Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator

We study here a fixed mini-batch gradient decent (FMGD) algorithm to solve optimization problems with massive datasets. In FMGD, the whole sample is split into multiple non-overlapping partitions. Once the partitions are formed, they are then fixed throughout the rest of the algorithm. For convenience, we refer to the fixed partitions as fixed mini-batches. Then for each computation iteration, the gradients are sequentially calculated on each fixed mini-batch. Because the size of fixed mini-batches is typically much smaller than the whole sample size, it can be easily computed. This leads to much reduced computation cost for each computational iteration. It makes FMGD computationally efficient and practically more feasible. To demonstrate the theoretical properties of FMGD, we start with a linear regression model with a constant learning rate. We study its numerical convergence and statistical efficiency properties. We find that sufficiently small learning rates are necessarily required for both numerical convergence and statistical efficiency. Nevertheless, an extremely small learning rate might lead to painfully slow numerical convergence. To solve the problem, a diminishing learning rate scheduling strategy can be used. This leads to the FMGD estimator with faster numerical convergence and better statistical efficiency. Finally, the FMGD algorithms with random shuffling and a general loss function are also studied

A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer interface

BackgroundBrain computer interface (BCI) technology may provide a new way of communication for some patients with disorder of consciousness (DOC), which can directly connect the brain and external devices. However, the DOC patients’ EEG differ significantly from that of the normal person and are difficult to collected, the decoding algorithm currently only is trained based on a small amount of the patient’s own data and performs poorly.MethodsIn this study, a decoding algorithm called WD-ADSTCN based on domain adaptation is proposed to improve the DOC patients’ P300 signal detection. We used the Wasserstein distance to filter the normal population data to increase the training data. Furthermore, an adversarial approach is adopted to resolve the differences between the normal and patient data.ResultsThe results showed that in the cross-subject P300 detection of DOC patients, 7 of 11 patients achieved an average accuracy of over 70%. Furthermore, their clinical diagnosis changed and CRS-R scores improved three months after the experiment.ConclusionThese results demonstrated that the proposed method could be employed in the P300 BCI system for the DOC patients, which has important implications for the clinical diagnosis and prognosis of these patients

Oridonin nanosuspension was more effective than free oridonin on G2/M cell cycle arrest and apoptosis in the human pancreatic cancer PANC-1 cell line

Oridonin, a diterpenoid isolated from Rabdosia rubescencs, has been reported to have antitumor effects. However, low solubility has limited its clinical applications. Preparation of drugs in the form of nanosuspensions is an extensively utilized protocol. In this study, we investigated the anticancer activity of oridonin and oridonin nanosuspension on human pancreatic carcinoma PANC-1 cells. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay was performed to investigate the effect of oridonin on cell growth. Propidium iodide and Hoechst 33342 staining were used to detect morphologic changes. The percentage of apoptosis and cell cycle progression was determined by flow cytometric method staining with propidium iodide. Annexin V-fluorescein isothiocyanate (FITC)/PI staining was used to evaluate cell apoptosis by flow cytometry. Caspase-3 activity was measured by spectrophotometry. The apoptotic and cell cycle protein expression were determined by Western blot analysis. Both oridonin and oridonin nanosuspension induced apoptosis and G2/M phase cell cycle arrest, and the latter had a more significant cytotoxic effect. The ratio of Bcl-2/Bax protein expression was decreased and caspase- 3 activity was stimulated. The expression of cyclin B1 and p-cdc2 (T161) was suppressed. Our results showed that oridonin nanosuspension was more effective than free oridonin on G2/M cell cycle arrest and apoptosis in the human pancreatic cancer PANC-1 cell line

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

The raw depth image captured by indoor depth sensors usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and the limited distance range. The incomplete depth map with missing values burdens many downstream vision tasks, and a rising number of depth completion methods have been proposed to alleviate this issue. While most existing methods can generate accurate dense depth maps from sparse and uniformly sampled depth maps, they are not suitable for complementing large contiguous regions of missing depth values, which is common and critical in images captured in indoor environments. To overcome these challenges, we design a novel two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure, by adhering to the Manhattan world assumption and utilizing normal maps from RGB-D information as guidance, to regress the local dense depth values from the raw depth map. In the other branch, we propose an RGB-depth fusion CycleGAN to transfer the RGB image to the fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate the features across the two branches, and we append a confidence fusion head to fuse the two outputs of the branches for the final depth map. Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method clearly improves the depth completion performance, especially in a more realistic setting of indoor environments, with the help of our proposed pseudo depth maps in training.Comment: Haowen Wang and Zhengping Che are with equal contributions. Under review. An earlier version has been accepted by CVPR 2022 (arXiv:2203.10856

A Pooling Strategy for Detecting Carbapenem Resistance Genes by the Xpert Carba-R Test in Rectal Swab Specimens

