Search

The expression and activity of transcription factors, which directly mediate gene transcription, are strictly regulated to control numerous normal cellular processes. In cancer, transcription factor activity is often dysregulated, resulting in abnormal expression of genes related to tumorigenesis and development. The carcinogenicity of transcription factors can be reduced through targeted therapy. However, most studies on the pathogenic and drug-resistant mechanisms of ovarian cancer have focused on the expression and signaling pathways of individual transcription factors. To improve the prognosis and treatment of patients with ovarian cancer, multiple transcription factors should be evaluated simultaneously to determine the effects of their protein activity on drug therapies. In this study, the transcription factor activity of ovarian cancer samples was inferred from virtual inference of protein activity by enriched regulon algorithm using mRNA expression data. Patients were clustered according to their transcription factor protein activities to investigate the association of transcription factor activities of different subtypes with prognosis and drug sensitivity for filtering subtype-specific drugs. Meanwhile, master regulator analysis was utilized to identify master regulators of differential protein activity between clustering subtypes, thereby identifying transcription factors associated with prognosis and assessing their potential as therapeutic targets. Master regulator risk scores were then constructed for guiding patients’ clinical treatment, providing new insights into the treatment of ovarian cancer at the level of transcriptional regulation.

Background Hepatocellular carcinoma (HCC) is one of the highly malignant and aggressive gastrointestinal tumors. Anoikis is a specific form of cell death that is closely related to malignant aggressive behavior of tumors. The role and significance of anoikis-related genes (ANRGs) in HCC deserve to be explored. Methods Here, transcriptome profiling and relevant clinical data needed for analysis were collected from public databases. Prognostic model of ANRGs was constructed by using Lasso regression algorithm. Then, patients were given a reasonable risk grouping, and survival analysis was conducted to compare the different survival rates in each risk group. Receiver operating characteristic (ROC) curve was employed to examine the predictive accuracy of the prognostic model. The single sample gene set enrichment (ssGSEA) was carried out to investigate important disease characteristics of each risk group, such as immune status profile and tumor microenvironment differences. The gene set enrichment analysis (GSEA) method was also implemented to complete functional and pathway enrichment analysis. In addition, drug sensitivity analysis and exploration of single cell data for HCC were completed with the aid of online analytical databases. Results We successfully created a prognostic model containing 14 ANRGs, namely: ANXA5, BSG, SKP2, BAK1, PHLDA2, CDKN3, SFN, EZH2, HMGA1, PBK, NRAS, SLC2A1, MAD2L1 and CASP2, and observed a lower overall survival in high-risk group. The ROC curve confirmed good performance of this new model in predicting prognosis. The ssGSEA revealed significant differences in tumor immune microenvironment between different risk groups, with higher activity about cancer related pathways in high-risk group. The expression level of immune checkpoint and m6A genes also differed between risk subgroups. These prognostic genes were also be related to chemotherapy susceptibility. Conclusion The novel prognostic model identified with ANRGs can be applied to prediction prognostic and assessment immune status profile, tumor microenvironment differences and chemosensitivity in HCC. Rational use of the prognostic new model may provide an important reference for individualized treatment of HCC.

The soil temperature (ST) is closely related to the surface air temperature (AT), but their coupling may be affected by other factors. In this study, by using linear analysis and nonlinear causality analysis—convergent cross mapping (CCM) and its time-lagged version (time-lagged CCM), significant effects of the AT on the underlying ST were found, and the time taken to propagate downward to 320 cm can be up to 10 months. Besides the AT, the ST is also affected by memory effects—namely, its prior thermal conditions. At deeper depth (i.e., 320 cm), the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season. At shallower layers (i.e., < 80 cm), the effects of the AT may be blocked by the snow cover, resulting in a poorly synchronous correlation between the AT and the ST. In northeastern China, this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring. Due to the thermal insulation effect of the snow cover, the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period. These findings may be instructive for better understanding ST variations, as well as land−atmosphere interactions.

In the production process of laser welding products, visual inspection is usually employed to recognize welding spot locations and diagnose their quality faults. However, commonly used algorithms fail to succeed in both reliability and computational efficiency, especially when applied to assembly line. In this article, a method based on deep learning algorithm and traditional computer vision (TCV) algorithm is proposed, which achieves quality inspection of laser welding spots in the process of battery production. First, compressed U-shape network (CU-net) is proposed to extract welding pads and welding spots. Then, a template-based method is proposed to confirm the validity of each welding spot. Finally, TCV heuristic algorithms are proposed to achieve three error detections, i.e., welding pad placed obliquely, electrode tab placed over highly, and welding spot welded through. Moreover, we build a Welding Spot Quality Inspection Dataset taken from real assembly line. Compared with other pipelines, including U-net, MaskRCNN, and PSPNet, CU-net shows a significant superiority in both processing speed and detection accuracy. The results of template-based method and TCV heuristic algorithms have shown high computational efficiency and ensured inspection accuracy. The inference time of the whole method is less than 100 ms with the implementation on NVIDIA 1060 and Intel i7-6700.

Reference evapotranspiration (ETo) is one of the most important factors influencing hydrologic and climatic quantities, and increases in ETo are often considered as a primary cause for drying under global warming scenarios. Based on bias‐corrected data from the Half a degree Additional Warming, Prognosis and Projected Impacts (HAPPI) experiment, we investigated changes in ETo and drying across the Yellow River Basin (YRB) for 1.5°C and 2°C stabilized warming, relative to the current decade (2006–2015). We used changes in the climatic water deficit (WD: defined as precipitation (P) minus (ETo) to determine drying and wetting trends. Results projected that mean annual ETo would increase significantly over the whole YRB, under 1.5°C and 2°C warming scenarios, with an increase of 4.54% (1.57–7.59% full uncertainty range) and 7.66% (4.49–10.61%), at 1.5°C and 2°C, respectively, averaged over the YRB for the multi‐model mean. It is unclear whether the YRB source region (the NE region of the Tibet Plateau) would be drier or wetter under 1.5°C and 2°C of warming, although it is likely that the N regions of the YRB, and the whole YRB except for the source region will be drier under 1.5°C and 2°C warming scenarios, respectively. With an additional 0.5°C warming, further intensification of drying is projected in the whole YRB, resulting from a more pronounced increase in ETo, and lesser increase/decrease in P. The increased ETo projected under a 0.5°C additional warming was mainly attributed to rising maximum temperatures (tasmax). These results highlight the importance of ETo, in terms of potential future YRB drying, and our study suggests that limiting future warming to 1.5°C would impact regional drying the least.

Intracranial recordings from the human amygdala and the hippocampus during an emotional memory encoding and discrimination task reveal increased awake sharp-wave/ripples (aSWR) after encoding of emotional compared to neutral stimuli. Further, post-encoding aSWR-locked memory reinstatement in the amygdala and the hippocampus was predictive of later memory discrimination. These findings provide electrophysiological evidence that post-encoding aSWRs enhance memory for emotional events.

Shot boundary detection (SBD) plays an important role in video understanding, since most recent works take the shot as minimal granularity instead of frames for upstream tasks. However, the large variations of hard-cut and gradual-change transitions within shots significantly limit the performance of SBD. To deal with the variations, we propose a multi-task architecture called Transnet++. Transnet++ disentangles the two types of transition and adopts two separate branches to predict them respectively. Two branches share the same video knowledge space and their results are fused for final prediction. Moreover, we propose a spatial attention module (SAM) to enhance the feature representations which suffers from redundant padding region. Meanwhile, a temporal attention module (TAM) is applied to capture the long-term information of the video for alleviating the over-segmentation problem. Experimental results (91.16% f1-score) on Tencent AVS Dataset demonstrate the effectiveness and superiority of Transnet++ for SBD.

Hippocampal-dependent memory consolidation during sleep is hypothesized to depend on the synchronization of distributed neuronal ensembles, organized by the hippocampal sharp-wave ripples (SWRs, 80 to 150 Hz), subcortical/cortical slow-wave activity (SWA, 0.5 to 4 Hz), and sleep spindles (SP, 7 to 15 Hz). However, the precise role of these interactions in synchronizing subcortical/cortical neuronal activity is unclear. Here, we leverage intracranial electrophysiological recordings from the human hippocampus, amygdala, and temporal and frontal cortices to examine activity modulation and cross-regional coordination during SWRs. Hippocampal SWRs are associated with widespread modulation of high-frequency activity (HFA, 70 to 200 Hz), a measure of local neuronal activation. This peri-SWR HFA modulation is predicted by the coupling between hippocampal SWRs and local subcortical/cortical SWA or SP. Finally, local cortical SWA phase offsets and SWR amplitudes predicted functional connectivity between the frontal and temporal cortex during individual SWRs. These findings suggest a selection mechanism wherein hippocampal SWR and cortical slow-wave synchronization governs the transient engagement of distributed neuronal populations supporting hippocampal-dependent memory consolidation.

Land–atmosphere energy and moisture exchange can strongly influence local and regional climate. However, high uncertainty exits in the representation of land–atmosphere interactions in numerical models. The parameterization of surface exchange process is greatly affected by varying the parameter Czil which, however, is typically set to a domain-wide constant value. In this study, we examine the sensitivity of regional climate simulations over China to different surface exchange strengths using three Czil schemes (default without Czil , constant Czil = 0.1, and dynamic canopy-height-dependent Czil -h schemes) in the 13-km-resolution Weather Research and Forecasting model coupled with a Noah land surface model with multi-parameterization options (WRF/Noah-MP). Our results demonstrate that the Czil -h scheme substantially reduces the overestimations of land–atmosphere coupling strength in the other two schemes, and comparisons with the ChinaFLUX observations indicate the capability of the Czil -h scheme to better match the observed surface energy and water variations. The results of the Czil schemes applying to four typical climate zones of China present that the Czil -h simulations are in the closest agreements with the field observations. The Czil -h scheme can narrow the positive discrepancies of simulated precipitation and surface fluxes as well as the negative biases of Ts in areas of Northeast, North China, Eastern Northwest, and Southwest. Especially, the above remarkable improvements produced by the Czil -h scheme are primarily over areas covering short vegetation. Also noted that the precipitation simulated by the Czil -h scheme exhibits more intricate and unclear changes compared with surface fluxes simulations due to the non-local impacts of surface exchange strength resulted from the fluidity of the atmosphere. Overall, our findings highlight the applicability of the dynamical Czil as a better physical alternative to treat the surface exchange process in atmosphere coupling models.

Swarm intelligence algorithms have been widely adopted in solving many highly nonlinear, multimodal problems and have achieved tremendous successes. However, their application on deep neural networks is largely unexplored. On the other hand, deep neural networks, especially convolutional neural network (CNN), have recently achieved breakthroughs in tackling many intractable problems; nevertheless their performance depends heavily on the chosen values of their hyper-parameters, whose fine-tuning is both labor-intensive and time-consuming. In this paper, we propose a novel particle swarm optimization (PSO) variant cPSO-CNN for optimizing the hyper-parameter configuration of architecture-determined CNNs. cPSO-CNN utilizes a confidence function defined by a compound normal distribution to model experts' knowledge on CNN hyper-parameter fine-tunings so as to enhance the canonical PSO's exploration capability. cPSO-CNN also redefines the scalar acceleration coefficients of PSO as vectors to better adapt for the variant ranges of CNN hyper-parameters. Besides, a linear prediction model is adopted for fast ranking the PSO particles to reduce the cost of fitness function evaluation. The experimental results demonstrate that cPSO-CNN performs competitively when compared with several reported algorithms in terms of both CNN hyper-parameter superiority and overall computation cost.

In this paper, a design method for the yarn tension sensor based on a surface acoustic wave is presented. When the yarn tension is applied to the sensor's doubly clamped beam (i.e., piezoelectric substrate), it is linearly related to the output frequency shift, which achieves the purpose of measuring the yarn tension. This paper deduces the functional relationship between the oscillation frequency shift of the sensor and the yarn tension. We put forward the choice of a piezoelectric substrate material, the choice of the piezoelectric die size, and the placement of interdigital transducers (IDTs) as the three key issues to be determined. By using the ANSYS software, we determined that the optimal piezoelectric substrate is quartz, and determined the optimal size of the die and the maximum strain area of the die. When IDTs are placed in the maximum strain area of the piezoelectric substrate, the sensor has the best sensitivity. We have designed and produced the sensor and also have tested the sensor. Experimental results: the range is 0–2 N, the linearity is 1.0112%, the hysteresis is 0.6153%, the repeatability is 1.1067%, the accuracy is 1.6205%, the maximum overload capacity is 200% (i.e., 4 N), and the fracture strength of the substrate is 5 N.

Detecting double JPEG compression is important to forensic experts in identifying the originality and authenticity of images. However, there are some anti-forensic techniques which can evade existing double compression detectors. It is desirable to design a unified approach to address the issues of JPEG forensics and counter-anti-forensics simultaneously, but existing hand-crafted feature based methods and deep learning based methods may fail to satisfy the requirement. In this paper, we present a data-driven approach by using a convolutional neural network (CNN) which takes input from both raw JPEG DCT coefficients and decompressed image pixels. Expert knowledge about JPEG characteristics is incorporated in the CNN design by exploring the intricate relations both within and among DCT subbands and by looking for spatial artifacts both within and among JPEG grids. The CNN is capable of learning deep representations from training data and thus can effectively detect double JPEG compression and its related anti-forensic operations together. The end-to-end CNN that takes into account the information from both DCT domain and spatial domain, shows outstanding performance when compared to prior arts in the experiments. It shows a promising way to address counter-anti-forensic issues without designing specific features for each anti-forensic operation.

Variation in the location of the South Asian High (SAH) in early boreal summer is strongly influenced by elevated surface heating from the Tibetan Plateau (TP) and the Iranian Plateau (IP). Based on observational and ERA-Interim data, diagnostic analyses reveal that the interannual northwestward–southeastward (NW–SE) shift of the SAH in June is more closely correlated with the synergistic effect of concurrent surface thermal anomalies over the TP and IP than with each single surface thermal anomaly over either plateau from the preceding May. Concurrent surface thermal anomalies over these two plateaus in May are characterized by a negative correlation between sensible heat flux over most parts of the TP (TPSH) and IP (IPSH). This anomaly pattern can persist till June and influences the NW–SE shift of the SAH in June through the release of latent heat (LH) over northeastern India. When the IPSH is stronger (weaker) and the TPSH is weaker (stronger) than normal in May, an anomalous cyclone (anticyclone) appears over northern India at 850 hPa, which is accompanied by the ascent (descent) of air and anomalous convergence (divergence) of moisture flux in May and June. Therefore, the LH release over northeastern India is strengthened (weakened) and the vertical gradient of apparent heat source is decreased (increased) in the upper troposphere, which is responsible for the northwestward (southeastward) shift of the SAH in June.

In order to improve the ability of a power system with doubly-fed induction generator (DFIG) to suppress low-frequency oscillations, this paper put forward a damping control strategy based on model predictive control (MPC). It is raised that stochastic subspace identification (SSI) is used to identity a power system with DFIG. Compared with other control strategies, configuring MPC can achieve the purpose of system optimization, which is to enhance the stability of a power system with DFIG. This paper uses the 4-machine 2-area system to compare results of MPC and traditional power system stabilizer (PSS) control in terms of damping characteristics, stability and applicability.

In this Letter, we propose a microstructured in-fiber optofluidic surface-enhanced Raman spectroscopy (SERS) sensor for the initial inspection of uremia through the detection of unlabeled urea and creatinine. As a natural microfluidic device, microstructured hollow fiber has a special structure inside. Through chemical bonds, the SERS substrate can be modified and grown on the surface of the suspended core. Here, the silver nanoparticles (Ag NPs) are embedded on the poly diallyl dimethyl ammonium chloride–modified graphene oxide sheet to achieve the self-assembled SERS substrate. The reduced distance between Ag NPs can increase the strong hot spots that generate enhanced Raman signals. Therefore, it can effectively detect the Raman signal of unlabeled trace uremic toxin analytes (urea, creatinine) inside the optical fiber. The results show that under simulated biophysical conditions, the limit detection (LOD) for urea is 10 − 4 M and the linearity is good, especially at the clinical conventional concentration range ( 2.5 − 6.5 × 10 − 3 M ). In addition, the online Raman detection of creatinine aqueous solution LOD is 10 − 6 M , which also has good linearity. Significantly, this Letter provides a microstructured optofluidic in-fiber Raman sensor for the preliminary detection of uremia, which will have good development prospects in the field of clinical biomedicine.

In the traditional direct visual odometry, it is difficult to satisfy the photometric invariant assumption due to the influence of illumination changes in the real environment, which will lead to errors and drift. This paper proposes an improved direct visual odometry system, which combines luminosity and depth information. The algorithm proposed in this paper uses Kinect 2 to collect RGB images with the corresponding depth information, and selects points with large changes of gray gradient to construct a luminosity error function and uses the corresponding depth information to construct a depth error function. The two error functions are merged into one function and converted into the least squares function of the pose of camera, the Levenberg-Marquardt algorithm is used to solve the camera pose. Finally, the Graph optimization theory and the g2o library are used to optimize the initial pose. Experiments show that the algorithm can reduce the error to a certain extent and reduce the drift caused by illumination changes.

It is reported that surface warming over the Tibetan Plateau (TP) has been faster than the global average, but exactly how much faster remains controversial. This study investigates the time dependency of warming amplification over the TP using CRU_TS4.01 grid dataset during 1961–2016 with a consideration of its consistency with the global average. We find that the magnitude of warming on the TP and its consistency with the global average have been variable. Compared to the global average, the TP warming during 1983–2016 is faster than in the period of 1961–1983, and has a higher consistency with the global average in the period of 1983–2016. The TP warming has a seasonal amplification of 1.1–1.4 times than the global average during 1983–2016, while warming amplification during 1961–1983 is relatively less evident. Generally, the magnitude of warming on the TP is smaller than in the northern high‐latitudes, but larger than in the Southern Hemisphere and the Tropics. Based on current scientific understanding, the possible contribution of snow/ice‐albedo feedback may have played an important role in warming amplification on the TP since the 1980s.

Hippocampal-dependent memory consolidation during sleep is hypothesized to depend on the synchronization of distributed neuronal ensembles, organized by the hippocampal sharp-wave ripples (SWRs, 80-150 Hz) and subcortical/cortical slow-waves (0.5-4 Hz). However, the precise role of SWR-slow-wave interactions in synchronizing subcortical/cortical neuronal activity is unclear. Here, we leverage intracranial electrophysiological recordings from the human hippocampus, amygdala, temporal and frontal cortices, to examine activity modulation and cross-regional coordination during SWRs. Hippocampal SWRs are associated with widespread modulation of high frequency activity (HFA; 70-200 Hz) a measure of local neuronal activation. This peri-SWR HFA modulation is predicted by the coupling between hippocampal SWRs and local subcortical/cortical slow-waves. Finally, local cortical slow-wave phase offsets during SWRs predicted functional connectivity between the frontal and temporal cortex. These findings suggest a selection mechanism wherein hippocampal SWR and cortical slow-wave synchronization governs the transient engagement of distributed neuronal populations supporting hippocampal-dependent memory consolidation.

In this paper, we propose a self-assembled graphene oxide (GO)/Ag NPs SERS Raman sensor based on a novel type of optofluidic MHF as a point-of-care testing (POCT) device. This device is used to diagnose jaundice and its related diseases through on-line detection of free bilirubin content in human serum. This optofluidic Raman sensor is composed of a microstructured hollow fiber (MHF) with a microstructured channel and a suspended core, which allows the sample solution to flow in the channel while interacting with the strong evanescent field on the suspended core. Here, the suspended core was modified by a GO/Ag NPs SERS substrate. When the sample flows through the channel, and interacts with the strong evanescent field generated by the suspended core, the on-line SERS signal is generated and can be coupled back to the suspended core to be detected. In addition, both the electrostatic interaction and interference between GO/Ag NPs with the target enriched bilirubin. The results show that the detection concentration range of bilirubin aqueous, bilirubin in albumin and bilirubin in human blood are all in the range of 2 μM–100 μM, and all have a good linear response. The limit of detection reaches the order of 10−6 M. This rapid, sensitive and label-free SERS Raman sensor of free bilirubin in blood can detect excessive levels of bilirubin in the actual blood environment of the human body, providing a broad prospect for clinically accurate diagnosis of jaundice and related diseases.

Purpose When designing the electrode widths of the electrode-width-weighted (EWW) input interdigital transducers (IDTs) according to the envelope amplitudes of the wavelet function, the EWW wavelet transform processor (WTP) using surface acoustic wave (SAW) devices can be fabricated. The electrode widths have influence on the frequency characteristic of the EWW WTP using SAW devices. The purpose of this research is to solve the influence of the electrode width accuracy on the frequency characteristic of the EWW WTP using SAW devices. Design/methodology/approach In order to solve the influence of the electrode width accuracy on the frequency characteristics of the EWW WTP using SAW devices, the function between the electrode widths and the −3 dB bandwidth is derived. That the −3 dB bandwidth varies as the electrode widths is known according to this function so that the exposure time and the etching are presented as the two key problems. Findings Solutions to these problems are achieved in this study. As long as there is accurate exposure time, the precision IDTs (i.e. the precision electrode widths) will be obtained. The accuracy of the exposure time for the EWW WTP using SAW devices is ±1 per cent. Because the dry etching is a type of etching technology in gas medium, it can etch nanometer lines, even more fine lines, so that the dry etching is used in EWW WTP using SAW devices. Originality/value Research highlights solving the influence of the electrode width accuracy on the frequency characteristic for the EWW WTP using SAW devices; deriving the function between the electrode widths and the −3 dB bandwidth (it is known from this function that the −3 dB bandwidth varies as the electrode widths); and presenting the exposure time and the etching as two key problems.

With the development of intelligent mobile robots, SLAM, especially V-SLAM, as the basic technology of robot localization and navigation, has the advantages of strong adaptability, high precision and strong intelligence compared with the traditional localization technology. It is widely used in smart devices such as unmanned aerial vehicle, automatic driving and sweeping robots. According to different implementation methods, the visual SLAM is divided into: filter V-SLAM based on probability model, key frame BA-based V-SLAM using nonlinear optimization theory, direct tracking of V-SLAM under the assumption of luminosity invariance, space occupying V-SLAM that focuses on building three-dimensional dense maps. This paper focuses on representative systems of various V-SLAMs and gives their respective applicable scenarios and characteristics. Finally, this article forecasts the development of V-SLAM combining with multi-information fusion technology, semantic deep and learning technology.