Does biofilm origin matter? Biofilm responses to non-flow period in permanent and temporary streams

1. In some regions, climate change is increasing the variability of rainfall and the frequency of extreme events such as drought. Consequently, non-flow periods have grown in length and frequency, both in temporary and in formerly permanent streams. Water abstraction for human use may further prolong these dry periods.
2. We analysed the resistance and resilience of biofilms from permanent and temporary streams to non-flow conditions. This was achieved by exposing cobbles (collected from permanent and temporary streams) with intact biofilm to 31 days of non-flow, followed by 20 days of stream flow in artificial stream channels. Biofilm resistance and resilience were assessed at a structural (algal biomass, pigment composition, and algae and cyanobacteria composition) and functional level (photosynthetic efficiency and community metabolism).
3. Algal taxa in biofilms from permanent and temporary streams differed throughout the experiment. Biofilms from permanent streams were less resistant to non-flow than those from temporary streams at structural level. Permanent stream biofilms also presented lower resilience at a structural level, but responded similarly to temporary stream biofilms at a functional level.
4. Our investigation shows how the non-flow period disturbed permanent stream biofilms, and suggests that temporary stream biofilms will have greater adaptive capacity as hydroperiod becomes shorter due to climate change.

     

Bacterial classification with convolutional neural networks based on different data reduction layers

Abstract

For high accuracy classification of DNA sequences through Convolutional Neural Networks (CNNs), it is essential to use an efficient sequence representation that can accelerate similarity comparison between DNA sequences. In addition, CNN networks can be improved by avoiding the dimensionality problem associated with multi-layer CNN features. This paper presents a new approach for classification of bacterial DNA sequences based on a custom layer. A CNN is used with Frequency Chaos Game Representation (FCGR) of DNA. The FCGR is adopted as a sequence representation method with a suitable choice of the frequency k-lengthen words occurrence in DNA sequences. The DNA sequence is mapped using FCGR that produces an image of a gene sequence. This sequence displays both local and global patterns. A pre-trained CNN is built for image classification. First, the image is converted to feature maps through convolutional layers. This is sometimes followed by a down-sampling operation that reduces the spatial size of the feature map and removes redundant spatial information using the pooling layers. The Random Projection (RP) with an activation function, which carries data with a decent variety with some randomness, is suggested instead of the pooling layers. The feature reduction is achieved while keeping the high accuracy for classifying bacteria into taxonomic levels. The simulation results show that the proposed CNN based on RP has a trade-off between accuracy score and processing time.     

A generalized threshold mixed model for analyzing nonnormal nonlinear time series, with application to plague in Kazakhstan

We introduce the generalized threshold mixed model for piecewise-linearstochastic regression with possibly nonnormal time-series data.It is assumed that the conditional probability distributionof the response variable belongs to the exponential family,and the conditional mean response is linked to some piecewise-linearstochastic regression function. We study the particular casewhere the response variable equals zero in the lower regime.Some large-sample properties of a likelihood-based estimationscheme are derived. Our approach is motivated by the need formodelling nonlinearity in serially correlated epizootic events.Data coming from monitoring conducted in a natural plague focusin Kazakhstan are used to illustrate this model by obtainingbiologically meaningful conclusions regarding the thresholdrelationship between prevalence of plague and some covariatesincluding past abundance of great gerbils and other climaticvariables.