Cell death machinery makes life more robust

CED-3, a protein that is essential for programmed cell death, also has an unexpected role in the regulation of non-apoptotic genes during normal development

Spatial Structure of Ion Beams in an Expanding Plasma

We report spatially resolved perpendicular and parallel, to the magnetic field, ion velocity distribution function (IVDF) measurements in an expanding argon helicon plasma. The parallel IVDFs, obtained through laser induced fluorescence (LIF), show an ion beam with v ≈ 8000 m/s flowing downstream and confined to the center of the discharge. The ion beam is measurable for tens of centimeters along the expansion axis before the LIF signal fades, likely a result of metastable quenching of the beam ions. The parallel ion beam velocity slows in agreement with expectations for the measured parallel electric field. The perpendicular IVDFs show an ion population with a radially outward flow that increases with distance from the plasma axis. Structures aligned to the expanding magnetic field appear in the DC electric field, the electron temperature, and the plasma density in the plasma plume. These measurements demonstrate that at least two-dimensional and perhaps fully three-dimensional models are needed to accurately describe the spontaneous acceleration of ion beams in expanding plasmas

Automatic Identification of Driving Maneuver Patterns using a Robust Hidden Semi-Markov Models

There is an increase in interest to model driving maneuver patterns via the automatic unsupervised clustering of naturalistic sequential kinematic driving data. The patterns learned are often used in transportation research areas such as eco-driving, road safety, and intelligent vehicles. One such model capable of modeling these patterns is the Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM), as it is often used to estimate data segmentation, state duration, and transition probabilities. While this model is a powerful tool for automatically clustering observed sequential data, the existing HDP-HSMM estimation suffers from an inherent tendency to overestimate the number of states. This can result in poor estimation, which can potentially impact impact transportation research through incorrect inference of driving patterns. In this paper, a new robust HDP-HSMM (rHDP-HSMM) method is proposed to reduce the number of redundant states and improve the consistency of the model's estimation. Both a simulation study and a case study using naturalistic driving data are presented to demonstrate the effectiveness of the proposed rHDP-HSMM in identifying and inference of driving maneuver patterns

Methods for Measuring Risk for Type 2 Diabetes in Youth: the Oral Glucose Tolerance Test (OGTT)

Purpose of Review The oral glucose tolerance test (OGTT) is used both in clinical practice and research to assess glucose tolerance. In addition, the OGTT is utilized for surrogate measures of insulin sensitivity and the insulin response to enteral glucose and has been widely applied in the evaluation of β-cell dysfunction in obesity, prediabetes, and type 2 diabetes. Here we review the use of the OGTT and the OGTT-derived indices for measurement of risk markers for type 2 diabetes in youth. Recent Findings Advantages of using the OGTT for measures of diabetes risk include its accessibility and the incorporation of physiological contributions of the gut-pancreas axis in the measures of insulin response to glucose. Mathematical modeling expands the potential gains from the OGTT in physiology and clinical research. Disadvantages include individual differences in the rate of glucose absorption that modify insulin responses, imperfect control of the glycemic stimulus, and poor intraindividual reproducibility. Summary Available research suggests the OGTT provides valuable information about the development of impaired glycemic control and β-cell function in obese youth along the spectrum of glucose tolerance

LIN-42, the Caenorhabditis elegans PERIOD homolog, Negatively Regulates MicroRNA Transcription

During C. elegans development, microRNAs (miRNAs) function as molecular switches that define temporal gene expression and cell lineage patterns in a dosage-dependent manner. It is critical, therefore, that the expression of miRNAs be tightly regulated so that target mRNA expression is properly controlled. The molecular mechanisms that function to optimize or control miRNA levels during development are unknown. Here we find that mutations in lin-42, the C. elegans homolog of the circadian-related period gene, suppress multiple dosage-dependent miRNA phenotypes including those involved in developmental timing and neuronal cell fate determination. Analysis of mature miRNA levels in lin-42 mutants indicates that lin-42 functions to attenuate miRNA expression. Through the analysis of transcriptional reporters, we show that the upstream cis-acting regulatory regions of several miRNA genes are sufficient to promote highly dynamic transcription that is coupled to the molting cycles of post-embryonic development. Immunoprecipitation of LIN-42 complexes indicates that LIN-42 binds the putative cis-regulatory regions of both non-coding and protein-coding genes and likely plays a role in regulating their transcription. Consistent with this hypothesis, analysis of miRNA transcriptional reporters in lin-42 mutants indicates that lin-42 regulates miRNA transcription. Surprisingly, strong loss-of-function mutations in lin-42 do not abolish the oscillatory expression patterns of lin-4 and let-7 transcription but lead to increased expression of these genes. We propose that lin-42 functions to negatively regulate the transcriptional output of multiple miRNAs and mRNAs and therefore coordinates the expression levels of genes that dictate temporal cell fate with other regulatory programs that promote rhythmic gene expression

White Gaussian Noise Based Capacity Estimate and Characterization of Fiber-Optic Links

We use white Gaussian noise as a test signal for single-mode and multimode transmission links and estimate the link capacity based on a calculation of mutual information. We also extract the complex amplitude channel estimations and mode-dependent loss with high accuracy.Comment: submitted to The Optical Networking and Communication Conference (OFC) 201

Liquid-gas phase transition in hot asymmetric nuclear matter with density-dependent relativistic mean-field models

The liquid-gas phase transition in hot asymmetric nuclear matter is studied within density-dependent relativistic mean-field models where the density dependence is introduced according to the Brown-Rho scaling and constrained by available data at low densities and empirical properties of nuclear matter. The critical temperature of the liquid-gas phase transition is obtained to be 15.7 MeV in symmetric nuclear matter falling on the lower edge of the small experimental error bars. In hot asymmetric matter, the boundary of the phase-coexistence region is found to be sensitive to the density dependence of the symmetry energy. The critical pressure and the area of phase-coexistence region increases clearly with the softening of the symmetry energy. The critical temperature of hot asymmetric matter separating the gas phase from the LG coexistence phase is found to be higher for the softer symmetry energy.Comment: To be published in Phys. Lett.

Three Kinds of Special Relativity via Inverse Wick Rotation

Since the special relativity can be viewed as the physics in an inverse Wick rotation of 4-d Euclid space, which is at almost equal footing with the 4-d Riemann/Lobachevski space, there should be important physics in the inverse Wick rotation of 4-d Riemann/Lobachevski space. Thus, there are three kinds of special relativity in de Sitter/Minkowski/anti-de Sitter space at almost equal footing, respectively. There is an instanton tunnelling scenario in the Riemann-de Sitter case that may explain why \La be positive and link with the multiverse.Comment: 3 pages, no figures, to appear in Chin. Phys. Let

Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance