Methods for direct C-H trifluoromethoxylation of arenes and heteroarenes are rare, despite the importance of trifluoromethoxylated compounds for pharmaceuticals, agrochemicals, and material sciences. ...Especially selective C-H trifluoromethoxylation of pyridines remains a formidable challenge. Here we show a general late-stage C-H trifluoromethoxylation of arenes and heteroarenes as limiting reagent with trifluoromethoxide anion. The reaction is mediated by silver salts under mild reaction conditions, exhibiting broad substrate scope and wide functional-group compatibility. In addition, ortho-position selective C-H trifluoromethoxylation of pyridines is observed. The method is not only applicable to the gram-scale synthesis of trifluoromethoxylated products but also allows efficient late-stage C-H trifluoromethoxylation of marketed small-molecule drugs, common pharmacophores and natural products.
PIK3CA is the second most frequently mutated gene in cancers and is extensively studied for its role in promoting cancer cell resistance to chemotherapy or targeted therapy. However, PIK3CA functions ...have mostly been investigated at a lower-order genetic level, and therapeutic strategies targeting PIK3CA mutations have limited effects. Here, we explore crucial factors interacting with PIK3CA mutations to facilitate a significant marginal survival effect at the higher-order level and identify therapeutic strategies based on these marginal factors.
Mutations in stomach adenocarcinoma (STAD), breast adenocarcinoma (BRCA), and colon adenocarcinoma (COAD) samples from The Cancer Genome Atlas (TCGA) database were top-selected and combined for Cox proportional-hazards model analysis to calculate hazard ratios of mutation combinations according to overall survival data and define criteria to acquire mutation combinations with considerable marginal effects. We next analyzed the PIK3CA + HMCN1 + LRP1B mutation combination with marginal effects in STAD patients by Kaplan-Meier, transcriptomic differential, and KEGG integrated pathway enrichment analyses. Lastly, we adopted a connectivity map (CMap) to find potentially useful drugs specifically targeting LRP1B mutation in STAD patients.
Factors interacting with PIK3CA mutations in a higher-order manner significantly influenced patient cohort survival curves (hazard ratio (HR) = 2.93, p-value = 2.63 × 10
). Moreover, PIK3CA mutations interacting with higher-order combination elements distinctly differentiated survival curves, with or without a marginal factor (HR = 0.26, p-value = 6.18 × 10
). Approximately 3238 PIK3CA-specific higher-order mutational combinations producing marginal survival effects were obtained. In STAD patients, PIK3CA + HMCN1 mutation yielded a substantial beneficial survival effect by interacting with LRP1B (HR = 3.78 × 10
, p-value = 0.0361) and AHNAK2 (HR = 3.86 × 10
, p-value = 0.0493) mutations. We next identified 208 differentially expressed genes (DEGs) induced by PIK3CA + HMCN1 compared with LRP1B mutation and mapped them to specific KEGG modules. Finally, small-molecule drugs such as geldanamycin (connectivity score = - 0.4011) and vemurafenib (connectivity score = - 0.4488) were selected as optimal therapeutic agents for targeting the STAD subtype with LRP1B mutation.
Overall, PIK3CA-induced marginal survival effects need to be analyzed. We established a framework to systematically identify crucial factors responsible for marginal survival effects, analyzed mechanisms underlying marginal effects, and identified related drugs.
Despite the growing importance of fluorinated organic compounds in pharmaceuticals, agrochemicals, and materials science, the introduction of fluorine into organic molecules is still a challenge, and ...no catalytic fluorocarbonylation of aryl/alkyl boron compounds has been reported to date. Herein, we present the development of palladium and phosphine synergistic redox catalysis of fluorocarbonylation of potassium aryl/alkyl trifluoroborate. Trifluoromethyl arylsulfonate (TFMS), which was used as a trifluoromethoxylation reagent, an easily handled and bench-scale reagent, has been employed as an efficient source of COF
. The reaction operates under mild conditions with good to excellent yields and tolerates diverse complex scaffolds, which allows efficient late-stage fluorocarbonylation of marked small-molecule drugs. Mechanistically, the key intermediates of labile Brettphos-Pd(II)-OCF
complex and difluoro-Brettphos were synthesized and spectroscopically characterized, including X-ray crystallography. A detailed reaction mechanism involving the synergistic redox catalytic cycles Pd(II)/(0) and P(III)/(V) was proposed, and multifunction of phosphine ligand was identified based on
F NMR, isotope tracing, synthetic, and computational studies.
Siamese networks have been extensively studied in recent years. Most of the previous research focuses on improving accuracy, while merely a few recognize the necessity of reducing parameter ...redundancy and computation load. Even less work has been done to optimize the runtime memory cost when designing networks, making the Siamese-network-based tracker difficult to deploy on edge devices. In this paper, we present SiamMixer, a lightweight and hardware-friendly visual object-tracking network. It uses patch-by-patch inference to reduce memory use in shallow layers, where each small image region is processed individually. It merges and globally encodes feature maps in deep layers to enhance accuracy. Benefiting from these techniques, SiamMixer demonstrates a comparable accuracy to other large trackers with only 286 kB parameters and 196 kB extra memory use for feature maps. Additionally, we verify the impact of various activation functions and replace all activation functions with ReLU in SiamMixer. This reduces the cost when deploying on mobile devices.
Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on ...deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices.
In recent years, a large number of quantization schemes have been proposed for compressing convolutional neural networks (CNN). However, most of them have the following problems: 1) when there are ...outliers in the weight, post-training quantization cannot obtain the ideal effect, and the accuracy loss is unavoidable; 2) quantizing the non-maximum suppression (NMS) stage of oriented object detection networks is non-trivial so that such networks are difficult to deploy on edge computing devices that only support integer operations. In this letter, we propose the outlier-aware quantization (OAQ) to boost the robustness of the post-training quantization method. Besides, we design a multilayer perceptron network to approximate the intersection-over-union (IoU) of rotated boxes, making the NMS stage can be deployed on integer-arithmetic-only devices. The experiment results demonstrate that our solution outperforms the widely used post-training quantization method. Meanwhile, to the best of our knowledge, this is the first study that focuses on the optimization and quantization of the NMS stage of oriented object detection networks.
The increasing household loads make series arc faults more complex, which are difficult to be detected by traditional circuit breakers and lead to the frequent occurrence of residential fire ...accidents. In this paper, a comprehensive approach of complex load recognition and series arc detection is proposed on the basis of principal component analysis and support vector machine (PCA-SVM) combination model. Several typical loads were selected and analyzed, especially nonlinear and complex loads like power electronics load and multi-state load. Three time-domain parameters, maximum slip difference (MSD), zero current period (ZCP) and maximum Euclidean distance (MED), and nine frequency-domain harmonics information are collected to complex waveforms. To decrease the computation cost and further to enhance the response velocity, all the time-domain and frequency-domain information were blended and dimensionally reduced to three parameters by principal component analysis (PCA). Prior to the series arc detection, load recognition is trained and completed with the artificial intelligence (AI) algorithm. At last, the comprehensive model of load recognition and series arc detection is achieved based on a support vector machine (SVM). The accuracy of load recognition and series arc detection reaches 99.1% and 99.3%, respectively, demonstrating the excellent performances of the intelligent approach to diagnose the series arcing activities in modern household applications.
Methane is one of the indicative gases in power transformer oil, and the detection of methane dissolved in oil with high accuracy is of great importance for dissolved gases analysis and fault ...diagnosis inside power transformers. Based on the Beer-Lambert spectral absorption law, dissolved methane detection with tunable diode laser absorption spectrum (TDLAS) is proposed in this paper for the advantages of high sensitivity and resolution. On the basis of wavelength modulation spectroscopy, a specialized TDLAS system was established. To meet the actual needs of field testing, the anti-vibration design of an integrated Herriott cell and a gas pressure (P)/temperature (T) setting are worked out. Photodetector, collimator, and Herriott cell are integrated into one component to reduce the effects of vibration. It is investigated that the temperature has little effect on the second harmonic amplitude in the range of 30 °C ~ 50 °C, and the vacuum pressure is reasonably set at about 1 kPa. Experimental results showed that the resolution of sensitivity could be reached as 6.8 mV/(μL/L), the maximum deviation was less than ±4 μL/L, and the response time is less than 5 min. In the end, field application was also carried out, proving it is a prospective online sensing technique to serve oil-immersed power transformers better.
As an important function of the advanced driver assistance system (ADAS), the reversing assistant (RA) achieves trajectory retracing by applying accurate position estimation and tracking control. To ...overcome the problem of the modeling complexity in dead reckoning for the reversing assistant function, the heading angular rate is compensated by using the extreme learning machine (ELM) to improve the positioning accuracy. In addition, considering the time delay of the steering system, a tracking controller with a feed-forward of the recorded steering angle and a self-tuning PID feedback controller is designed based on the preview-and-following scheme. Vehicle experiments under various reversing scenarios prove that the proposed positioning method and tracking control scheme are effective, the overall lateral error is less than 10 cm, and the heading angle error is less than 1°, which meets the requirements of performance indicators.
Calcium sulfate whisker (CSW) was prepared from flue gas desulfurization (FGD) gypsum by recrystallization method, and then was employed in preparing ZnO/CSW photocatalysts by impregnation method. ...CSW and ZnO/CSW were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), UV-Vis diffuse reflectance spectroscopy (UV-Vis DRS) and photoluminescence spectrum (PL). The photocatalytic activity of various ZnO/CSW photocatalysts was evaluated by photocatalytic degradation of methylene blue (MB) under simulated sunlight irradiation. The results showed that various ZnO/CSW photocatalysts exhibited much higher photocatalytic activity than pure ZnO and CSW. Among various ZnO/CSW photocatalysts, the photocatlytic activity of ZnO/CSW increased as increasing the ZnO loading amount from 1.2% to 8.7%, but decreased when the ZnO loading amount was beyond 8.7% due to the increasing crystalline size of ZnO and recombination of photogenerated hole/electron pairs. Besides ZnO loading amount, MB initial concentration and the dosage of photocatalyst also had significant influence on MB degradation rate, and MB degradation rate over ZnO/CSW reached 95.4% under optimum conditions. Kinetics study revealed that the photocatalytic degradation of MB over ZnO/CSW can be described by the pseudo-first-order kinetic model, and the apparent rate constant k versus ZnO loading amount L, MB initial concentration C
0
and the dosage of photocatalyst D can be described as: k=0.5237L
0.5193
C
0
−0.3074
D
0.4589
.
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