction between apnea events and movements: modulation maximum amplitude and total spectral power modulation at the time of the event. An ensemble support vector machine was trained to classify events using a bagging procedure and validated in a leave-one-subject-out cross validation procedure. All apnea episodes were detected in the signals from at least one camera angle. Integrating camera inputs capturing different angles increased overall detection sensitivity (>90%). Overall detection specificity of >99% was achieved with both individual cameras and integrated camera inputs. These results suggest that it is feasible to detect central apneas automatically in video, using this algorithm. When validated, the algorithm might be used as an online remote apnea detector for safety monitoring

Probing molecular choreography through single-molecule biochemistry

Single-molecule approaches are having a dramatic impact on views of how proteins work. The ability to observe molecular properties at the single-molecule level allows characterization of subpopulations and acquisition of detailed kinetic information that would otherwise be hidden in the averaging over an ensemble of molecules. In this Perspective, we discuss how such approaches have successfully been applied to in vitro-reconstituted systems of increasing complexity

An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models

This chapter presents an assessment of falls and everyday situations in people by sensors dataset collected in fall simulation. This evaluation was performed through the use of intelligent techniques and models based on feature selection techniques and fuzzy neural networks. Therefore, this work can be seen as an auxiliary approach of presenting a vision of knowledge extraction for the construction of actions, prevention, and training to functional that will work in areas correlated to health impacts of people who may have difficulties or injuries due to the impact suffered in a fall. The results obtained were compared with state of the art for the theme and the version of the hybrid model that acts on the most relevant dataset dimensions identifying falls obtained results that surpassed the other models submitted to the test. They were successful in extracting various information from a highly sophisticated and incredibly dimensional dataset to help professionals from various areas expand their investigations in the field of falling people