A population‐based temporal logic gate for timing and recording chemical events

Engineered bacterial sensors have potential applications in human health monitoring, environmental chemical detection, and materials biosynthesis. While such bacterial devices have long been engineered to differentiate between combinations of inputs, their potential to process signal timing and duration has been overlooked. In this work, we present a two‐input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. Our temporal logic gate design relies on unidirectional DNA recombination mediated by bacteriophage integrases to detect and encode sequences of input events. For an E. coli strain engineered to contain our temporal logic gate, we compare predictions of Markov model simulations with laboratory measurements of final population distributions for both step and pulse inputs. Although single cells were engineered to have digital outputs, stochastic noise created heterogeneous single‐cell responses that translated into analog population responses. Furthermore, when single‐cell genetic states were aggregated into population‐level distributions, these distributions contained unique information not encoded in individual cells. Thus, final differentiated sub‐populations could be used to deduce order, timing, and duration of transient chemical events.     

An innovative approach for testing bioinformatics programs using metamorphic testing

Background  

Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered.     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Gene-gene interaction filtering with ensemble of filters

BACKGROUND: Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the 'unstable' results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance. RESULTS: We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional χ2-test and odds ratio methods in terms of retaining gene-gene interactions.     

云南省米虾属一新种(十足目：匙指虾科)

报道了米虾属一新种-昆明米虾Caridina kunmingensis Wang et Liang,sp.nov.。昆明米虾与石林米虾Caridina shilinica Liang et Cai2000有相似之处,但前者额角较短,前两对步足腕节和螯短而粗,雄性第1腹肢内肢、雄附肢形态和结构均与后者不同。     

Genetic and morphologic determinants of pneumothorax in lymphangioleiomyomatosis

Lymphangioleiomyomatosis, a multisystem disease affecting women, is characterized by proliferation of abnormal smooth muscle-like cells in the lungs, leading to cystic destruction of the parenchyma and recurrent pneumothoraces. Clinical characteristics of lymphangioleiomyomatosis patients were analyzed to determine the relationship of pneumothoraces to disease progression. Patients were genotyped for polymorphisms in genes of extracellular matrix proteins collagen, elastin, and matrix metalloproteinase-1 to assess their association with pneumothoraces. Clinical data and polymorphisms in the genes for types I and III collagen, elastin, and matrix metalloproteinase-1 were compared with the prevalence of pneumothorax. Of 227 patients, 57% reported having had at least one pneumothorax. Cyst size on high-resolution computed tomography scans was associated with pneumothorax; patients with a history of pneumothorax were more likely to have larger cysts than patients who had no pneumothoraces. In patients with mild disease, those with a history of pneumothorax had a faster rate of decline in forced expiratory volume in 1 s (FEV(1); P = 0.001, adjusted for age) than those without. Genotype frequencies differed between patients with and without pneumothorax for polymorphisms in the types I and III collagen and matrix metalloproteinase-1 genes. Larger cysts may predispose lymphangioleiomyomatosis patients to pneumothorax, which, in early stages of disease, correlates with a more rapid rate of decline in FEV(1). Polymorphisms in types I and III collagen and matrix metalloproteinase-1 genes may cause differences in lung extracellular matrix that result in greater susceptibility to pneumothorax.