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We recently developed an in vitro testing system, namely, ESCAR (Emulator of SubCutaneous Absorption and Release). The objective of this work was to investigate drug release behaviors of unmilled and milled suspensions in ESCAR. A mass transport-based model was developed to describe the multi-step drug release process, including drug dissolution, particle settling, drug distribution/partition, and drug permeation through the membrane(s). To address the particle settling effect, a correction factor was included in the model and its value was obtained by data fitting. It was found that, for both suspensions, (i) the experimental data of various dose/formulation combinations could be fit by the developed model; (ii) the dose effect on drug release was offset by the particle settling effect. This model may help to reduce experimental efforts and facilitate subcutaneous suspension formulation development using ESCAR.

Dueling bandits are widely used to model preferential feedback that is prevalent in machine learning applications such as recommendation systems and ranking. In this paper, we study the Borda regret minimization problem for dueling bandits, which aims to identify the item with the highest Borda score while minimizing the cumulative regret. We propose a new and highly expressive generalized linear dueling bandits model, which covers many existing models. Surprisingly, the Borda regret minimization problem turns out to be difficult, as we prove a regret lower bound of order $\Omega(d^{2/3} T^{2/3})$, where $d$ is the dimension of contextual vectors and $T$ is the time horizon. To attain the lower bound, we propose an explore-then-commit type algorithm, which has a nearly matching regret upper bound $\tilde{O}(d^{2/3} T^{2/3})$. When the number of items/arms $K$ is small, our algorithm can achieve a smaller regret $\tilde{O}( (d \log K)^{1/3} T^{2/3})$ with proper choices of hyperparameters. We also conduct empirical experiments on both synthetic data and a simulated real-world environment, which corroborate our theoretical analysis., Comment: 28 pages, 3 figure

Attention can be focused on specific locations in our visual field (space-based attention), but also spreads along objects (object-based attention). However, object-based effects are prone to large individual differences and seem not as prevalent as previously assumed. In a previous study, we investigated individual differences in the temporal dynamics of attentional selection. Our results showed that individual differences in the temporal aspects of attentional selection are not the underlying cause for the low-prevalence of object-based effects. We further assessed the rhythmic sampling of visual space underlying object- and space-based effects. Our data suggest that the rhythmic sampling depends on the target frequency and is particularly enhanced for targets with a low frequency (invalid targets: 10% validity for invalid same and different targets) and robust for those with a high frequency (valid trials: 80% cue validity). In three experiments, we further assess the effects of target frequency on attentional selection and the rhythmic sampling of visual space. Experiment 1 is a direct replication of our previous study. However, we will collect data for all three experiments online. In Experiment 2, we change the target frequency to be 50% (50% valid, 25% invalid same- and different-object target locations). In Experiment 3, target frequency was balanced across valid, invalid same- and different-object target locations.

Additional file 1: Figure S1. Summary of meteorological data in Wuhan from July 7, 08:00 to July 9, 08:00, 2017. Figure S2. Rhythmic occupancy of RNA polymerase II (RNAPII). Figure S3. Whole-transcriptome RNA-seq analysis of rhythmic gene expression in paddy field rice leaves. Figure S4. Reproducibility and loop span of RNAPII-mediated ChIA-PET data. Figure S5. Global patterns of chromatin interactions in rice. Figure S6. Characterization of RNAPII-mediated chromatin interactions. Figure S7. Percentage of overlap or dynamic anchors throughout the day and the correlation between peak intensity and transcription. Figure S8. Properties of RNAPII-organized chromatin spatial clusters (CSCs). Figure S9. Interaction frequencies around AM- and PM- specific node genes in the RNAPII-arranged networks. Figure S10. Features of node genes in the RNAPII-mediated networks. Figure S11. Core circadian clock genes-associated chromatin interaction networks.

Additional file 2: Table S1. Sensitivity analysis. Table S2. The distribution of the score of the Self-Rating of Biological Rhythm Disorder for Adolescents (SBRDA). Table S3. Sample characteristics stratified by sleep rhythm disorder (N = 3693). Table S4. Sample characteristics stratified by activity rhythm disorder (N = 3693). Table S5. Sample characteristics stratified by eating habits rhythm disorder (N = 3693). Table S6. Sample characteristics stratified by digital media use (N = 3693). Table S7. Partial coefficients between self-rating biological rhythm disorders and Depression and anxiety symptoms. Table S8. Adjusted associations between self-rating biological rhythm disorders and depression symptoms. Table S9. Adjusted associations between self-rating biological rhythm disorders and anxiety symptoms.

