Optimization strategy of power control for C+L+S band transmission using a simulated annealing algorithm.

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.

Random forest assisted vector displacement sensor based on a multicore fiber.

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.

Identification and development of an independent immune-related genes prognostic model for breast cancer.

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 < 0.05 was considered to be statistically significant. RESULTS: In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, 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.

Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning.

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.

Aflatoxin B1 inhibited autophagy flux by inducing lysosomal alkalinization in HepG2 cells.

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.

Joint linear and nonlinear noise monitoring techniques based on spectrum analysis.

As one of the key indicators of signal quality in fiber communication systems, optical signal-to-noise ratio (OSNR) needs to be accurately monitored to ensure reliable network planning, operation, and reconfiguration. OSNR monitoring techniques considering only accumulated amplified spontaneous emission (ASE) noise are no longer suitable for dispersion unmanaged long-haul and dense wavelength division multiplexing (WDM) systems, where the contribution of fiber nonlinearity to total SNR cannot be neglected. In this paper, we propose a modulation-format-transparent, accurate joint linear and nonlinear noise monitoring scheme based on calculation of correlation between two spectral components at the upper and lower sideband of the signal spectrum. Different characteristics of flat linear noise spectrum and non-flat nonlinear noise spectrum are used to distinguish the influences on the correlation value from both noise sources. Simulation results show that the proposed scheme can accurately monitor SNRlinear and SNRnonlinear within a wide launch power range from -5 dBm to 5 dBm per channel for multi-channel WDM systems with a 915-km single mode fiber (SMF) link. The performance of the proposed scheme is further experimentally verified in up-to-7 channel WDM systems over a 915 km SMF link.

Effects of simulated anisometropia and aniseikonia on stereopsis.

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.

Comparison of blur and magnification effects on stereopsis: overall and meridional, monocularly- and binocularly-induced.

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.

Modulation format identification assisted by sparse-fast-Fourier-transform for hitless flexible coherent transceivers.

For hitless flexible coherent transceivers based next-generation agile optical network, efficient modulation format identification (MFI) is an essential element in digital signal processing (DSP) flow at the receiver-side (Rx). In this paper, we propose a blind and fast MFI scheme with high identification accuracy at low optical signal-to-noise ratio (OSNR) regime. This is achieved by first raising the signal to the 4th power and calculate the peak-to-average power ratio (PAPR) of the corresponding spectra to distinguish 32 quadrature amplitude modulation (QAM) from quadrature phase shift keying (QPSK), 16 and 64QAM signals. Then, followed by iterative partition schemes to remove signals with phase ±π4,±3π4 (or QPSK-like phases) based on the signal amplitudes, the PAPR of the remaining signals is calculated to distinguish the other three formats. Additionally, by frequency offset (FO) pre-compensation, the spectrum can be obtained using sparse-fast-Fourier-transform (S-FFT), which greatly reduces the total complexity. The MFI performance is numerically and experimentally investigated by 28 Gbaud dual-polarization (DP) coherent optical back-to-back (B2B) and up to 1500 km standard single mode fiber (SSMF) transmission system using QPSK, 16QAM, 32QAM, and 64QAM. Results show that high identification accuracy can be achieved, even when OSNR is lower than that required for the 20% forward error correction (FEC) threshold of BER=2×10-2 for each format. Furthermore, fast format switching between 64QAM-32QAM and 32QAM-16QAM are demonstrated experimentally for B2B scenario and 900 km SSMF with the proposed MFI technique, respectively.

Frequency offset drift monitoring: enabling simultaneously optimum performance and minimum cost of frequency offset estimation.

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.