Active Beam Steering Enabled by Photonic-Crystal Surface-Emitting Laser.

Emitting light toward on-demand directions is important for various optoelectronic applications, such as optical communication, displaying, and ranging. However, almost all existing directional emitters are assemblies of passive optical antennae and external light sources, which are usually bulky and fragile and show unendurable loss of light power. Here we theoretically propose and experimentally demonstrate a conceptual design of a directional emitter, by using a single surface-emitting laser source itself to achieve dynamically controlled beam steering. The laser is built on photonic crystals that operate near the band edges in the continuum. By shrinking laser sizes to tens-of-wavelength, the optical modes quantize in three-dimensional momentum space, and each of them directionally radiates toward the far-field. Further utilizing the luminescence spectrum shifting effect under current injection, we consecutively select a sequence of modes into lasing action and show the laser maintaining single-mode operation with line widths at a minimum of 1.8 MHz and an emitting power of ∼10 milliwatts, and we demonstrate fast beam steering across a range of 3.2° × 4° on a time scale of 500 ns. Our work proposes a method for on-chip active beam steering for the development of automotive, industrial, and robotic applications.

Acupuncture for cervical dystonia associated with anxiety and depression: A case report.

BACKGROUND: Cervical dystonia (CD) is a type of muscle tone disorder that usually occurs in the neck muscles. Due to the intermittent or continuous involuntary contraction of the neck muscles, the head and neck are twisted and skewed and some postural abnormalities occur. Long-term abnormal posture or pain can cause negative emotions in patients, which can affect their quality of life. CASE SUMMARY: This case report included a 37-year-old woman who was diagnosed with CD associated with anxiety and depression; the accompanying symptoms were head and neck tilt of approximately 90° to the right and mental abnormality. After two courses of acupuncture treatment, the patient's head and neck can be maintained in a normal position, and the negative emotions can be relieved. CONCLUSION: This case indicates that acupuncture can effectively improve CD and the emotional state and quality of life of patients, making it an effective alternative treatment for the condition.

Improved Adaptive Multipoint Optimal Minimum Entropy Deconvolution and Application on Bearing Fault Detection in Random Impulsive Noise Environments.

Random impulsive noise is a special kind of noise, which has strong impact features and random disturbances with large amplitude, short duration, and long intervals. This type of noise often displays nonGaussianity, while common background noise obeys Gaussian distribution. Hence, random impulsive noise greatly differs from common background noise, which renders many commonly used approaches in bearing fault diagnosis inapplicable. In this work, we explore the challenge of bearing fault detection in the presence of random impulsive noise. To deal with this issue, an improved adaptive multipoint optimal minimum entropy deconvolution (IAMOMED) is introduced. In this IAMOMED, an envelope autocorrelation function is used to automatically estimate the cyclic impulse period instead of setting an approximate period range. Moreover, the target vector in the original MOMED is rearranged to enhance its practical applicability. Finally, particle swarm optimization is employed to determine the optimal filter length for selection purposes. According to these improvements, IAMOMED is more suitable for detecting bearing fault features in the case of random impulsive noise when compared to the original MOMED. The contrast experiments demonstrate that the proposed IAMOMED technique is capable of effectively identifying fault characteristics from the vibration signal with strong random impulsive noise and, in addition, it can accurately diagnose the fault types. Thus, the proposed method provides an alternative fault detection tool for rotating machinery in the presence of random impulsive noise.

Design, Synthesis and Biological Evaluation of Novel Quinoline Derivatives as Potential Anti-Proliferative Agents Against PC-3 and KG-1 Cells.

BACKGROUND: Cancer is a major public health problem worldwide, and is the leading cause of death. The discovery and development of cancer therapeutic drugs have become the most urgent measure, which significantly benefited from the usage of small molecule compounds. The quinoline core possessed a vast number of biological activities that were found to be imperative. OBJECTIVE: The aim is to design, synthesize and perform the biological evaluation of novel quinoline derivatives as potential anti-proliferative agents. METHODS: Quinoline as a privileged scaffold was adopted to introduce diverse effective nitrogen heterocycles through different linkers. The synthesized compounds were spectroscopically characterized and evaluated for their anti-proliferative activity using the CCK8 assay. The mechanism of action was investigated by flow cytometry and the inhibitory activity against Pim-1 kinase was measured by mobility shift assay. Molecular docking analysis was performed to rationalize biochemical potency as well. RESULTS: The majority of these quinolines displayed potent growth inhibitory effects, among which compounds 13e, 13f and 13h were the most effective ones, with GI50 values of 2.61/3.56, 4.73/4.88 and 4.68/2.98 µM, respectively. Structure-activity relationships indicated that both appropriate heterocycles at the C4 position of pyridine and suitable substituent at quinoline had a significant impact on improving activity. Compounds 13e and 24d exhibited moderate Pim-1 kinase inhibitory activity. CONCLUSION: In this study, three series of novel molecules bearing quinoline scaffold were designed, synthesized and evaluated for their in-vitro anti-proliferative activity. The most promising candidate, 13e, caused cell cycle arrest in a concentration-dependent manner and further induced apoptosis, which might represent a novel antiproliferative agent working through Pim-1 kinase inhibition to a certain extent.

Quasi-continuous tunable semiconductor laser based on the gain-lever effect with full C-band coverage fabricated by standard lithography.

A quasi-continuous tunable semiconductor laser covered full C-band is demonstrated. The quasi-continuous tuning range of the tunable semiconductor laser is significantly improved by optimizing the length of the phase section using the gain-lever effect, achieving a 36 nm range that covered the whole C-band. In the tuning range, 46 channels with 100 GHz spacing are achieved, and all channels exhibit a side mode suppression ratio above 30 dB. No regrowth or high-precision lithography is involved in the fabrication process of the tunable semiconductor laser, which has the potential to provide a cost-effective light source for dense wavelength division multiplexing systems.

Comparison of Diagnostic Value for Chronic Kidney Disease between 640-Slice Computed Tomography Kidney Scan and Conventional Computed Tomography Scan.

Objective: To explore the diagnostic value for chronic kidney disease (CKD) between 640-slice computed tomography (CT) kidney scan and conventional CT scan. Methods: A total of 120 CKD patients who received kidney plain scan plus enhanced examination in the CT room of the Medical Imaging Department of our hospital from June 2019 to September 2019 were selected and randomly divided into the experimental group (n = 60) and the control group (n = 60). Patients in the control group received the conventional CT plain scan and enhanced scan, and for patients in the experimental group, CT plain scan was performed first, the range of 640-slice CT dynamic volume scan was determined, and after bolus injection of contrast agent, dynamic volume scan was performed for scanning in the cortical phase, myeloid phase, and secretory phase. The imaging quality and effective scanning dose were compared between the two modalities, and the relationship between CT values obtained from 640-slice CT scan and conventional CT scan and the renal impairment was analyzed. Results: Compared with the control group, the image quality of 640-slice CT scan conducted in the experimental group was significantly better (P < 0.05); the effective radiation doses of the experimental group and the control group were, respectively, (1.89 ± 0.32) mSv and (3.26 ± 0.47) mSv, indicating that the dose was significantly lower in the experimental group than in the control group (t = 18.664, P < 0.001), and the correlation analysis showed that the relationship between the sum of CT values in the cortical phase of both kidneys and kidney injury in the experimental group was r = 0.835, P < 0.001. Conclusion: Both 640-slice CT kidney scan and conventional CT scan can be used in the diagnosis of CKD. 640-slice CT has a lower radiation dose, better image quality, and higher application value.

Comparison of the Application Value for Diagnosis of Chronic Kidney Disease between Color Doppler Flow Quantification Technique and Computed Tomography.

Objective: The aim of this study is to compare the application value for diagnosis of chronic kidney disease (CKD) between the color Doppler flow quantification (CDFQ) technique and computed tomography (CT). Methods: The clinical data of 88 hospitalized patients treated in the Renal Medicine of our hospital and diagnosed with CKD after pathological examination from June 2020 to June 2021 were selected for the retrospective analysis, and 32 individuals with normal physical examination results in the same period were selected as the control group. All study subjects received CDFQ and 640-slice volume CT examination, and by plotting the ROC curves, the clinical value of different diagnostic modalities was analyzed. Results: The 3D renal volumes between the stage 1 group and control group were significantly different (P < 0.05); the 3D renal volumes between the stage 2 group and control group and between the stage 2 group and stage 1 group were significantly different (P < 0.05); in the comparison between the stage 3 group versus control group/stage 2 group, the RI values, 3D renal volumes, and cortical thicknesses were significantly different (P < 0.05); in the comparison between the stage 4 group versus control group/stage 1 group, the RI values, 3D renal volumes, and cortical thicknesses were significantly different, and between the stage 4 group and stage 2 group, the RI values and cortical thicknesses were significantly different (P < 0.05); in the comparison between the stage 5 group versus control group/stage 1 group/stage 2 group/stage 3 group, the RI values, 3D renal volumes, and cortical thicknesses were significantly different, and between the stage 5 group and stage 4 group, the RI values and 3D renal volumes were significantly different (P < 0.05); among various groups, the measurement indicators of 640-slice volume CT scan were significantly different (P < 0.05); and in terms of disease classification, the AUC value, positive predictive value, negative predictive value, sensitivity, and specificity of 640-slice volume CT were higher than those of CDFQ diagnosis. Conclusion: 640-slice volume CT has a higher efficacy in diagnosing CKD and can provide a reliable basis for the selection of treatment schemes for CKD patients.

Single-chip hybrid integrated silicon photonics transmitter based on passive alignment.

A single-chip hybrid integrated silicon photonics transmitter based on passive alignment flip-chip bonding technology has been demonstrated. The transmitter is developed by the hybrid integration of a C-band slotted laser with 1 mm cavity length and a Mach-Zehnder modulator with 2 mm long phase shifter. A 3 dB bandwidth of the small signal response is 16.35 GHz at 5.99 VPP superimposed with a reverse bias voltage of 2.43 V. A 25 Gbps data transmission experiment of the hybrid integrated transmitter is performed at 25°C.

A fast iterative filtering decomposition and symmetric difference analytic energy operator for bearing fault extraction.

The fault vibration signals extracted from defective bearings are generally non-stationary and non-linear. Besides, such signals are extremely weak and easily buried by inevitable background noise and vibration interferences. Thus, the development of methods capable of detecting their hidden information in a fast and reliable way is of high interest in bearing fault detection. An alternative bearing fault extraction method based on fast iterative filtering decomposition (FIFD) and symmetric difference analytic energy operator (SD-AEO) is proposed in this work. The FIFD method performs excellently in suppressing mode mixing and produce a meaningful decomposition for a higher level of noise. More importantly, unlike other mode decomposition techniques, the FIFD has high computational efficiency, so we can speed up the calculations significantly. After decomposing the signal into a group of intrinsic mode functions (IMFs), a criterion based on the product of kurtosis and permutation entropy (PeEn) is proposed to choose the IMFs embedding richer bearing fault impulses. Subsequently, an enhanced demodulation technique, SD-AEO, is employed to detect the bearing fault signatures from the selected IMF. The simulated and real signals verify the efficiency of the proposed method.

A 18ß-glycyrrhetinic acid conjugate with Vorinostat degrades HDAC3 and HDAC6 with improved antitumor effects.

Semisynthetic 18ß-glycyrrhetinic acid (GA) analogues bearing 1-en-2-cyano-3-oxo substitution on ring A have enhanced antitumor effects with reduced levels of HDAC3 and HDAC6 proteins. Aiming to inhibit both HDAC protein and activity, we developed a hybrid molecule by tethering active GA analogue methyl 2-cyano-3,11-dioxo-18ß-olean-1,12-dien-30-oate (CDODA-Me) and Vorinostat (SAHA). We tested the proper hybrid approaches of GA with hydroxamic acid and turned out that GA conjugated with SAHA by a piperazine linker was the best. The conjugate (15) of CDODA-Me and SAHA linked through a piperazine group was a potent cytotoxic agent against cancer cells with apoptosis induction. Compound 15 was more effective than the simple combination of CDODA-Me and SAHA to induce apoptosis. Mechanistic studies revealed that 15 was less effective than SAHA to inhibit HDAC activity, but was more effective than CDODA-Me to decrease the levels of HDAC3 and HDAC6 proteins with upregulated levels of acetylated H3 and acetylated α-tubulin. Compound 15 represents a new HDAC3 and HDAC6 inhibitor by reducing protein levels.

Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation.

In the existing recommender systems, matrix factorization (MF) is widely applied to model user preferences and item features by mapping the user-item ratings into a low-dimension latent vector space. However, MF has ignored the individual diversity where the user's preference for different unrated items is usually different. A fixed representation of user preference factor extracted by MF cannot model the individual diversity well, which leads to a repeated and inaccurate recommendation. To this end, we propose a novel latent factor model called adaptive deep latent factor model (ADLFM), which learns the preference factor of users adaptively in accordance with the specific items under consideration. We propose a novel user representation method that is derived from their rated item descriptions instead of original user-item ratings. Based on this, we further propose a deep neural networks framework with an attention factor to learn the adaptive representations of users. Extensive experiments on Amazon data sets demonstrate that ADLFM outperforms the state-of-the-art baselines greatly. Also, further experiments show that the attention factor indeed makes a great contribution to our method.

NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks.

Traditional recommender systems rely on user profiling based on either user ratings or reviews through bi-sentimental analysis. However, in real-world scenarios, there are two common phenomena: (1) users only provide ratings for items but without detailed review comments. As a result, the historical transaction data available for recommender systems are usually unbalanced and sparse; (2) in many cases, users' opinions can be better grasped in their reviews than ratings. For the reason that there is always a bias between ratings and reviews, it is really important that users' ratings and reviews should be mutually reinforced to grasp the users' true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation. Specifically, we exploit two-step training neural networks, which utilize both reviews and ratings to grasp users' true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring (SC) method, which employs dual attention vectors to predict the users' sentiment scores of their reviews rather than using bi-sentiment analysis. Next, a combination function is designed to use the results of SC and user-item rating matrix to catch the opinion bias. It can filter the reviews and users, and build an enhanced user-item matrix. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user-item matrix. Extensive experiments on several real-world datasets (Yelp, Amazon, Taobao and Jingdong) demonstrate that (1) our approach can achieve a superior performance over state-of-the-art baselines; (2) our approach is able to tackle unbalanced data and achieve stable performances.