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Abstract Background Inhibition of kinases is the ever-expanding therapeutic approach to various types of cancer. Typically, assessment of the treatment response is accomplished by standard, volumetric imaging procedures, performed weeks to months after the onset of treatment, given the predominantly cytostatic nature of the kinase inhibitors, at least when used as single agents. Therefore, there is a great clinical need to develop new monitoring approaches to detect the response to kinase inhibition much more promptly. Noninvasive 1H magnetic resonance spectroscopy (MRS) can measure in vitro and in vivo concentration of key metabolites which may potentially serve as biomarkers of response to kinase inhibition. Methods We employed mantle cell lymphoma (MCL) cell lines demonstrating markedly diverse sensitivity of inhibition of Bruton’s tyrosine kinase (BTK) regarding their growth and studied in-depth effects of the inhibition on various aspects of cell metabolism including metabolite synthesis using metabolomics, glucose and oxidative metabolism by Seahorse XF technology, and concentration of index metabolites lactate, alanine, total choline and taurine by 1H MRS. Results Effective BTK inhibition profoundly suppressed key cell metabolic pathways, foremost pyrimidine and purine synthesis, the citrate (TCA) cycle, glycolysis, and pyruvate and glutamine/alanine metabolism. It also inhibited glycolysis and amino acid-related oxidative metabolism. Finally, it profoundly and quickly decreased concentration of lactate (a product of mainly glycolysis) and alanine (an indicator of amino acid metabolism) and, less universally total choline both in vitro and in vivo, in the MCL xenotransplant model. The decrease correlated directly with the degree of inhibition of lymphoma cell expansion and tumor growth. Conclusions Our results indicate that BTK inhibition exerts a broad and profound suppressive effect on cell metabolism and that the affected index metabolites such as lactate, alanine may serve as early, sensitive, and reliable biomarkers of inhibition in lymphoma patients detectable by noninvasive MRS-based imaging method. This kind of imaging-based detection may also be applicable to other kinase inhibitors, as well as diverse lymphoid and non-lymphoid malignancies.

Abstract Arylamines, serving as crucial building blocks in natural products and finding applications in multifunctional materials, are synthesized on a large scale via an electrophilic nitration/reduction sequence. However, the current methods for aromatic C–H amination have not yet attained the same level of versatility as electrophilic nitration. Here we show an extensively investigated transition metal-free and regioselective strategy for the amination of nitrobenzenes, enabling the synthesis of 4-nitro-N-arylamines through C(sp2)-H/N-H cross-coupling between electron-deficient nitroarenes and amines. Mechanistic studies have elucidated that the crucial aspects of these reactions encompass the generation of nitrogen radicals and recombination of nitrobenzene complex radicals. The C(sp2)-N bond formation is demonstrated to be highly effective for primary and secondary arylamines as well as aliphatic amines under mild conditions, exhibiting exceptional tolerance towards diverse functional groups in both nitroarenes and amines (>100 examples with yields up to 96%). Notably, this C(sp2)-H/N-H cross-coupling exhibits exclusive para-selectivity.

ObjectiveNondisplaced femoral neck fractures constitute a substantial portion of these injuries. The optimal treatment strategy between internal fixation (IF) and hemiarthroplasty (HA) remains debated, particularly concerning cost-effectiveness.MethodsWe conducted a cost-effectiveness analysis using a Markov decision model to compare HA and IF in treating nondisplaced femoral neck fractures in elderly patients in China. The analysis was performed from a payer perspective with a 5-year time horizon. Costs were measured in 2020 USD, and effectiveness was measured in quality-adjusted life-years (QALYs). Sensitivity analyses, including one-way and probabilistic analyses, were conducted to assess the robustness of the results. The willingness-to-pay threshold for incremental cost-effectiveness ratio (ICER) was set at $11,083/QALY following the Chinese gross domestic product in 2020.ResultsHA demonstrated higher cumulative QALYs (2.94) compared to IF (2.75) but at a higher total cost ($13,324 vs. $12,167), resulting in an ICER of $6,128.52/QALY. The one-way sensitivity analysis identified the costs of HA and IF as the most influential factors. Probabilistic sensitivity analysis indicated that HA was more effective in 69.3% of simulations, with an ICER below the willingness-to-pay threshold of $11,083 in 58.8% of simulations.ConclusionsHA is a cost-effective alternative to IF for treating nondisplaced femoral neck fractures in elderly patients in mainland China.

Abstract Ligands and additives are often utilized to stabilize low-valent catalytic metal species experimentally, while their role in suppressing metal deposition has been less studied. Herein, an on-cycle mechanism is reported for CoCl2bpy2 catalyzed Negishi-type cross-coupling. A full catalytic cycle of this kind of reaction was elucidated by multiple spectroscopic studies. The solvent and ligand were found to be essential for the generation of catalytic active Co(I) species, among which acetonitrile and bipyridine ligand are resistant to the disproportionation events of Co(I). Investigations, based on Quick-X-Ray Absorption Fine Structure (Q-XAFS) spectroscopy, Electron Paramagnetic Resonance (EPR), IR allied with DFT calculations, allow comprehensive mechanistic insights that establish the structural information of the catalytic active cobalt species along with the whole catalytic Co(I)/Co(III) cycle. Moreover, the acetonitrile and bipyridine system can be further extended to the acylation, allylation, and benzylation of aryl zinc reagents, which present a broad substrate scope with a catalytic amount of Co salt. Overall, this work provides a basic mechanistic perspective for designing cobalt-catalyzed cross-coupling reactions.

Numerous single-image dehazing algorithms have been developed, employing a spectrum of techniques ranging from intricate physical computations to state-of-the-art deep-learning methodologies. However, conventional deep-learning approaches, particularly those based on standard convolutional neural networks (CNNs), often result in the persistence of residual fog patches when applied to images featuring high fog concentration or heterogeneous fog distribution. In response to this challenge, we propose an innovative solution known as the multi-feature fusion image dehazing network (MFID-Net). This approach employs an end-to-end methodology to directly capture the mapping relationship between hazy and fog-free images. Central to our approach is the introduction of a novel multi-feature fusion (MF) module, strategically designed to address channel and pixel characteristics in regions with uneven or high fog concentrations. Notably, this module achieves effective haze reduction while minimizing computational resources, thereby mitigating the issue of residual fog patches. Experimental results underscore the superior performance of our algorithm compared to similar dehazing methods, as evidenced by higher scores in structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and computational velocity. Moreover, MFID-Net exhibits significant advancements in restoring details within expansive monochromatic areas, such as skies and white walls.

AbstractCircular ribonucleic acids (circRNAs) are single-stranded RNAs with covalently closed-loop structures that lack terminal 5′ caps and 3′ poly-(A) tails. Recent evidence confirmed that some circRNAs can be translated into proteins. More importantly, there is a growing body of evidence that dysregulation of circRNAs is closely associated with the development of human diseases, especially malignant tumors. CircZNF609 is a novel circular RNA with an open reading frame and an internal ribosomal entry site that can interact with microRNAs and mRNAs and be translated into proteins. Studies have shown that circZNF609 is abnormally expressed in various human diseases, especially malignant tumors such as hepatocellular carcinoma, nasopharyngeal carcinoma, colorectal cancer and glioma, which lead to the occurrence and development of diseases. This article attempts to provide a comprehensive overview of the structural properties of circZNF609 discovered so far and focuses on the pathogenesis of circZNF609 in human diseases and its potential clinical application value. Finally, we elaborate on the directions of future investigations of this molecule.

1,2-Aminoxygenation of alkenes without extra oxidant is a practical yet challenging way to prepare β-amino alcohols. Here, the authors report an electro-oxidative route achieving such a goal with H2 evolution, exhibiting broad scope and application potential.

Precipitation nowcasting is mainly achieved by the radar echo extrapolation method. Due to the timing characteristics of radar echo extrapolation, convolutional recurrent neural networks (ConvRNNs) have been used to solve the task. Most ConvRNNs have been proven to perform far better than traditional optical flow methods, but they still have fatal problems. These models lack differentiation in the prediction of echoes of different intensities, which leads to the omission of responses from regions with high intensities. Moreover, because it is difficult for these models to capture long-term feature dependencies among multiple echo maps, the extrapolation effect declines sharply over time. This paper proposes an embedded multi-layer attention module (MLAM) to address the shortcomings of ConvRNNs. Specifically, an MLAM mainly enhances attention to critical regions in echo images and the processing of long-term spatiotemporal features through the interaction between input and memory features in the current moment. Comprehensive experiments were conducted on the radar dataset HKO-7 provided by the Hong Kong Observatory and the radar dataset HMB provided by the Hunan Meteorological Bureau. Experiments show that ConvRNNs embedded with MLAMs achieve more advanced results than standard ConvRNNs.

Along with the rapid development of connected vehicle communication technology, describing the vehicle following driving status becomes gradually complicated. Driver behavior, vehicle type, and road factors affect vehicle speed, and the following distance reflects variability. In this paper, a nonlinear following distance model is constructed to characterize this variability. The model is based on the full speed difference model (FVD), and introduces the headway time distance coefficient, the following vehicle type coefficient, the communication advance response parameter reflecting the driver’s personal characteristics, and the slope coefficient and curve curvature coefficient reflecting the road conditions, etc., and analyzes to obtain the stability conditions of the model. MATLAB is applied to numerical simulation experiments of the model, and the results show that the model can better describe the variability of following headway due to driver attributes, vehicle type, slope and curve in the connected vehicle scenario, thus providing a reference for traffic flow control and management.

Abstract Purpose The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms. Materials and methods 1760 anonymous digital X-ray images of real patients between January 2017 and June 2020 were collected for this experiment. In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people. Results Among the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively. Conclusion The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.

The non-coplanar lasers on both sides of the rail during full-section rail profile measurement based on line-structured light vision will cause the measured profile to be distorted, resulting in measurement errors. Currently, in the field of rail profile measurement, there are no effective methods for evaluating laser plane attitude, and it is impossible to determine the degree of laser coplanarity quantitatively and accurately. This study proposes an evaluation method based on fitting planes in response to this problem. Real-time fitting of laser planes with three planar targets of different heights provides information about the laser plane attitude on both sides of the rails. On this basis, laser coplanarity evaluation criteria were developed to determine whether the laser planes on both sides of the rails are coplanar. Using the method in this study, the laser plane attitude can be quantified and accurately assessed on both sides, effectively resolving the problem with traditional methods that can only assess the laser plane attitude qualitatively and roughly, thereby providing a solid foundation for calibration and error correction of the measurement system.

Transition metal-catalyzed regioselective difunctionalizations of alkenes with two different functional groups are useful for preparing organic compounds, but the construction of two new C–C bonds is challenging. Here, the authors report cobalt-catalyzed regioselective difluoroalkylarylation of alkenes with solid arylzinc pivalates and difluoroalkyl bromides, through a cascade Csp3 ‒Csp3/Csp3 ‒Csp2 bond formation.

Abstract Ibrutinib inhibits Bruton tyrosine kinase while venetoclax is a specific inhibitor of the anti-apoptotic protein BCL2. Both drugs are highly effective as monotherapy against chronic lymphocytic leukemia (CLL), and clinical trials using the combination therapy have produced remarkable results in terms of rate of complete remission and frequency of undetectable minimal residual disease. However, the laboratory rationale behind the success of the drug combination is still lacking. A better understanding of how these two drugs synergize would eventually help develop other rational combination strategies. Using an ex vivo model that promotes CLL proliferation, we show that modeled ibrutinib proliferative responses, but not viability responses, correlate well with patients’ actual clinical responses. Importantly, we demonstrate for the first time that ibrutinib and venetoclax act on distinct CLL subpopulations that have different proliferative capacities. While the dividing subpopulation of CLL responds to ibrutinib, the resting subpopulation preferentially responds to venetoclax. The combination of these targeted therapies effectively reduced both the resting and dividing subpopulations in most cases. Our laboratory findings help explain several clinical observations and contribute to the understanding of tumor dynamics. Additionally, our proliferation model may be used to identify novel drug combinations with the potential of eradicating residual disease.

Due to the importance of SCN-containing heteroarenes, developing novel and green synthetic protocols for the synthesis of SCN-containing compounds has drawn much attention over the last decades. We reported here an electrochemical oxidative cyclization of ortho-vinyl aniline to access various SCN-containing benzoxazines. Mild conditions, an extra catalyst-free and oxidant-free system, and good tolerance for air highlight the application potential of this method.

Text summarization is an information compression technology to extract important information from long text, which has become a challenging research direction in the field of natural language processing. At present, the text summary model based on deep learning has shown good results, but how to more effectively model the relationship between words, more accurately extract feature information and eliminate redundant information is still a problem of concern. This paper proposes a graph neural network model GA-GNN based on gated attention, which effectively improves the accuracy and readability of text summarization. First, the words are encoded using a concatenated sentence encoder to generate a deeper vector containing local and global semantic information. Secondly, the ability to extract key information features is improved by using gated attention units to eliminate local irrelevant information. Finally, the loss function is optimized from the three aspects of contrastive learning, confidence calculation of important sentences, and graph feature extraction to improve the robustness of the model. Experimental validation was conducted on a CNN/Daily Mail dataset and MR dataset, and the results showed that the model in this paper outperformed existing methods.

The problem of foreign object intrusion onto the track bed often occurs in the actual operation process of high-speed railways. To solve the problem, we propose an anomaly detection method for the ballastless track bed, which is based on semantic segmentation. Firstly, we put forward the RFODLab semantic segmentation network according to the randomness of foreign objects distribution, and a small proportion of target pixels in the track image. The segmentation results of track image obtained through this model can be used to obtain the accurate pixel information of foreign objects. To further improve the recall and precision, the channel attention mechanism is introduced for the backbone network of the model to aggregate the context information of images, which achieves the weighted constraints of the model on the area to be recognized. Furthermore, to improve the model performance affected by unbalanced sample category distribution during the anomaly detection, we modify the loss function by balancing distribution of each category. The experimental results show that our proposed method can effectively segment various types of anomalies on the ballastless track bed including broken elastic strips, animal carcasses, and fallen pieces. The precision of anomaly detection on the test set can reach 90% while the recall can be maintained at more than 95%. The anomaly detection results on actual lines also verify the effectiveness of the method.

At present, the comprehensive inspection trains of all countries in the world are equipped with an environmental video inspection system. The running environment is monitored by manual analysis of the video data, which is labor intensive and inefficient. In this paper, we propose a fast and intelligent inspection method for high-speed railway infrastructure monitoring. A railway environment video is acquired by an imaging device installed on a high-speed integrated detection train, and the forward-looking video is used to establish a panoramic imaging model by constructing the rectangular sampling area having a dynamic width from each frame of the video sequence, which covers the four side scenes of the railway and generate four panoramic images through image stitching technology. The experimental analysis indicates that proposed method makes the examination and analysis of video faster, simpler and more intelligent, which not only simplifies video content and reduces the time cost for users to browse video; but is also more feasible to run automatic identification algorithm on the compressed panoramic image for realizing the automatic intelligent detection of railway environment anomalies.

Most previous recurrent neural networks for spatiotemporal prediction have difficulty in learning the long-term spatiotemporal correlations and capturing skip-frame correlations. The reason is that the recurrent neural networks update the memory states only using information from the previous time step node and the networks tend to suffer from gradient propagation difficulties. We propose a new framework, KeyMemoryRNN, which has two contributions. The first is that we propose the KeyTranslate Module to extract the most effective historical memory state named keyword state, and we propose the KeyMemory-LSTM which uses the keyword state to update the hidden state to capture the skip-frame correlation. In particular, KeyMemoryLSTM has two training stages. In the second stage, KeyMemoryLSTM adaptively skips the update of sometime step nodes to build a shorter memory information flow to alleviate the difficulty of gradient propagation to learn the long-term spatiotemporal correlations. The second is that both KeyTranslate Module and KeyMemoryLSTM are flexible additional modules, so we can apply them to most RNN-based prediction networks to build KeyMemoryRNN with different base network. The KeyMemoryRNN achieves the state-of-the-art on three spatiotemporal prediction tasks, and we provide ablation studies and memory analysis to verify the effectiveness of KeyMemoryRNN.

Enriching the chemical space of polycyclic heterocycles is of high value in chemical biology. Here, the authors report an electrooxidative protocol for the regioselective dearomative annulation of indoles and benzofurans under undivided cell conditions and obtain five- to eight-membered fused heterocycles.

Alcohol oxidation to carbonyl compounds is a very useful functional group transformation in organic synthesis. Here, the authors perform the direct electrochemical oxidation of various alcohols to the corresponding ketones in a continuous-flow reactor without external oxidants, base or mediators.

Oxidative C–H/N–H cross-coupling is a convenient method to construct C–N bonds, however this is rarely achieved in a sustainable manner. Here, the authors show an oxidant-free electrochemical C–H/N–H cross-coupling between electron-rich arenes and diarylamines to afford triarylamines with exclusive para-selectivity.

Alcohols are very useful building blocks in organic synthesis, however C–H functionaliztion in presence of free –OH groups is highly challenging. Here, the authors report a selective visible light-promoted α-functionalization of free alcohols with heteroarenes mediated by Selectofluor.

Background Real-world data (RWD) from the Society for Vascular Surgery Vascular Quality Initiative (VQI) registry has been used to support US Food and Drug Administration (FDA) regulatory decisions regarding vascular devices. The variables of cost and time needed for these registry-based studies have not been previously compared to traditional, independent, industry studies that would otherwise have been conducted to support regulatory decisions.Objectives To determine the potential value (cost and time saving and return on investment) created by device evaluation studies using the VQI registry infrastructure.Methods We compared studies that used data from the VQI registry with estimated costs of independent industry studies (counterfactual studies) using an established model using design specifications determined by FDA reviewers.Results We analyzed the initial six studies evaluating vascular devices for regulatory decisions using data from the VQI registry that generated evidence for four device manufacturers. Return on investment for these studies was estimated to be 143% and cost saving as 59% based on an actual per patient (with 5-year follow-up) cost of US$11K using VQI data versus US$26K from the counterfactual when averaged across all studies. Significant enrollment time savings (45%–71%) were also realized compared with industry-based estimates.Conclusions The use of RWD from the VQI registry in this study and the transcatheter valve treatment coordinated registry network in a prior study indicates that substantial value was added to device evaluation projects by the reuse of registry data, with additional potential savings if linked claims data can be used instead of costly long-term in-person follow-up.

