Temporal classification of short time series data

MOTIVATION

Within the frame of their genetic capacity, organisms are able to modify their molecular state to cope with changing environmental conditions or induced genetic disposition. As high throughput methods are becoming increasingly affordable, time series analysis techniques are applied frequently to study the complex dynamic interplay between genes, proteins, and metabolites at the physiological and molecular level. Common analysis approaches fail to simultaneously include (i) information about the replicate variance and (ii) the limited number of responses/shapes that a biological system is typically able to take.

RESULTS

We present a novel approach to model and classify short time series signals, conceptually based on a classical time series analysis, where the dependency of the consecutive time points is exploited. Constrained spline regression with automated model selection separates between noise and signal under the assumption that highly frequent changes are less likely to occur, simultaneously preserving information about the detected variance. This enables a more precise representation of the measured information and improves temporal classification in order to identify biologically interpretable correlations among the data.

AVAILABILITY AND IMPLEMENTATION

An open source F# implementation of the presented method and documentation of its usage is freely available in the TempClass repository, https://github.com/CSBiology/TempClass  [58].

Virulome analysis of Escherichia coli ST117 from bovine sources identifies similarities and differences with strains isolated from other food animals

Escherichia coli ST117 is a pandemic extraintestinal pathogenic E. coli (ExPEC) causing significant morbidity globally. Poultry are a known reservoir of this pathogen, but the characteristics of ST117 strains from other animal sources have not been adequately investigated. Here we characterize the genomes of 36 ST117 strains recovered primarily from preweaned dairy calves, but also from older postweaned calves and lactating cows, in the context of other bovine-associated strains and strains from poultry, swine, and humans. Results of this study demonstrate that bovine-associated ST117 genomes encode virulence factors (VFs) known to be involved in extraintestinal infections, but also occasionally encode the Shiga toxin, a virulence factor (VF) involved in severe gastrointestinal infections and more frequently identified in E. coli from ruminants than other animals. Bovine-associated ST117 genomes were also more likely to encode afa-VIII (adhesins), pap (P-fimbriae), cdt (cytolethal distending toxin), and stx (Shiga toxins) than were poultry and swine-associated genomes. All of the ST117 genomes were grouped into seven virulence clusters, with bovine-associated genomes grouping into Clusters 1, 2, 4, 5, but not 3, 6, or 7. Major differences in the presence of virulence factors between clusters were observed as well. Antimicrobial resistance genes were detected in 112 of 122 (91%) bovine-associated genomes, with 103 of these being multidrug-resistant (MDR). Inclusion of genomes that differed from ST117 by one multi-locus sequence type (MLST) allele identified 31 STs, four of these among the bovine-associated genomes. These non-ST117 genomes clustered with the ST117 genomes suggesting that they may cause similar disease as ST117. Results of this study identify cattle as a reservoir of ST117 strains, some of which are highly similar to those isolated from other food animals and some of which have unique bovine-specific characteristics.

Melatonin administration alleviates 2,2,4,4-tetra-brominated diphenyl ether (PBDE-47)-induced necroptosis and secretion of inflammatory factors via miR-140-5p/TLR4/NF-κB axis in fish kidney cells

2,2,4,4-tetra-brominated diphenyl ether (PBDE-47)-the dominant homologue of polybrominated diphenyl ethers-is a toxic environmental pollutant in the aquatic environment that continuously exists and bioaccumulates in the aquatic food chain. In experimental disease models, melatonin (MEL) has been reported to attenuate necroptosis and inflammatory responses. To further explore the mechanism underlying PBDE-47 toxicity and the mitigative impact of MEL detoxification, in this study, fish kidney cell models of PBDE-47 poisoning and/or MEL treatment were developed. The Ctenopharyngodon idellus kidney (CIK) cell line was treated with PBDE-47 (100 μM) and/or MEL (60 μM) for 24 h. Experimental data suggest that PBDE-47 exposure resulted in the enhancement of cytoplasmic Ca²⁺ concentration, induction of calcium dysmetabolism, decrease in the miR-140-5p miRNA level, upregulation of Toll-like Receptor 4 (TLR4) and nuclear factor-kappaB (NF-κB), triggering of receptor interacting serine/threonine kinase-induced necroptosis, and NF-κB pathway mediated secretion of inflammatory factors in CIK cells. PBDE-47-induced CIK cell damage could be mitigated by MEL through the regulation of calcium channels and the restoration of disorders of the miR-140-5p/TLR4/NF-κB axis. Overall, MEL relieved PBDE-47-induced necroptosis and the secretion of inflammatory factors through the miR-140-5p/TLR4/NF-κB axis. These findings enrich the current understanding of the toxicological molecular mechanisms of the PBDE-47 as well as the detoxification mechanisms of the MEL.

Optimal Cluster Head Positioning Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs) are increasingly gaining popularity, especially with the advent of many artificial intelligence (AI) driven applications and expert systems. Such applications require specific relevant sensors’ data to be stored, processed, analyzed, and input to the expert systems. Obviously, sensor nodes (SNs) have limited energy and computation capabilities and are normally deployed remotely over an area of interest (AoI). Therefore, proposing efficient protocols for sensing and sending data is paramount to WSNs operation. Nodes’ clustering is a widely used technique in WSNs, where the sensor nodes are grouped into clusters. Each cluster has a cluster head (CH) that is used to gather captured data of sensor nodes and forward it to a remote sink node for further processing and decision-making. In this paper, an optimization algorithm for adjusting the CH location with respect to the nodes within the cluster is proposed. This algorithm aims at finding the optimal CH location that minimizes the total sum of the nodes’ path-loss incurred within the intra-cluster communication links between the sensor nodes and the CH. Once the optimal CH is identified, the CH moves to the optimal location. This suggestion of CH re-positioning is frequently repeated for new geometric position. Excitingly, the algorithm is extended to consider the inter-cluster communication between CH nodes belonging to different clusters and distributed over a spiral trajectory. These CH nodes form a multi-hop communication link that convey the captured data of the clusters’ nodes to the sink destination node. The performance of the proposed CH positioning algorithm for the single and multi-clusters has been evaluated and compared with other related studies. The results showed the effectiveness of the proposed CH positioning algorithm.