Rapid and accurate detection of carriers of carbapenemase-producing organisms (CPO) in hospitalized patients is critical for infection control and prevention. This study aimed to evaluate a pooling strategy for the detection of carbapenem resistance genes (CRG) in multiple specimens using the Xpert Carba-R test. Two rectal swabs each were collected from 415 unique patients. One swab was tested by Carba-R on the five specimen-pooled strategy. The other swab was tested individually by culture followed by DNA sequence analysis for CRG as the reference. At the first 5:1 pooling testing, 22 of 83 pools were positive, which yielded 34 positives from individual specimens when positive pools were subsequently retested. All individual specimens in the 61 negative pools were retested as negative by Carba-R. Among the 34 Carba-R-positive samples, 30 and four were positive and negative, respectively, by culture and sequencing. The remaining 381 Carba-R-negative specimens were also negative by culture and sequencing. Overall sensitivity, specificity, positive predictive value, and negative predictive value of the 5:1 pooled screening were 100.0% (95% confidence interval [CI] = 85.9% to 100%), 99.0% (95% CI = 97.2% to 99.7%), 88.2% (95% CI = 71.6% to 96.2%), and 100.0% (95% CI = 98.8% to 100%), respectively. Using the 5:1 pooling strategy, our study completed CRG screening in 414 patients with 193 reagents with significant cost savings. The 5:1 pooling strategy using the Carba-R test showed a potential method for screening CRG from rectal swabs with good sensitivity and decreased cost

Lewis y antigen promotes the proliferation of ovarian carcinoma-derived RMG-I cells through the PI3K/Akt signaling pathway

<p>Abstract</p> <p>Background</p> <p>Lewis y antigen is difucosylated oligosaccharide and is carried by glycoconjugates at cell surface. Elevated expression of Lewis y has been found in 75% of ovarian tumor, and the high expression level is correlated to the tumor's pathological staging and prognosis. This study was to investigate the effect and the possible mechanism of Lewis y on the proliferation of human ovarian cancer cells.</p> <p>Methods</p> <p>We constructed a plasmid encoding α1,2-fucosyltransferase (α1,2-FT) gene and then transfected it into ovarian carcinoma-derived RMG-I cells with lowest Lewis y antigen expression level. Effect of Lewis y on cell proliferation was assessed after transfection. Changes in cell survival and signal transduction were evaluated after α-L-fucosidase, anti-Lewis y antibody and phosphatidylinositol 3-kinase (PI3K) inhibitor treatment.</p> <p>Results</p> <p>Our results showed that the levels of α1,2-FT gene and Lewis y increased significantly after transfection. The cell proliferation of ovarian carcinoma-derived RMG-I cells sped up as the Lewis y antigen was increased. Both of α-L-fucosidase and anti-Lewis y antibody inhibited the cell proliferation. The phosphorylation level of Akt was apparently elevated in Lewis y-overexpressing cells and the inhibitor of PI3K, LY294002, dramatically inhibited the growth of Lewis y-overexpressing cells. In addition, the phosphorylation intensity and difference in phosphorylation intensity between cells with different expression of α1,2-FT were attenuated significantly by the monoantibody to Lewis y and by the PI3K inhibitor LY294002.</p> <p>Conclusions</p> <p>Increased expression of Lewis y antigen plays an important role in promoting cell proliferation through activating PI3K/Akt signaling pathway in ovarian carcinoma-derived RMG-I cells. Inhibition of Lewis y expression may provide a new therapeutic approach for Lewis y positive ovarian cancer.</p

Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this paper a novel data-synthesized spatio-temporally convolutional encoder-decoder network method termed DST-CedNet is proposed for ESI. DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a convolutional encoder-decoder network (CedNet) to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and Epilepsy EEG dataset demonstrate that DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.Comment: 15 pages, 14 figures, and journa

The importance of the positional probability of word final (but not word initial) characters for word segmentation and identification in children and adults' natural Chinese reading

Word spacing is important in guiding eye movements during spaced alphabetic reading. Chinese is unspaced and it remains unclear as to how Chinese readers segment and identify words in reading. We conducted two parallel experiments to investigate whether the positional probabilities of the initial and the final characters of a multi-character word affected word segmentation and identification in Chinese reading. Two-character words were selected as targets. In Experiment 1, the initial character's positional probability was manipulated as being either high or low, and the final character was kept identical across the two conditions. In Experiment 2, an analogous manipulation was made for the final character of the target word. We recorded adults' and children's eye movements when they read sentences containing these words. In Experiment 1 reading times on targets did not differ in the two conditions for both children and adults, providing no evidence that a word initial character's positional probability contributes to word segmentation. In Experiment 2, adults had shorter reading times, and made fewer refixations on targets that were comprised of final characters with high relative to low positional probabilities; a similar effect was observed in children, but this effect had a slower time course. The results demonstrate that the positional probability of the final (but not the initial) character of a word influences segmentation commitments in reading. It suggests that Chinese readers identify where a currently fixated word ends, and via this commitment, by default, they identify where the subsequent word begins