MIP is a candidate tumour suppressor protein found in hepatocellular carcinoma. It can interact with different proteins in different subcellular locations. However, antibodies against MIP for testing purposes are still not available and this has hindered the research on MIP protein. In this study, the full coding sequence of MIP protein was cloned into the prokaryotic expression vector pET-28a. The recombinant protein his-MIP was expressed by IPTG (isopropyl β-D-1-thiogalactopyranoside) induction and purified by nickel-affinity chromatography, then used to immunize New Zealand rabbits to obtain a polyclonal antibody against MIP. The sensitivity and specificity of the antibody were evaluated by enzyme-linked immunosorbent assay and western blot. We used the obtained antibody to detect endogenous MIP protein in hepatocellular carcinoma (HCC) cells and normal human hepatic cells L-O2 and found decreased MIP expression in HCC cells. The immunohistochemistry results further showed that the expression level of MIP protein in HCC tissues was lower than that in the corresponding adjacent non-cancerous tissues. The MIP protein was distributed mainly in the cytoplasm in HCC. In adjacent tissues, however, high expression of MIP was detected in the nucleus in addition to the cytoplasm. The prepared polyclonal antibody against MIP provides a useful tool for further research on the functions of MIP.

Functionalization of unactivated C–H bonds is an attractive strategy to introduce functionality without the need for pre-functionalized starting materials. Here the authors combine cobalt catalysis with photoredox catalysis, allowing arene-azole cross-coupling under oxidant-free conditions.

At present, the method of two-dimensional image recognition is mainly used to detect the abnormal fastener in the rail-track inspection system. However, the too-tight-or-too-loose fastener condition may cause the clip of the fastener to break or loose due to the high frequency vibration shock, which is difficult to detect from the two-dimensional image. In this practical application background, 3D visual detection technology provides a feasible solution. In this paper, we propose a fundamental multi-source visual data detection method, as well as an accurate and robust fastener location and nut or bolt segmentation algorithm. By combining two-dimensional intensity information and three-dimensional depth information generated by the projection of line structural light, the locating of nut or bolt position and accurate perception of height information can be realized in the dynamic running environment of railway. The experimental results show that the static measurement accuracy in the vertical direction using the structural light vision sensor is 0.1 mm under the laboratory condition, and the dynamic measurement accuracy is 0.5 mm under the dynamic train running environment. We use dynamic template matching algorithm to locate fasteners from 2D intensity map, which achieves 99.4% accuracy, then use the watershed algorithm to segment the nut and bolt from the corresponding depth image of located fastener. Finally, the 3D shape of the nut and bolt is analyzed to determine whether the nut or bolt height meets the local statistical threshold requirements, so as to detect the hidden danger of railway transportation caused by too loose or too tight fasteners.

miR-155 plays critical roles in numerous physiological and pathological processes, however, its function in the regulation of blood glucose homeostasis and insulin sensitivity and underlying mechanisms remain unknown. Here, we reveal that miR-155 levels are downregulated in serum from type 2 diabetes (T2D) patients, suggesting that miR-155 might be involved in blood glucose control and diabetes. Gain-of-function and loss-of-function studies in mice demonstrate that miR-155 has no effects on the pancreatic β-cell proliferation and function. Global transgenic overexpression of miR-155 in mice leads to hypoglycaemia, improved glucose tolerance and insulin sensitivity. Conversely, miR-155 deficiency in mice causes hyperglycemia, impaired glucose tolerance and insulin resistance. In addition, consistent with a positive regulatory role of miR-155 in glucose metabolism, miR-155 positively modulates glucose uptake in all cell types examined, while mice overexpressing miR-155 transgene show enhanced glycolysis, and insulin-stimulated AKT and IRS-1 phosphorylation in liver, adipose tissue or skeletal muscle. Furthermore, we reveal these aforementioned phenomena occur, at least partially, through miR-155-mediated repression of important negative regulators (i.e. C/EBPβ, HDAC4 and SOCS1) of insulin signaling. Taken together, these findings demonstrate, for the first time, that miR-155 is a positive regulator of insulin sensitivity with potential applications for diabetes treatment.

Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA) method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR) model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality.