A Bayesian adaptive marker‐stratified design for molecularly targeted agents with customized hierarchical modeling

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker‐stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta‐binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a “customized” equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs

DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for “pipeline” data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for “pipeline” data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest

Resistance-gene-directed discovery of a natural-product herbicide with a new mode of action.

Bioactive natural products have evolved to inhibit specific cellular targets and have served as lead molecules for health and agricultural applications for the past century1-3. The post-genomics era has brought a renaissance in the discovery of natural products using synthetic-biology tools4-6. However, compared to traditional bioactivity-guided approaches, genome mining of natural products with specific and potent biological activities remains challenging4. Here we present the discovery and validation of a potent herbicide that targets a critical metabolic enzyme that is required for plant survival. Our approach is based on the co-clustering of a self-resistance gene in the natural-product biosynthesis gene cluster7-9, which provides insight into the potential biological activity of the encoded compound. We targeted dihydroxy-acid dehydratase in the branched-chain amino acid biosynthetic pathway in plants; the last step in this pathway is often targeted for herbicide development10. We show that the fungal sesquiterpenoid aspterric acid, which was discovered using the method described above, is a sub-micromolar inhibitor of dihydroxy-acid dehydratase that is effective as a herbicide in spray applications. The self-resistance gene astD was validated to be insensitive to aspterric acid and was deployed as a transgene in the establishment of plants that are resistant to aspterric acid. This herbicide-resistance gene combination complements the urgent ongoing efforts to overcome weed resistance11. Our discovery demonstrates the potential of using a resistance-gene-directed approach in the discovery of bioactive natural products

Feature Weaken: Vicinal Data Augmentation for Classification

Deep learning usually relies on training large-scale data samples to achieve better performance. However, over-fitting based on training data always remains a problem. Scholars have proposed various strategies, such as feature dropping and feature mixing, to improve the generalization continuously. For the same purpose, we subversively propose a novel training method, Feature Weaken, which can be regarded as a data augmentation method. Feature Weaken constructs the vicinal data distribution with the same cosine similarity for model training by weakening features of the original samples. In especially, Feature Weaken changes the spatial distribution of samples, adjusts sample boundaries, and reduces the gradient optimization value of back-propagation. This work can not only improve the classification performance and generalization of the model, but also stabilize the model training and accelerate the model convergence. We conduct extensive experiments on classical deep convolution neural models with five common image classification datasets and the Bert model with four common text classification datasets. Compared with the classical models or the generalization improvement methods, such as Dropout, Mixup, Cutout, and CutMix, Feature Weaken shows good compatibility and performance. We also use adversarial samples to perform the robustness experiments, and the results show that Feature Weaken is effective in improving the robustness of the model.Comment: 9 pages,6 figure

Model and Algorithm for Container Allocation Problem with Random Freight Demands in Synchromodal Transportation

This paper aims to investigate container allocation problem with random freight demands in synchromodal transportation network from container carriers’ perspective. Firstly, the problem is formulated as a stochastic integer programming model where the overall objective is to determine a container capacity allocation plan at operational level, so that the expected total transportation profit is maximized. Furthermore, by integrating simulated annealing with genetic algorithm, a problem-oriented hybrid algorithm with a novel gene encode method is designed to solve the optimization model. Some numerical experiments are carried out to demonstrate the effectiveness and efficiency of the proposed model and algorithm

Direct observation of significant hot carrier cooling suppression in a two-dimensional silicon phononic crystal

Finding hot carrier cooling suppression in new material structures is fundamentally important for developing promising technological applications. These phenomenona have not been reported for crystalline silicon phononic crystals. Herein, we experimentally design two-dimensional (2D) silicon samples consisting of airy hole arrays in a crystalline silicon matrix. For reference, the determined hot carrier cooling times were 0.45 ps and 0.37 ps, respectively, at probe wavelengths of 1080 nm and 1100 nm. Surprisingly, when the 2D structured silicon possessed the properties of a phononic crystal, significant suppression of hot carrier cooling was observed. In these cases, the observed hot carrier cooling times were as long as 15.9 ps and 10.7 ps at probe wavelengths of 1080 nm and 1100 nm, respectively, indicating prolongation by orders of magnitude. This remarkable enhancement was also observed with other probe wavelengths. The present work presents experimental evidence for hot carrier cooling suppression in 2D silicon phononic crystals and opens opportunities for promising applications

The association of depression status with menopause symptoms among rural midlife women in China

Objective: This study aims to evaluate the association of depression with menopausal status and some menopause symptoms (vasomotor symptoms and poor sleep).Methods: A total of 743 participants aged 40-60 years were recruited. Depression status was evaluated by using Self-Rating Depression Scale (SDS). Sleep quality and vasomotor symptoms were evaluated by specific symptoms questionnaire.Results: The prevalence of depression among participants was 11.4%. Depression was found more likely to occur in participants with poor sleep (OR, 6.02; 95%CI, 3.61, 10.03) or with vasomotor symptoms (VMS) (OR, 2.03; 95%CI, 1.20, 3.44) after controlling for age, education level, marital status, menopause status, monthly family income and chronic diseases. Menopause status was not associated with depression. Stratification analysis showed a significant association between poor sleep and depression across different menopause stages, while VMS were associated with depression only in premenopausal status.Conclusion: The majority of Chinese rural midlife women do not experience depression. The relationship between depression, VMS and sleep disturbances tends to change with menopausal status in Chinese rural midlife women.Keywords: depression, poor sleep, vasomotor symptoms, menopause, rural wome

Investigating word length effects in Chinese reading

A word’s length in English is fundamental in determining whether readers fixate it, and how long they spend processing it during reading. Chinese is unspaced and most words are two characters long: Is word length an important cue to eye guidance in Chinese reading? Eye movements were recorded as participants read sentences containing a one-, two-, or three-character word matched for frequency. Results showed that longer words took longer to process (primarily driven by refixations). Furthermore, skips were fewer, incoming saccades longer and landing positions further to the right of long than short words. Additional analyses of a three-character region (matched stroke number) showed an incremental processing cost when character(s) belonged to different, rather than the same, word. These results demonstrate that word length affects both lexical identification and saccade target selection in Chinese reading