Characterization of DNA unwinding properties of three N-terminal fragments of RecQ5β helicase

RecQ5β is one member of the human RecQ family helicases that belong to superfamily 2 (SF2) and are critical for the maintenance of genomic stability. Here, the DNA unwinding kinetics of three N-terminal fragments of RecQ5β helicase, RecQ5β1-467, RecQ5β1-567 and RecQ5β1-662, were studied with stopped-flow method based on fluorescence resonance energy transfer (FRET). Under single-turnover kinetic conditions, we found that both the unwinding amplitude and rate increased with the increase of the 3’-tail length of the DNA substrate for each fragment. The maximum amplitudes were 73.5, 57.6 and 35.5% for RecQ5β1-467, RecQ5β1-567 and RecQ5β1-662, respectively. Obviously, the unwinding amplitude decreased with the increase of the fragment length. For each RecQ5β fragment, when the 3’-tail length of the DNA substrates was short, essentially only one slow unwinding process occurred. When the 3’-tail length was increased, the unwinding amplitude of the fast unwinding process increased obviously; that is, the RecQ5β-catalyzed DNA unwinding depended on the 3’-tail length of the DNA substrate. It indicates that RecQ5β molecules are cooperative in DNA unwinding. This is an interesting new feature for a SF2 helicase.Key words: RecQ5β helicase, stopped-flow technique, fluorescence resonance energy transfer (FRET), DNA unwinding kinetics

Development and validation study of a non-alcoholic fatty liver disease risk scoring model among adults in China

Background: Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases in China. It is usually asymptomatic and transabdominal ultrasound (USS) is the usual means for diagnosis, but it may not be feasible to have USS screening of the whole population. Objective: To develop a risk scoring model for predicting the presence of NAFLD using parameters that can be easily obtain in clinical settings. Methods: A retrospective study on the data of 672 adults who had general health check including a transabdominal ultrasound. Fractional polynomial and multivariable logistic regressions of sociodemographic and biochemical variables on NAFLD were used to identify the predictors. A risk score was assigned to each predictor using the scaled standardized β-coefficient to create a risk prediction algorithm. The accuracy for NAFLD detection by each cut-off score in the risk algorithm was evaluated. Results: The prevalence of NAFLD in our study population was 33.0% (222/672). Six significant factors were selected in the final prediction model. The areas under the curve (AUC) was 0.82 (95% CI: 0.78–0.85). The optimal cut-off score, based on the ROC was 35, with a sensitivity of 76.58% (95% CI: 70.44–81.98%) and specificity of 74.89% (95% CI: 70.62–78.83%). Conclusion: A NAFLD risk scoring model can be used to identify asymptomatic Chinese people who are at risk of NAFLD for further USS investigation.published_or_final_versio

Noise auto-correlation spectroscopy with coherent Raman scattering

Ultrafast lasers have become one of the most powerful tools in coherent nonlinear optical spectroscopy. Short pulses enable direct observation of fast molecular dynamics, whereas broad spectral bandwidth offers ways of controlling nonlinear optical processes by means of quantum interferences. Special care is usually taken to preserve the coherence of laser pulses as it determines the accuracy of a spectroscopic measurement. Here we present a new approach to coherent Raman spectroscopy based on deliberately introduced noise, which increases the spectral resolution, robustness and efficiency. We probe laser induced molecular vibrations using a broadband laser pulse with intentionally randomized amplitude and phase. The vibrational resonances result in and are identified through the appearance of intensity correlations in the noisy spectrum of coherently scattered photons. Spectral resolution is neither limited by the pulse bandwidth, nor sensitive to the quality of the temporal and spectral profile of the pulses. This is particularly attractive for the applications in microscopy, biological imaging and remote sensing, where dispersion and scattering properties of the medium often undermine the applicability of ultrafast lasers. The proposed method combines the efficiency and resolution of a coherent process with the robustness of incoherent light. As we demonstrate here, it can be implemented by simply destroying the coherence of a laser pulse, and without any elaborate temporal scanning or spectral shaping commonly required by the frequency-resolved spectroscopic methods with ultrashort pulses.Comment: To appear in Nature Physic

Lineage Divergence and Historical Gene Flow in the Chinese Horseshoe Bat (Rhinolophus sinicus)

PMCID: PMC3581519This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies