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Gs-Rb1 could alleviate HFD and PA-induced lipid accumulation by stimulating autophagic flux and inducing autophagic lipid degradation through the miR-128-TFEB pathway.

COVID-19 claimed thousands of lives worldwide and put food systems, public health, and the workplace in grave danger. Airline stock losses indicate industry-wide damage. We compared two large government-owned airlines to two smaller for-profit airlines over five years. A case study examined how online reviews have affected Chinese tourism. Investors in the hotel industry made losses during the pandemic. This study analyzed the operational status of Air China, China Eastern Airlines, China Southern Airlines, Juneyao Airlines, and Spring Airlines. This was done to assess COVID 19's impact on the Chinese airline industry. The results show that the pandemic affected aviation.

In recent years, the effect of lipid metabolism on health has attracted more and more attention. Ginseng is a traditional Chinese herbal medicine in China and is widely used as food in Asia. Ginsenoside Rb1 (Gs-Rb1) is the most abundant ingredient in ginsenoside, which has a variety of biological activities. In this study, we found that Gs-Rb1 can reduce lipid accumulation in mice and HepG2 cells induced by a high-fat diet (HFD) and palmitic acid (PA). At the same time, we also found that Gs-Rb1 could stimulate the autophagic flux of HFD-fed mice and PA-treated HepG2 cells, and it is further verified by adding the autophagy activator rapamycin (Rapa) and autophagy inhibitor chloroquine (CQ). Furthermore, we found that Gs-Rb1 promoted the nucleus translocation of the transcription factor EB (TFEB) and the target role of miR-128, thus stimulating autophagic flux. Therefore, our results showed that Gs-Rb1 enhanced the transcription of TFEB and its downstream lysosome-related genes by inhibiting miR-128, improved the degradation ability of lysosomes to autophagosomes, and then promoted autophagic lipid degradation.

We propose an efficient and modulation-format-transparent method to jointly estimate optical signal-to-noise ratio (OSNR) and frequency offset (FO) by using signal spectrum correlations for coherent optical fiber communication systems. Based on the signal spectrum correlation analysis, a coarse FO estimation (FOE) and the corresponding compensation can be conducted after chromatic dispersion compensation (CDC). Then, OSNR monitoring can be accurately operated without deterioration caused by FO. Meanwhile, to realize fine FOE, down-sampling process of signal can be used to reduce the complexity of fast Fourier transform-based FOE (FFT-FOE) without losing FOE resolution. Simulation results show that when the OSNR is in the range of 10 to 30 dB, the proposed scheme presents an absolute OSNR estimation error lower than 0.18 dB. Finally, we experimentally demonstrate our scheme in an optical back-to-back (B2B) transmission link using 28 Gbaud dual-polarization (DP)-4/16/32-quadrature amplitude modulation (QAM) formats, and an absolute OSNR estimation error lower than 0.66 dB is achieved for OSNR ranging from 15 to 30 dB.

A dataset is the basis of deep learning model development, and the success of deep learning models heavily relies on the quality and size of the dataset. In this work, we present a new data preparation protocol and build a large fragment-based dataset Frag20, which consists of optimized 3D geometries and calculated molecular properties from Merck molecular force field (MMFF) and DFT at the B3LYP/6-31G* level of theory for more than half a million molecules composed of H, B, C, O, N, F, P, S, Cl, and Br with no larger than 20 heavy atoms. Based on the new dataset, we develop robust molecular energy prediction models using a simplified PhysNet architecture for both DFT-optimized and MMFF-optimized geometries, which achieve better than or close to chemical accuracy (1 kcal/mol) on multiple test sets, including CSD20 and Plati20 based on experimental crystal structures.

Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p P p P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

To increase the transmission capacity, ultra-wideband wavelength-division multiplexing (UWB WDM) has been exploited to enlarge the spectral range. However, inter-channel stimulated Raman scattering (ISRS) results in power transition from high-frequency channels to low-frequency channels in wideband scenarios, which degrades the Q-factor of signals. Hence, we modify the optimization method of power control by applying the simulated annealing (SA) algorithm to search for the optimal power slopes and offsets of three bands to construct an optimum distribution of launch powers over channels. High transmission capacity can be reached by carrying 384 channels (96+96+192) in the C+L+S band with the consideration of dynamic Raman gain and channel-dependent parameters. We show that compared to using brute-force searching (BFS), a comparable and even higher transmission capacity can be achieved by the SA algorithm. Meanwhile, the searching speed of the SA algorithm is much faster. Also, different optimizing strategies can be selected to balance the trade-off between capacity and spectral flatness. This method can be used for designing arbitrary optical fiber UWB WDM systems before practical testing.

Purpose: To determine whether monocularly- and binocularly-induced spherical and meridional blur and aniseikonia had similar effects on stereopsis thresholds. Methods: Twelve participants with normal binocular vision viewed McGill modified random dot stereograms to determine stereoacuities in a four-alternative forced-choice procedure. Astigmatism was induced by placing trial lenses in front of the eyes. Twenty-three conditions were used, consisting of zero (no lens), +1 D and +2 D spheres and cylinders at axes 180, 45 and 90 in front of the right eye, and the following binocular combinations of both lens powers: R × 180/L × 180, R × 45/L × 45, R × 90/L × 90, R sphere/L sphere, R × 180/L × 90, R × 45/L × 135, R × 90/L × 180. Aniseikonia was induced by placing magnifying lenses in front of the eyes. Twenty-three conditions were used, consisting of zero, 6% and 12% overall magnification and both magnifications at axes 180, 45 and 90 in front of the right eye only, and the following binocular combinations using 3% and 6% lenses: R × 90/L × 90, R × 45/L × 45, R × 180/L × 180, R overall/L overall, R × 90/L × 180, R × 45/L × 135, and R × 180/L × 90. Results: Stereopsis losses for binocular blur effects with parallel axes (non-anisometropic) were the same as for monocular blur effects of the same axes, and these were strongly dependent on axis (spherical blur and ×90 had the greatest effects). Binocular blur effects with orthogonal axes had greater effects than with parallel axes, with the axis combination of the former having no effect (e.g. R × 90/L × 180 was similar to R × 45/L × 135). For induced aniseikonia, splitting the magnifications between the eyes improved stereopsis slightly, and the effects were not dependent on axis. Conclusion: Binocular blur affects stereopsis similarly to monocular meridional blur if axes in the two eyes are parallel, whereas the effect is greater if the axes are orthogonal. In meridional aniseikonia, splitting magnification between the right and left lenses produces a small improvement in stereopsis that is independent of axis direction and right/left combination.

Purpose: Stereopsis depends on horizontally disparate retinal images but otherwise concordance between eyes. Here we investigate the effect of spherical and meridional simulated anisometropia and aniseikonia on stereopsis thresholds. The aims were to determine effects of meridian, magnitude and the relative effects of the two conditions. Methods: Ten participants with normal binocular vision viewed McGill modified random dot stereograms through synchronised shutter glasses. Stereoacuities were determined using a four-alternative forced-choice procedure. To induce anisometropia, trial lenses of varying power and axes were placed in front of right eyes. Seventeen combinations were used: zero (no lens) and both positive and negative, 1 and 2 D powers, at 45, 90 and 180 axes; spherical lenses were also tested. To induce aniseikonia 17 magnification power and axis combinations were used. This included zero (no lens), and 3%, 6%, 9% and 12% at axes 45, 90 and 180; overall magnifications were also tested. Results: For induced anisometropia, stereopsis loss increased as cylindrical axis rotated from 180° to 90°, at which the loss was similar to that for spherical blur. For example, for 2 D meridional anisometropia threshold increased from 1.53 log sec arc (i.e. 34 sec arc) for x 180 to 1.89 log sec arc (78 sec arc) for x 90. Anisometropia induced with either positive or negative lenses had similar detrimental effects on stereopsis. Unlike anisometropia, the stereopsis loss with induced meridional aniseikonia was not affected by axis and was about 64% of that for overall aniseikonia of the same amount. Approximately, each 1 D of induced anisometropia had the same effect on threshold as did each 6% of induced aniseikonia. Conclusion: The axes of meridional anisometropia but not aniseikonia affected stereopsis. This suggests differences in the way that monocular blur (anisometropia) and interocular shape differences (aniseikonia) are processed during the production of stereopsis.

In recent years, foodborne pollutants have become a hot issue in the field of food safety. 3-chloro-1,2-propanediol (3-MCPD) is a widely existing food contaminant. In our previous study, it was confirmed that 3-MCPD can block autophagic flux by inhibiting lysosomal function, thus causing liver injury. Ginseng is a traditional Chinese herbal medicine that contains a variety of bioactive ingredients, among which ginsenoside Rb1 (Gs-Rb1) is the most abundant. In this study, we aim to use Gs-Rb1 to improve 3-MCPD-induced autophagic flux blockage to alleviate liver injury. First, a nontoxic dose of Gs-Rb1 was identified by screening with the MTT method in which Gs-Rb1was added to HepG2 cells and co-treated with 3-MCPD. We found that Gs-Rb1 effectively enhanced the cell activity inhibited by 3-MCPD. Meanwhile, apoptosis data showed that Gs-Rb1 significantly alleviated the apoptosis of HepG2 cells induced by 3-MCPD. Subsequently, we found that Gs-Rb1 could alleviate autophagic flux blockage caused by 3-MCPD in a dose-dependent manner by detecting autophagy-related protein levels and transfecting mRFP-GFP-LC3 adenovirus. On this basis, we used Western blotting and qPCR to explore whether miR-128 was involved in the alleviation effect of Gs-Rb1 on autophagic flux blockade induced by 3-MCPD. The results showed that Gs-Rb1 inhibited the expression of miR-128 and promoted the nuclear expression and target gene transcription of TFEB. Finally, the findings were confirmed by using a hsa-miR-128 inhibitor and mimic. We found that hsa-miR-128 inhibitor alleviated the autophagic flux blockage and apoptosis caused by 3-MCPD and Gs-Rb1 also had a certain alleviation effect on the autophagic flux blockage and apoptosis caused by hsa-miR-128 mimic. This study elaborated the mechanism by which Gs-Rb1 alleviates hepatotoxicity induced by foodborne 3-MCPD by stimulating autophagic flux via miR-128-targeted TFEB, which provides a reliable theoretical basis and target for the use of natural substances to reduce the harm of food processing pollutants on the human body. PRACTICAL APPLICATION: We found that natural ginsenoside Rb1 can alleviate liver injury induced by 3-MCPD(a toxic substance found in foods such as refined vegetable oil, soy sauce, and baby milk powder), which is conducive to the development and utilization of ginseng and has practical significance for the prevention of foodborne liver injury.

Efficient synchronization is of great importance for fiber optical coherent communication system. Here, we propose an accurate and low-complexity timing/frequency synchronization scheme based on sparse fast Fourier transform (S-FFT). The proposed scheme consists of coarse timing/frequency synchronization, fine timing synchronization, and fine frequency synchronization. Inspired by the idea of S-FFT, we take full advantages of the sparse nature of training symbols (TSs) and realize the synchronization in the frequency domain. The proposed scheme enables accurate timing synchronization at low signal-to-noise ratio (SNR) region. A complete frequency offset estimation (FOE) range of [−symbol rate/2, + symbol rate/2] can be realized with high resolution. Finally, the proposed scheme is experimentally verified through 10 Gbaud dual-polarization (DP) 16/32-quadrature amplitude modulation (QAM) transmission, without the BER performance penalty. In particular, the total computation complexity is reduced by nearly 200 times in comparison with that using conventional sliding window correlation scheme.

We have assembled a three-dimensional (3D) plasmon ruler using an atomic force microscope (AFM) tip to manipulate single gold nanocrystals on top of electron beam lithography fabricated base layers...

The use of neural networks to predict molecular properties calculated from high level quantum mechanical calculations has made significant advances in recent years, but most models need input geometries from DFT optimizations which limit their applicability in practice. In this work, we explored how machine learning can be used to predict molecular atomization energies and conformation stability using optimized geometries from Merck Molecular Force Field (MMFF). Based on the recently introduced deep tensor neural network (DTNN) approach, we first improved its training efficiency and performed an extensive search of its hyperparameters, and developed a DTNN_7ib model which has a test accuracy of 0.34 kcal/mol mean absolute error (MAE) on QM9 dataset. Then using atomic vector representations in the DTNN_7ib model, we employed transfer learning (TL) strategy to train readout layers on the QM9(M) dataset, in which QM properties are the same as in QM9 [calculated at the B3LYP/6–31G(2df,p) level] while molecular geometries are corresponding local minima optimized with MMFF94 force field. The developed TL_QM9(M) model can achieve an MAE of 0.79 kcal/mol using MMFF optimized geometries. Furthermore, we demonstrated that the same transfer learning strategy with the same atomic vector representation can be used to develop a machine learning model that can achieve an MAE of 0.51 kcal/mol in molecular energy prediction using MMFF geometries for an eMol9_C(M) conformation dataset, which consists of 9959 molecules and 88,234 conformations with energies calculated at the B3LYP/6–31G* level. Our results indicate that DFT-level accuracy of molecular energy prediction can be achieved using force-field optimized geometries and atomic vector representations learned from deep tensor neural network, and integrated molecular modeling and machine learning would be a promising approach to develop more powerful computational tools for molecular conformation analysis.

SIGNIFICANCE Nominally plano ophthalmic prisms give autorefraction results similar to those predicted on the basis of how effective powers change with pantoscopic tilt, and magnifying lenses give autorefraction results similar to those predicted on the basis of vergence changes. Without appreciation of the optics involved, these effects might wrongly be considered artifacts. PURPOSE The purpose of this study was to investigate the interactions of autorefractors with lenses and prisms. METHODS There were 15 adult participants across three experiments, with a range of ages and refractions. In experiments 1 and 2, participants wore frames containing base-up and base-down nominally plano prisms. In experiment 3, participants wore a lens that produced either 6.3% magnification or 5.9% minification, depending on which surface faced the eye. Autorefracting instruments with different operating principles were used: Shin-Nippon SRW5000 autorefractor, Grand Seiko 5100K autorefractor, Hoya AR-530 autorefractor, a Complete Ophthalmic Analysis System–High Definition wavefront sensor, and Tomey FC-800 autorefractor. A theory on the likely effects of magnifying lenses was presented. RESULTS For ophthalmic prisms, refractions showed results similar to those predicted on the basis of how effective prism powers change with pantoscopic tilt. As tilt increased, base-up prism gave more positive mean refractions and more negative horizontal/vertical astigmatism and vice versa for base-down prisms. In the presence of 10° tilt, 8Δ base-up prisms and 8Δ base-down prisms had different effects by a mean of 0.36 diopters. Magnifying lenses affected refractions similar to those predicted on the basis of vergence changes, with 6% magnification and minification producing mean changes of −11 and +8%, respectively, in absolute mean refraction. There was no strong evidence that different instruments had different effects. CONCLUSIONS The results have implications for studies in which prisms and lenses are placed in the front eyes, such as accommodation studies using thick lenses close to the eyes to stimulate accommodation rather than by changing object distance.

Aflatoxin B1 (AFB1) is a hazard food pollutant and the most toxic one of all the aflatoxins. It is mainly metabolized in the liver and exerts strong hepatotoxicity and carcinogenesis. Autophagy is an important biological process to maintain the homeostasis of intracellular environment. But the role of autophagy in AFB1-exposured hepatotoxicity remains unclear. The objective of this study was to explore the effect of AFB1 on autophagy flux and its potential mechanisms in HepG2 cells. The data showed AFB1 with no-observed adverse effect level (NOAEL) induced the accumulation of autophagosomes by detecting the level of LC3 and MDC staining. Subsequent findings revealed that autophagosome accumulation was caused by the inhibition of autophagy flux by transfection mRFP-GFP-LC3 adenovirus in the presence of autophagy inhibitor 3-MA and CQ. Further, we investigated lysosomal pH by Acridine orange (AO) and Lysotracker Red (LTR) staining and found that AFB1 exposure caused lysosomal alkalinization. These results indicated AFB1 with NOAEL could inhibit autophagy flux by inducing lysosomal alkalinization. Our study was helpful to further explain early hepatotoxicity mechanism of AFB1.

Additional file 4: Figure S4. Survival analysis of 15 model immune genes.

Additional file 4: Figure S4. Survival analysis of 15 model immune genes.

Additional file 3: Figure S3. Correlation analysis between TNM&Stage and 15 model genes in breast cancer cases. (A) Correlation analysis between tumor stage and 15 model genes expression in breast cancer cases. (B) Correlation analysis between node stage and 15 model genes expression in breast cancer cases. (C) Correlation analysis between metastasis stage and 15 model genes in breast cancer cases. (D) Correlation analysis between pathologic stage and 15 model genes expression in breast cancer cases.

We study the impact of parameter uncertainty induced by inaccurate α, β, γ, fiber length (L) and noise figure on QoT estimation using the GN-based analytical model. We find γ and L have the relatively large influence in most system structure cases.

Additional file 2: Figure S2. Analysis of copy number variation and single nucleotide polymorphism of 15 model immune genes.

Additional file 3: Figure S3. Correlation analysis between TNM&Stage and 15 model genes in breast cancer cases. (A) Correlation analysis between tumor stage and 15 model genes expression in breast cancer cases. (B) Correlation analysis between node stage and 15 model genes expression in breast cancer cases. (C) Correlation analysis between metastasis stage and 15 model genes in breast cancer cases. (D) Correlation analysis between pathologic stage and 15 model genes expression in breast cancer cases.

Additional file 1: Figure S1. GO (A) and KEGG(B) enrichment analysis of DEIGs.

Additional file 5: Figure S5. Correlation between 15 model immune genes and immune cell infiltration.

The mechanism of how the solvent type influences photovoltaic performance and thermal stability of non-fullerene organic solar cells remains unexplored. In this article, the well-known PTB7-Th was selected as a donor, while F8IC was used as an acceptor. The PTB7-Th:F8IC processed from chloroform (CF) exhibited a superiorly higher power conversion efficiency (PCE) of 10.5%, in contrast to the specimen processed from chlorobenzene (CB) of 6.8%. In addition, upon thermal annealing at 160 °C for 120 min, the device processed from CF was more stable than that processed from CB. The incorporation of perylene diimide derivative TBDPDI-C11, serving as the third additive, could also obviously improve the PCE value and thermal stability of PTB7-Th:F8IC processed from CB. According to ultraviolet spectroscopy, atomic force microscopy, transmission electron microscopy, and grazing incidence wide-angle X-ray scattering analyses, the enhanced photovoltaic performance and thermal stability are mainly attributed to formation of PTB7-Th nanofibers and appropriate aggregation of F8IC. The interaction free energy calculated using water and diiodomethane contact angles reveals that PTB7-Th well disperses in CB and tends to aggregate in CF, while F8IC aggregates strongly in CB. The preaggregation matching of the donor and acceptor in solution is essential for the optimization of morphology, efficiency, and thermal stability. The findings in this article could provide useful guidelines to fabricate efficient and thermally stable organic solar cells simply by analyzing the surface energy of components processed from different solvents.

Objective Increasing evidence has indicated an association between immune micro-environment in breast cancer and clinical outcomes. The aim of this research is to comprehensively investigate the effect of tumor immune genes on the prognosis of breast cancer patients. Methods 2498 immune genes were downloaded from ImmPort database. Additionally, we identified and downloaded the transcriptome data of patients with breast cancer from the TCGA database through the R package, as well as relevant clinical information. Survival R package was applied in survival analyses for hub-genes. Cox regression analysis was used to analyze the effect of immune genes on the prognosis of breast cancer. Immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate cox regression analysis was utilized to investigate whether the immune genes risk score model was an independent factor for predicting the prognosis of breast cancer. Nomogram was constructed to comprehensively predict the survival rate of breast cancer. P

We discuss various applications of machine learning techniques in different aspects of optical communications and networking including optical performance monitoring, fiber nonlinearity compensation, cognitive network failure prediction, dynamic planning and cross-layer optimization of software-defined networks, quality of transmission estimation, and physical layer design of optical communication systems. Recent works employing deep learning technologies are also discussed.

3-chloro-1, 2-propanediol (3-MCPD) is a well-known contaminant that was produced in the thermal processing of food. Dietary intake represents the greatest source of exposure to 3-MCPD. Autophagy is an important catabolic pathway that plays an important role in liver physiological function. Evidence suggests that 3-MCPD exposure causes toxicity in liver, but the mechanism remains unknown. Here, we explored the effects of 3-MCPD on autophagic flux and traced the molecular mechanism in HepG2 cells. The data showed 3-MCPD exposure promoted the accumulation of autophagosomes in HepG2 cells. Subsequently, by detected te expression of LC3-Ⅱ and P62 and transfection of mRFP-GFP-LC3 adenovirus, we found that the accumulation of autophagosomes was caused by inhibition of autophagic flux. After that, we investigate lysosomal function and found that 3-MCPD induced lysosomal alkalinization. Further, we detected the expression of TFEB, which is a key nuclear transcription factor in control of lysosome biogenesis and function. We found that 3-MCPD inhibited the nuclear expression of TFEB and mRNA levels of some target genes of TFEB. In order to further verify the role of TFEB in autophagic flux blockage in HepG2 cells induced by 3-MCPD, we overexpressed TFEB by transfection with adenovirus and found that both autophagy inhibition and lysosomal alkalization induced by 3-MCPD were alleviated. These results suggested that 3-MCPD could induce the autophagic flux blockage in HepG2 cells. The possible mechanism was due to the destruction of lysosomal function.

We experimentally demonstrate a blind scheme enabled by a frequency offset loading technique to identify the shaping factor and modulation format of probabilistically shaped 16/64/256QAM signals with various entropies. © 2020 The Author(s)

The set-partitioning quadrature amplitude modulation (SP-QAM) formats are highly preferred for the next generation fiber optical transmission system, due to the Euclidean distance increment of constellation and its power efficiency. However, the existing carrier phase recovery (CPR) scheme of SP-QAM is independently implemented between two polarization signals, and the laser linewidth tolerance of SP-QAM is restricted the same as that of conventional QAM signals. Here, we propose a novel four-dimensional (4-D) CPR scheme for the SP-QAM formats. After the phase offset estimation and precompensation between two polarizations, we implement blind phase search (BPS) algorithm in the 4-D space. Meanwhile, traditional decision circuit is replaced by 4-D detection of SP-QAM and 4-D distance is calculated as the cost function for identifying the desired test phase angle. We carry out numerical simulations under conditions of 28 Gbaud SP-128-QAM, SP-512-QAM, and SP-2048-QAM coherent transmissions. The CPR performance of our proposed 4-D BPS scheme is much better than that of conventional BPS. Given the 1 dB required optical signal-to-noise ratio penalty at ${\rm{BER}} = 1 \times {10^{-3}}$ , the linewidth times symbol duration products of $5.2 \times {10^{-4}}$ , $2 \times {10^{-4}}$ , and $7 \times {10^{-5}}$ are tolerable for SP-128-QAM, SP-512-QAM, and SP-2048-QAM, respectively. We further experimentally verify the performance of the proposed 4-D BPS under the scenarios of 28 Gbaud SP-128-QAM back-to-back and standard single-mode fiber transmission from 750 to 1500 km, indicating of the robustness to the amplified spontaneous emission noise and transmission impairments.

Au@Cu2O core-shell nanocomposites (NCs) were synthesized by reducing copper nitrate on Au colloids with hydrazine. The thickness of the Cu2O shells could be varied by adjusting the molar ratios of Au: Cu. The results showed that the thickness of Cu2O shells played a crucial role in the catalytic activity of Au@Cu2O NCs under dark condition. The Au@Cu2O-Ag ternary NCs were further prepared by a simple galvanic replacement reaction method. Moreover, the surface features were revealed by TEM, XRD, XPS, and UV&ndash, Vis techniques. Compared with Au@Cu2O NCs, the ternary Au@Cu2O-Ag NCs had an excellent catalytic performance. The degradation of methyl orange (MO) catalyzed by Au@Cu2O-Ag NCs was achieved within 4 min. The mechanism study proved that the synergistic effects of Au@Cu2O-Ag NCs and sodium borohydride facilitated the degradation of MO. Hence, the designed Au@Cu2O-Ag NCs with high catalytic efficiency and good stability are expected to be the ideal environmental nanocatalysts for the degradation of dye pollutants in wastewater.

Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for both docking pose prediction and affinity ranking, and in D3R-GC4 for binding affinity ranking. The difficulties posed by CatS inhibitors in D3R mainly come from three aspects: large size, high flexibility and similar chemical structures. We have participated in GC4; our best submitted model, which employs a similarity-based alignment docking and Vina scoring protocol, yielded Kendall’s τ of 0.23 for 459 binders in GC4. In our further explorations with machine learning, by curating a CatS specific training set, adopting a similarity-based constrained docking method as well as an arm-based fragmentation strategy which can describe large inhibitors in a locality-sensitive fashion, our best structure-based ranking protocol can achieve Kendall’s τ of 0.52 for all binders in GC4. In this exploration process, we have demonstrated the importance of training data, docking approaches and fragmentation strategies in inhibitor-ranking protocol development with machine learning.

Au@Cu2O core-shell nanocomposites (NCs) were synthesized by reducing copper nitrate on Au colloids with hydrazine. The thickness of the Cu2O shells could be varied by adjusting the molar ratios of Au: Cu. The results showed that the thickness of Cu2O shells played a crucial role in the catalytic activity of Au@Cu2O NCs under dark condition. The Au@Cu2O-Ag ternary NCs were further prepared by a simple galvanic replacement reaction method. Moreover, the surface features were revealed by TEM, XRD, XPS, and UV–Vis techniques. Compared with Au@Cu2O NCs, the ternary Au@Cu2O-Ag NCs had an excellent catalytic performance. The degradation of methyl orange (MO) catalyzed by Au@Cu2O-Ag NCs was achieved within 4 min. The mechanism study proved that the synergistic effects of Au@Cu2O-Ag NCs and sodium borohydride facilitated the degradation of MO. Hence, the designed Au@Cu2O-Ag NCs with high catalytic efficiency and good stability are expected to be the ideal environmental nanocatalysts for the degradation of dye pollutants in wastewater.

The assembly of nanoscale materials into arbitrary, organized structures remains a major challenge in nanotechnology. Herein, we report a general method for creating 2D structures by combining top-down lithography with bottom-up chemical assembly. Under optimal conditions, the assembly of gold nanoparticles was achieved in less than 30 min. Single gold nanoparticles, from 10 to 100 nm, can be placed in predetermined patterns with high fidelity, and higher-order structures can be generated consisting of dimers or trimers. It is shown that the nanoparticle arrays can be transferred to, and embedded within, polymer films. This provides a new method for the large-scale fabrication of nanoparticle arrays onto diverse substrates using wet chemistry.

Traditional frequency offset estimation (FOE) algorithms, such as fast Fourier transform-based FOE (FFT-FOE) and differential FOE (Diff-FOE), are no longer suitable for 32-QAM optical coherent receiver. Here, we propose a feed-forward and non-data-aided FOE algorithm for 32-QAM based on constellation rotation. After equalization, the constellation points of the second and the forth ring are selected and rotated with an angle of $\pi /4$ . Thus, those points gather along diagonal lines, which enable us to use traditional Diff-FOE for 32-QAM signal. The performance of our proposed algorithm is numerically and experimentally verified under the scenario of back-to-back transmission with dual-polarization 32-QAM signals. Our proposed FOE algorithm is more robust to amplified spontaneous emission noise in comparison with recently proposed QPSK-selection assisted FFT-FOE, while the complexities are reduced by factors of 1.7 and 3.7 in the forms of multipliers and adders, respectively.

Applications of machine learning (ML) models in optical communications and networks have been extensively investigated. For an optical wavelength-division-multiplexing (WDM) system, the quality of transmission (QoT) estimation generally depends on many parameters including the number and arrangement of WDM channels; launch power of each channel; number and distribution of fiber spans; attenuation, dispersion, and nonlinearity parameters and length of each fiber span; noise figure; gain and gain tilt of erbium-doped fiber amplifiers; transceiver noise; digital signal processing (DSP) performance; and so on. In recent years, ML-based QoT estimation schemes have gained significant attention. However, nearly all relevant works are conducted through simulations because it is difficult to obtain sufficient and high-quality datasets for training ML models. In this paper, we demonstrate completely automated generation and collection of an ultra-large-scale experimental training dataset for ML-model-based QoT estimation by automation of transceivers and optical link parameters, as well as data transfer and DSP. Implementation details and key codes of automation are presented. Artificial neural network models with one and two hidden layers are trained by the collected dataset, and brief QoT estimation results are evaluated and discussed to verify the performance and stability of the established automated system.

A motor and a generator are usually needed for converting the forms of energy between mechanical and electrical in some applications. Recently, we have proposed an energy conversion/storage device based on a unique interacting behavior between a permanent magnet and a closed superconducting coil. This device needs neither a power supply nor a motor/generator and is able to complete the conversion from mechanical energy to electrical energy or the energy storing-releasing cycle of mechanical energy → electromagnetic energy → mechanical energy. In principle, the energy conversion efficiency of this kind of device is higher than most existing energy storages. This article introduces the principle and the experimental results of a proof-of-concept prototype of the proposed device. We conducted three categories of tests on the prototype. The first was for testing the energy conversion efficiency. The second was for examining the electrical loss of the superconducting coil. The third was for exploring the delivering capacity of this kind of device. The results of these experiments demonstrate that this device is able to effectively covert mechanical energy to electrical energy with an efficiency ＞ 90%. The conversion efficiency of a mechanical energy → electrical energy → mechanical energy cycle is greater than 80%. Based on the experimental results, we believe that this kind of device is suitable for being applied in vehicle regenerative braking for energy conservation, particularly meaningful for urban rail transportations.

We proposed a two-dimensional vector displacement sensor with the capability of distinguishing the direction and amplitude of the displacement simultaneously, with improved performance assisted by random forest, a powerful machine learning algorithm. The sensor was designed based on a seven-core multi-core fiber inscribed with Bragg gratings, with a displacement direction range of 0-360° and the amplitude range related to the length of the sensor body. The displacement information was obtained under a random circumstance, where the performances with theoretical model and random forest model were studied. With the theoretical model, the sensor performed well over a shorter linear range (from 0 to 9 mm). Whereas the sensor assisted with random forest algorithm exhibits better performance in two aspects, a wider measurement range (from 0 to 45 mm) and a reduced measurement error of displacement. Mean absolute errors of direction and amplitude reconstruction were decreased by 60% and 98%, respectively. The proposed displacement sensor shows the possibility of machine learning methods to be applied in point-based optical systems for multi-parameter sensing.

A linewidth-tolerant adaptive equalization scheme is proposed for M-ary offset quadrature amplitude modulation (OQAM) signal at the presence of wide laser linewidth. Initially, we present a close-form mathematical expression of OQAM signal at the presence of phase noise and derive the condition to obtain the optimal tap value of adaptive equalizer. Our theoretical investigations proves that phase noise in OQAM signal may result in variation of optimal tap value of adaptive equalizer. Consequently, conventional digital signal processing (DSP) flow that separates adaptive equalization and carrier phase recovery (CPR) into two independent modules cannot apply to OQAM signal anymore. Then, we propose a linewidth-tolerant adaptive equalization scheme that incorporate both adaptive equalizer and CPR for m-ary OQAM signal. Taking the 16-OQAM into account, we comprehensively evaluate its performance to compensate residual chromatic dispersion (CD) and polarization mode dispersion (PMD) at the presence of wide laser linewidth. Simulation shows that our proposed adaptive equalization can effectively compensate residual CD that is below 400 ps/nm without performance penalty at the presence of wide laser linewidth. In particular, a tolerance of linewidth and symbol duration products of 1 × 10 - 4 is secured under conditions of CD=400 ps/nm and DGD=10 ps, given 1-dB required-OSNR penalty at BER= 10 - 3 .

Low complexity carrier phase recovery (CPR) scheme based on vertical blind phase search (V-BPS) for M-ary offset quadrature amplitude modulation (OQAM) is proposed and numerically verified. After investigating the constellations of both even and odd samples with respect to the phase noise, we identify that the CPR can be realized by measuring the verticality of constellation with respect to different test phase angles. Then measurement without multiplication in the complex plane is found with low complexity. Furthermore, a two-stage configuration is put forward to further reduce the computational complexity (CC). Compared with our recently proposed modified blind phase search (M-BPS) algorithm, the proposed algorithm shows comparable tolerance of phase noise, but reduces the CC by a factor of 3.81 (or 3.05) in the form of multipliers (or adders), taking the CPR of 16-OQAM into account.

Structure-based drug design is critically dependent on accuracy of molecular docking scoring functions, and there is of significant interest to advance scoring functions with machine learning approaches. In this work, by judiciously expanding the training set, exploring new features related to explicit mediating water molecules as well as ligand conformation stability, and applying extreme gradient boosting (XGBoost) with Δ-Vina parametrization, we have improved robustness and applicability of machine-learning scoring functions. The new scoring function Δ(vina)XGB can not only perform consistently among the top compared to classical scoring functions for the CASF-2016 benchmark but also achieves significantly better prediction accuracy in different types of structures that mimic real docking applications.

Frequency offset (FO) is an important impairment in coherent transmission systems induced by wavelength mismatch between a free-running local oscillator (LO) and the transmit laser. A fast-Fourier-transform-based FO estimation (FFT-FOE) scheme is commonly employed. The obtained FOE value can be used to compensate FO of a relatively long duration of the signal. On the other hand, the drift of the laser frequency leads to time-varying FO, and FOE should operate in intervals to track the FO drift (FOD) for the best system performance. However, the complexity of FFT is high, and continuous operation of FFT-FOE results in huge energy consumption since it is unclear when FO drifts to an intolerable value. This Letter aims to solve such a problem: How often should FFT-FOE be operated to guarantee system performance with the lowest complexity? Here, we propose a FOD monitoring scheme based on sparse fast Fourier transform, by which optimum performance and minimum cost of FOE are achieved simultaneously. A downsampling process is further proposed to reduce the complexity of subsequent FFT-FOE. The performance is experimentally verified with 28 Gbaud coherent systems.

We propose an efficient timing/frequency synchronization assisted by sparse FFT and optimized training symbols. The proposed scheme is proved to be robust at low SNR can achieve complete frequency offset estimation range with ultra-low complexity.