Additional file 13. Review history.

In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets. To reduce the error in labeling and solve the problem of normal data distribution, we propose a new aesthetic mixed dataset with classification and regression called AMD-CR, and we train a meta reweighting network to reweight the loss of training data differently. In addition, we provide a training strategy acccording to different stages, based on pseudo labels of the binary classification task, and then we use it for aesthetic training acccording to different stages in classification and regression tasks. In the construction of the network structure, we construct an aesthetic adaptive block (AAB) structure that can adapt to any size of the input images. Besides, we also use the efficient channel attention (ECA) to strengthen the feature extracting ability of each task. The experimental result shows that our method improves 0.1112 compared with the conventional methods in SROCC. The method can also help to find best aesthetic path planning for unmanned aerial vehicles (UAV) and vehicles., Comment: 10 pages, 8 figures

Lead halide perovskites, which can be grown by solution process, are very suitable for various optoelectronic applications, such as in solar cells, light-emitting diodes, lasers and photo-detectors, due to their outstanding electrical and optical properties. However, lead halide perovskites are unstable under continuous light illumination, which severely degrades the performance and lifetime of optoelectronic devices based on them. Therefore, the photo-stability of perovskites and the mechanisms have received more and more attention. In this article we review the main phenomena in perovskites, induced by continuous light illumination, namely photo curing, photo dissociation, photo-induced phase separation, and photo-induced phase transition. The proposed mechanisms for the instability from the perspectives of defect states, ion migration, thermodynamics and chemical bonds, respectively, are also introduced. Finally, the complexity presented in the studying of photo-stability, and the issues should be addressed in the future are also briefly discussed.

By using Hakamada-Akasofu-Fry (HAF) solar wind model, we simulated 48 solar flare events during Carrington Rotation 1981 to 1985 and compared the simulated shock arrival time with observations by satellites at 1 AU. It is found that the initial solar wind shock speed deduced from the metric Type II radio burst observations plays an important role in the shock arrival time prediction. The match between predicted and observed shock arrival times (SATs) was considerably improved by iteratively adjusting the initial shock speed. We obtained the adjustment factors as a function of the initial shock speed for different hemispheres by statistical analysis and applied them to adjust the initial shock speed driven by the solar flares which occurred in the following five Carrington rotations (1986 similar to 1990), the mean absolute error of the shock arrival time was reduced from 16 (15) hours to 12 (11) hours for the flare occurred on the western (eastern) hemisphere. This indicates that there can be significant improvements for the shock arrival time prediction by adjusting the initial shock speed using the statistical functions.

This paper firstly introduces the theory of Hough transform and the principles of stepped frequency radar signal processing, then imports the Hough transform method into the stepped frequency radar range profile detection based on its characteristic in the sample-to-IFFT two dimension plane. The corresponding criterion for determining the threshold is proposed. The performance of target detection based on Hough transform is analyzed through Monte-Carlo simulation runs. Finally, the optimal detection strategy is discussed.

Researches on target detection are mainly focus on the amplitude characteristics nowadays. Sometimes the only use of amplitude characteristics is not enough to describe the signal, so that the target detection performance is usually poor. In this essay a method to make use of the information “hidden” in the signal phase is put forward, the theory is based on polynomial fitting of the signal phase train structure. At last, the method is validated using actual data.

Because of the unique characteristics of Single-Chip Microcomputer, some students find it is very hard to learn it well at the first sight. Especially the materials and books used nowadays are often focusing too much on the structure, system and function of the Micro-Controller Unit (MCU for short). However, there exists an extremely shortage on the experiment content and technique. Some suggestions on the MCU virtual laboratory are put forward in this essay, trying to solve the problems on the experiment modernization and innovation.

The recovery can be achieved from the undersampling signal in compressed sensing theory relying on the sparsity and incoherent characteristics of the signal. A data compression algorithm is advanced in this article, based on compressed sensing and wavelet transform. Firstly the framework of the compressed sensing theory is introduced, and then a one-dimension and a two-dimension wavelet transform matrixes are constructed respectively, which leads two compressed algorithms based on modulus and original data separately. At last, the compression characteristics are simulated and compared using one-dimension signal and two-dimension images separately, at the same time, the validity is proved by those results.