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Ferroptosis has emerged as a potential mechanism for enhancing the efficacy of chemotherapy in cancer treatment. By suppressing nuclear factor erythroid 2-related factor 2 (Nrf2), cancer cells may lose their ability to counteract the oxidative stress induced by chemotherapy, thereby becoming more susceptible to ferroptosis. In this study, we investigate the potential of penexanthone A (PXA), a xanthone dimer component derived from the endophytic fungus Diaporthe goulteri, obtained from mangrove plant Acanthus ilicifolius, to enhance the therapeutic effect of cisplatin (CDDP) on colorectal cancer (CRC) by inhibiting Nrf2. The present study reported that PXA significantly improved the ability of CDDP to inhibit the activity of and induce apoptosis in CRC cells. Moreover, PXA was found to increase the level of oxidative stress and DNA damage caused by CDDP. In addition, the overexpression of Nrf2 reversed the DNA damage and ferroptosis induced by the combination of PXA and CDDP. In vivo experiments using zebrafish xenograft models demonstrated that PXA enhanced the therapeutic effect of CDDP on CRC. These studies suggest that PXA enhanced the sensitivity of CRC to CDDP and induce ferroptosis by targeting Nrf2 inhibition, indicating that PXA might serve as a novel anticancer drug in combination chemotherapy. [ABSTRACT FROM AUTHOR]

Encephalitis and acute disseminated encephalomyelitis (ADEM) are often caused or triggered by viruses—but the specific pathogen commonly remains unidentified in routine care. We explored the use of viral metagenomic next-generation sequencing (mNGS) in addition to PCR testing of non-invasive stool samples to see if unbiased testing could potentially increase diagnostic yield. To identify specific clinical cases at the point of care, we took advantage of a previously published digital app allowing instant clinical case classification based on consensus case criteria, the VACC-Tool. This hospital-based prospective digital surveillance program assessed 100 pediatric patients (mean age: 11 years, range: 0.15–17.85; 49% male) with case-confirmed encephalitis and/or ADEM. Analysis of case classification at the point of care revealed that in routine care, 96% of confirmed encephalitis/ADEM cases had been missed. Overall agreement of routine care diagnoses with digital encephalitis/ADEM case classification was <50%. Also in routine care, only 13% of cases held a virus-related diagnosis, i.e., herpesvirus (n = 8) and enterovirus infection (n = 5). Use of mNGS increased the yield of virus detection by 77% (n = 23 virus hits). Specifically, mNGS identified 10 additional virus species beyond herpes- and enteroviruses. Of the additional 23 virus hits detected with mNGS, PCR confirmation was possible post hoc in 14 cases (61%). Linking digital case classification, mNGS, and PCR testing may not be feasible in routine care at this point but may help to provide hints to the pathogenesis of encephalitis/ADEM in childhood, warranting further research and exploration. [ABSTRACT FROM AUTHOR]

Two-dimensional intrinsic magnetic materials with high Curie temperature are promising candidates for next-generation spintronic devices. In this work, we design two kinds of two-dimensional transition metal nitrides, VN2 and FeN2, both with a hexagonal honeycomb lattice. Based on the formation energy, and phonon spectra calculations as well as the molecular dynamics simulations, their structural stability is demonstrated. Then, we determine the ferromagnetic ground states of VN2 and FeN2 monolayers through the energy calculations, and the Curie temperatures of 222 K and 238 K are estimated by solving the Heisenberg model using the Monte Carlo simulation method. Hence, the VN2 and FeN2 monolayers are demonstrated to be new two-dimensional ferromagnetic materials with high temperature ferromagnetism or large-gap half-metallicity. [ABSTRACT FROM AUTHOR]

Respiratory viral infections (RVIs) are common reasons for healthcare consultations. The inpatient management of RVIs consumes significant resources. From 2009 to 2014, we assessed the costs of RVI management in 4776 hospitalized children aged 0–18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by detailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU ('non-ICU') versus management requiring ICU care ('ICU') added up to EUR 2767.14 (non-ICU) vs. EUR 29,941.71 (ICU) for influenza, EUR 2713.14 (non-ICU) vs. EUR 16,951.06 (ICU) for RSV infections, and EUR 2767.33 (non-ICU) vs. EUR 14,394.02 (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all eight RVIs studied: influenza, RSV, hRV, adenovirus (hAdV), metapneumovirus (hMPV), parainfluenza virus (hPIV), bocavirus (hBoV), and seasonal coronavirus (hCoV) infections. ICU costs for influenza, however, exceeded all other RVIs. At the time of the study, influenza was the only RVI with antiviral treatment options available for children, but only 9.8% of influenza patients (non-ICU) and 1.5% of ICU patients with influenza received antivirals; only 2.9% were vaccinated. Future studies should investigate the economic impact of treatment and prevention of influenza, COVID-19, and RSV post vaccine introduction. [ABSTRACT FROM AUTHOR]

Atherosclerosis is a chronic inflammatory disease leading to various vascular diseases. Vascular smooth muscle cell (VSMC) senescence promotes atherosclerotic inflammation and the formation of plaque necrosis core, in part through telomere damage mediated by a high-fat diet. Our previous research found that paeonol, a potential anti-inflammatory agent extracted from Cortex Moutan, could significantly improve VSMCs dysfunction. However, the impact of paeonol on the senescence of VSMCs remains unexplored. This study presents the protective effects of paeonol on VSMCs senescence, and its potential activity in inhibiting the progression of atherosclerosis in vivo and in vitro. Sirtuin 1 (SIRT1) is a nuclear deacetylase involved in cell proliferation, senescence, telomere damage, and inflammation. Here, SIRT1 was identified as a potential target of paeonol having anti-senescence and anti-atherosclerosis activity. Mechanistic studies revealed that paeonol binds directly to SIRT1 and then activates the SIRT1/P53/TRF2 pathway to inhibit VSMCs senescence. Our results suggested that SIRT1-mediated VSMCs senescence is a promising druggable target for atherosclerosis, and that pharmacological modulation of the SIRT1/P53/TRF2 signaling pathway by paeonol is of potential benefit for patients with atherosclerosis. [ABSTRACT FROM AUTHOR]

A typhoon or hurricane is one of the most destructive high-impact weather events. In this study, the genesis and development processes of Typhoon Nangka (2015), which occurred over the Western Pacific in 2015, were investigated based on the comprehensive observation data from three satellites, i.e., the Himawari-8 satellite, the CloudSat satellite and the Global Precipitation Measurement mission satellite (GPM), focusing on the characteristics of typhoon structure, precipitation and cloud. The results (Results) show that during the developing stage of Typhoon Nangka, the cloud system was relatively complex and changed significantly, with large raindrops dominating the precipitation around the eyewall in the first quadrant, and the convection in the eyewall and outer rainband burst upward to 17 km. In addition, three features were obvious: stratiform precipitation was dominant in the inner rainband, both the precipitation type (stratiform or convective) and intensity were distributed unevenly in the outer rainband, and large water content was located in the warm layer of clouds. Moreover, the collision growth and breakup of water droplets tended to be stable. The precipitation in the typhoon eyewall, inner rainband and outer rainband was significantly different; stratiform precipitation mainly occurred in the inner rainband, while convective precipitation mainly appeared in the eyewall and outer rainband. The cloud system was distributed asymmetrically, and the upper-layer and lower-layer clouds were closely related, dominated by single-layer clouds. There were deep convective clouds in the eyewall, and cirrus clouds with the broadest range across the eyewall. The coverage range of cirrus clouds was close to the radius of the typhoon. There were stratocumulus, altostratus and cumulus in the low levels. [ABSTRACT FROM AUTHOR]

Multiple upwelling systems develop in the Indian Ocean during the summer monsoon. The Sri Lanka dome (SLD), which occurs in the open ocean off the east coast of Sri Lanka from June to September, is distinct from those near the coast. The SLD is characterized by uplifted thermocline and increased chlorophyll concentration. Mechanisms of the upwelling and its biogeochemical response are not well understood. Here, we explored the dynamics of the chlorophyll and sea-to-air CO2 flux in the SLD using ocean color and altimetry remote sensing data, together with other reanalysis products. We found that the occurrence of high chlorophyll concentration and sea-to-air CO2 flux happens along the pathway of the southwest monsoon current (SMC). The annual cycle of chlorophyll in the SLD has a one-month lag relative to that in the southern coast of Sri Lanka. The positive wind stress curl that forms in the SLD during the summer does not fully explain the seasonal chlorophyll maximum. Transport of the SMC, eddy activity, and associated frontal processes also play an important role in regulating the variability in chlorophyll. In the SLD, upwelled subsurface water has excess dissolved inorganic carbon (DIC) relative to the conventional Redfield ratio between DIC and nutrients; thus, upwelling and sub-mesoscale processes determine this region to be a net carbon source to the atmosphere. [ABSTRACT FROM AUTHOR]

Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current sentence's current error correction results rather than considering the results after multiple rounds of correction. In order to address this problem of mismatch between the training and inference processes, we propose a Chinese GEC method based on iterative training and sequence tagging (CGEC-IT). First, in the iterative training phase, we dynamically generate the target tags for each round by using the final target sentences and the input sentences of the current round. The final loss is the average of each round's loss. Next, by adding conditional random fields for sequence labeling, we ensure that the model pays more attention to the overall labeling results. In addition, we use the focal loss to solve the problem of category imbalance caused by the fact that most words in text error correction do not need error correction. Furthermore, the experiments on NLPCC 2018 Task 2 show that our method outperforms prior work by up to 2% on the F0.5 score, which verifies the efficiency of iterative training on the Chinese GEC model. [ABSTRACT FROM AUTHOR]

Relation extraction (RE) is the core link of downstream tasks, such as information retrieval, question answering systems, and knowledge graphs. Most of the current mainstream RE technologies focus on the sentence-level corpus, which has great limitations in practical applications. Moreover, the previously proposed models based on graph neural networks or transformers try to obtain context features from the global text, ignoring the importance of local features. In practice, the relation between entity pairs can usually be inferred just through a few keywords. This paper proposes a keyword detection and context filtering method based on the Self-Attention mechanism for document-level RE. In addition, a Self-Attention Memory (SAM) module in ConvLSTM is introduced to process the document context and capture keyword features. By searching for word embeddings with high cross-attention of entity pairs, we update and record critical local features to enhance the performance of the final classification model. The experimental results on three benchmark datasets (DocRED, CDR, and GBA) show that our model achieves advanced performance within open and specialized domain relationship extraction tasks, with up to 0.87% F1 value improvement compared to the state-of-the-art methods. We have also designed experiments to demonstrate that our model can achieve superior results by its stronger contextual filtering capability compared to other methods. [ABSTRACT FROM AUTHOR]

Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-feature fusion (SPERT-DC). The context area is picked dynamically with the help of threshold in feature selecting layer of the model. It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer. Furthermore, the model in this paper outperforms prior work by up to 1% F1 score on the public dataset, which has verified the efficiency of dynamic context on ERE model. [ABSTRACT FROM AUTHOR]

Featured Application: A conceptual model of aerosol-cloud interaction is first built to explain the aerosol effect on Mei-Yu frontal precipitation, with possible application to reducing the significant uncertainties in weather and climate prediction. The Mei-Yu front is a significantly important summer precipitation system in eastern Asia. In recent years, anthropogenic air pollution over the Yangtze-Huaihe region of China has been aggravating continuously. A cloud-resolving model coupled with an idealized frontal model is used to investigate the response of aerosols on the Mei-Yu frontal precipitation. The results indicate that increasing droplet concentrations lead to significant precipitation enhancement with the current pollution levels in Mei-Yu frontal system. Under the polluted conditions, the enhanced cold-cloud process is of great importance. Moreover, with the "towing" of active cold-cloud process, cold-cloud and warm-cloud processes developed mutually. These account for the complicated and special microphysical mechanism for aerosol impacts on Mei-Yu frontal system. Furthermore, two types of "microphysical-dynamic positive feedback loop" caused by the interactions of various physical processes and effects (direct dynamic effect, frontogenesis effect, and vapor pump effect) can be found in the Mei-Yu precipitation, which in turn reinforce the microphysical processes. The combined effect is to increase Mei-Yu front precipitation. The interaction of microphysical processes and dynamic processes, and the positive feedback loops they create are the main physical mechanisms behind the significant impacts of aerosol on Mei-Yu frontal precipitation. This may also be an important feature of climate change in eastern Asia. [ABSTRACT FROM AUTHOR]

In the application of 3D reconstruction of multi-cameras, it is necessary to calibrate the camera used separately, and at the same time carry out multi-stereo calibration, and the calibration accuracy directly affects the effect of the 3D reconstruction of the system. Many researchers focus on the optimization of the calibration algorithm and the improvement of calibration accuracy after obtaining the calibration plate pattern coordinates, ignoring the impact of calibration on the accuracy of the calibration board pattern coordinate extraction. Therefore, this paper proposes a multi-camera stereo calibration method based on circular calibration plate focusing on the extraction of pattern features during the calibration process. This method preforms the acquisition of the subpixel edge acquisition based on Franklin matrix and circular feature extraction of the circular calibration plate pattern collected by the camera, and then combines the Zhang's calibration method to calibrate the camera. Experimental results show that compared with the traditional calibration method, the method has better calibration effect and calibration accuracy, and the average reprojection error of the multi-camera is reduced by more than 0.006 pixels. [ABSTRACT FROM AUTHOR]

Studies have shown that ordinary color cameras can detect the subtle color changes of the skin caused by the heartbeat cycle. Therefore, cameras can be used to remotely monitor the pulse in a non-contact manner. The technology for non-contact physiological measurement in this way is called remote photoplethysmography (rPPG). Heart rate variability (HRV) analysis, as a very important physiological feature, requires us to be able to accurately recover the peak time locations of the rPPG signal. This paper proposes an efficient spatiotemporal attention network (ESA-rPPGNet) to recover high-quality rPPG signal for heart rate variability analysis. First, 3D depth-wise separable convolution and a structure based on mobilenet v3 are used to greatly reduce the time complexity of the network. Next, a lightweight attention block called 3D shuffle attention (3D-SA), which integrates spatial attention and channel attention, is designed to enable the network to effectively capture inter-channel dependencies and pixel-level dependencies. Moreover, ConvGRU is introduced to further improve the network's ability to learn long-term spatiotemporal feature information. Compared with existing methods, the experimental results show that the method proposed in this paper has better performance and robustness on the remote HRV analysis. [ABSTRACT FROM AUTHOR]

Remote photoplethysmography (rPPG) is a video-based non-contact heart rate measurement technology. It is a fact that most existing rPPG methods fail to deal with the spatiotemporal features of the video, which is significant for the extraction of the rPPG signal. In this paper, we propose a 3D central difference convolutional network (CDCA-rPPGNet) to measure heart rate, with an attention mechanism to combine spatial and temporal features. First, we crop and stitch the region of interest together through facial landmarks. Next, the high-quality regions of interest are fed to CDCA-rPPGNet based on a central difference convolution, which can enhance the spatiotemporal representation and capture rich relevant time contexts by collecting time difference information. In addition, we integrate the attention module into the neural network, aiming to strengthen the ability of the neural network to extract video channels and spatial features, so as to obtain more accurate rPPG signals. In summary, the three main contributions of this paper are as follows: (1) the proposed network base on central difference convolution could better capture the subtle color changes to recover the rPPG signals; (2) the proposed ROI extraction method provides high-quality input to the network; (3) the attention module is used to strengthen the ability of the network to extract features. Extensive experiments are conducted on two public datasets—the PURE dataset and the UBFC-rPPG dataset. In terms of the experiment results, our proposed method achieves 0.46 MAE (bpm), 0.90 RMSE (bpm) and 0.99 R value of Pearson's correlation coefficient on the PURE dataset, and 0.60 MAE (bpm), 1.38 RMSE (bpm) and 0.99 R value of Pearson's correlation coefficient on the UBFC dataset, which proves the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, human-computer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MA-SFV2: Mixed Attention-ShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Human motion classification based on micro-Doppler effect has been widely used in various fields. However, the motion classification performance would be greatly degraded if the wireless environment has non-target micro-motion interference. In this case, the interference signal aliases with the signal of target human motions and then generates cross-terms, making the signals hard to be used to identify target human motions. Existing methods do not consider this non-target micro-motion interference and have poor resistance to such interference. In this paper, we propose a target human motion classification system that can work in the scenarios with non-target micro-motion interference. Specifically, we build a continuous wave radar transceiver working in a low-frequency radar band using the software defined radio equipment Universal Software Radio Peripheral (USRP) N210 to collect signals. Moreover, we use Empirical Mode Decomposition and S-transform successively to remove non-target micro-motion interference and improve the time-frequency resolution of the raw signal. Then, an Energy Aggregation method based on S-method is proposed, which can suppress cross-terms and background noise. Furthermore, we extract a set of features and classify four human motions by adopting Bagged Trees. Extensive experiments using the test-bed show that under the scenarios with non-target micro-motion interference, 97.3% classification accuracy can be achieved. [ABSTRACT FROM AUTHOR]

The use of computers to simulate facial aging or rejuvenation has long been a hot research topic in the field of computer vision, and this technology can be applied in many fields, such as customs security, public places, and business entertainment. With the rapid increase in computing speeds, complex neural network algorithms can be implemented in an acceptable amount of time. In this paper, an optimized face-aging method based on a Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. In this method, an original face image is initially mapped to a personal latent vector by an encoder, and then the personal potential vector is combined with the age condition vector and the gender condition vector through a connector. The output of the connector is the input of the generator. A stable and photo-realistic facial image is then generated by maintaining personalized facial features and changing age conditions. With regard to the objective function, the single adversarial loss of the Generated Adversarial Network (GAN) with the perceptual similarity loss is replaced by the perceptual similarity loss function, which is the weighted sum of adversarial loss, feature space loss, pixel space loss, and age loss. The experimental results show that the proposed method can synthesize an aging face with rich texture and visual reality and outperform similar work. [ABSTRACT FROM AUTHOR]

In this paper, we propose the multiwindow Adaptive S-method (AS-method) distribution approach used in the time-frequency analysis for radar signals. Based on the results of orthogonal Hermite functions that have good time-frequency resolution, we vary the length of window to suppress the oscillating component caused by cross-terms. This method can bring a better compromise in the auto-terms concentration and cross-terms suppressing, which contributes to the multi-component signal separation. Finally, the effective micro signal is extracted by threshold segmentation and envelope extraction. To verify the proposed method, six states of motion are separated by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 95.4% for two cases without interference., Competing Interests: The authors declare no conflicts of interest.

Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver's safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. To identify vehicle steering maneuvers, we focus on the vehicle's angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW) is adopted to recognize the vehicle steering. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers.

Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices' random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.