Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. Besides, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with several state-of-the-art methods, including the CNN framework, on three widely used HSIs. The obtained results show that Bi-CLSTM can improve the classification performance as compared to other methods

Acupuncture and Auricular Acupressure in Relieving Menopausal Hot Flashes of Bilaterally Ovariectomized Chinese Women: A Randomized Controlled Trial

The objective of this study is to explore the effects of acupuncture and auricular acupressure in relieving menopausal hot flashes of bilaterally ovariectomized Chinese women. Between May 2006 and March 2008, 46 bilaterally ovariectomized Chinese women were randomized into an acupuncture and auricular acupressure group (n = 21) and a hormone replacement therapy (HRT) group (Tibolone, n = 25). Each patient was given a standard daily log and was required to record the frequency and severity of hot flashes and side effects of the treatment felt daily, from 1 week before the treatment started to the fourth week after the treatment ended. The serum levels of follicle stimulating hormone (FSH), LH and E2 were detected before and after the treatment. After the treatment and the follow-up, both the severity and frequency of hot flashes in the two groups were relieved significantly when compared with pre-treatment (P <  .05). There was no significant difference in the severity of hot flashes between them after treatment (P >  .05), while after the follow-up, the severity of hot flashes in the HRT group was alleviated more. After the treatment and the follow-up, the frequency of menopausal hot flashes in the HRT group was reduced more (P <  .05). After treatment, the levels of FSH decreased significantly and the levels of E2 increased significantly in both groups (P <  .05), and they changed more in the HRT group (P <  .05). Acupuncture and auricular acupressure can be used as alternative treatments to relieve menopausal hot flashes for those bilaterally ovariectomized women who are unable or unwilling to receive HRT

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for the same purpose. Distributed learning based on divide-and-conquer provides a promising way to settle the data silos, but it suffers from several challenges, including autonomy, privacy guarantees, and the necessity of collaborations. This paper focuses on developing an adaptive distributed kernel ridge regression (AdaDKRR) by taking autonomy in parameter selection, privacy in communicating non-sensitive information, and the necessity of collaborations in performance improvement into account. We provide both solid theoretical verification and comprehensive experiments for AdaDKRR to demonstrate its feasibility and effectiveness. Theoretically, we prove that under some mild conditions, AdaDKRR performs similarly to running the optimal learning algorithms on the whole data, verifying the necessity of collaborations and showing that no other distributed learning scheme can essentially beat AdaDKRR under the same conditions. Numerically, we test AdaDKRR on both toy simulations and two real-world applications to show that AdaDKRR is superior to other existing distributed learning schemes. All these results show that AdaDKRR is a feasible scheme to defend against data silos, which are highly desired in numerous application regions such as intelligent decision-making, pricing forecasting, and performance prediction for products.Comment: 46pages, 13figure

A facile molecularly imprinted polymer-based fluorometric assay for detection of histamine

Histamine is a biogenic amine naturally present in many body cells. It is also a contaminant that is mostly found in spoiled food. The consumption of foods containing high levels of histamine may lead to an allergy-like food poisoning. Analytical methods that can routinely screen histamine are thus urgently needed. In this paper, we developed a facile and cost-effective molecularly imprinted polymer (MIP)-based fluorometric assay to directly quantify histamine. Histamine-specific MIP nanoparticles (nanoMIPs) were synthesized using a modified solid-phase synthesis method. They were then immobilized in the wells of a microplate to bind the histamine in aqueous samples. After binding, o-phthaldialdehyde (OPA) was used to label the bound histamine, which converted the binding events into fluorescent signals. The obtained calibration curve of histamine showed a linear correlation ranging from 1.80 to 44.98 μM with the limit of detection of 1.80 μM. This method was successfully used to detect histamine in spiked diary milk with a recovery rate of more than 85%

A Galactomannoglucan Derived from Agaricus brasiliensis: Purification, Characterization and Macrophage Activation via MAPK and IkappaB/NFkappaB Pathways

In this study, a novel galactomannoglucan named as TJ2 was isolated from Agaricus brasiliensis with microwave extraction, macroporous resin, ion exchange resin and high resolution gel chromatography. TJ2 is composed of glucose, mannose and galactose in the ratio 99.2:0.2:0.6. Infrared spectra (IR), methylation analysis and nuclear magnetic resonance spectra indicated that TJ2 mainly contained a b-(1?3) – linked glucopyranosyl backbone. Interestingly, TJ2 significantly promoted RAW264.7 cell proliferation, and was able to activate the cells to engulf E. coli. In addition, TJ2 induced the expression of Interleukin 1b (IL-1b), Interleukin 6 (IL-6), tumor necrosis factor a (TNF-a) and cyclooxygenase-2 (Cox-2) in the cells. TJ2 also promoted the production of nitric oxide (NO) by inducing the expression of inducible nitric oxide synthase (iNOS). Moreover, TJ2 is a potent inducer in activating the mitogen-activated protein kinase (MAPK) and inhibitor of nuclear factor-kappa B (IkappaB)/nuclear factor-kappa B (NFkappaB) pathways