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Summary Cuttings easily accumulate in the horizontal section during the process of horizontal well drilling, causing tripping difficulty, drillpipe sticking, or even breaking. Existing hole cleaners work while the drillpipe is rotating continuously. In this paper, we describe a vortex cuttings cleaner that combines mechanical cuttings removal with hydraulic cuttings transport without drillpipe rotation. First, a 3D numerical model of vortex cuttings cleaner is established. The flow field characteristics (e.g., velocity distribution and pressure distribution) and energy conversion efficiency of different structures of cuttings cleaner are compared to define an improved structure of cuttings cleaner. Then, the annulus flow field of the cuttings cleaner with improved structure is analyzed to identify the factors influencing the cuttings removal efficiency. Finally, by comparing and analyzing the simulation results and field test results, the influence on cuttings removal efficiency of the vortex cuttings cleaner is clarified. The results show that the energy conversion efficiency of the vortex cuttings cleaner can be improved by reducing the angle of inclined tube and the number of spiral strips or increasing the rotation angle of spiral strips. Moreover, compared with the number of spiral strips, the rotation angle of spiral strips has a more significant influence. The pressure drop of the tool is also investigated. The cuttings removal efficiency increases with the increase of turbine speed, drilling fluid displacement, and viscosity and decreases with the increase of cuttings particle size. Each of these influencing factors has a critical value. When the turbine speed and drilling fluid displacement are higher than the critical values and the cuttings particle size is smaller than its critical value, the cuttings removal efficiency does not increase significantly. After the vortex cuttings cleaner is run into the hole, the circulated cuttings volume increases significantly, and the friction reduces noticeably. The vortex cuttings cleaner has a long failure-free working time, and all parts tripped out of the well are in good condition for future use. This study provides reference for the design and process optimization of hole cleaners in horizontal wells.

Deciduous woody plants like poplar (Populus spp.) have seasonal bud dormancy. It has been challenging to simultaneously delay the onset of bud dormancy in the fall and advance bud break in the spring, as bud dormancy, and bud break were thought to be controlled by different genetic factors. Here, we demonstrate that heterologous expression of the REVEILLE1 gene (named AaRVE1) from Agave (Agave americana) not only delays the onset of bud dormancy but also accelerates bud break in poplar in field trials. AaRVE1 heterologous expression increases poplar biomass yield by 166% in the greenhouse. Furthermore, we reveal that heterologous expression of AaRVE1 increases cytokinin contents, represses multiple dormancy-related genes, and up-regulates bud break-related genes, and that AaRVE1 functions as a transcriptional repressor and regulates the activity of the DORMANCY-ASSOCIATED PROTEIN 1 (DRM1) promoter. Our findings demonstrate that AaRVE1 appears to function as a regulator of bud dormancy and bud break, which has important implications for extending the growing season of deciduous trees in frost-free temperate and subtropical regions to increase crop yield.

Cardiotoxicity is the major side effect of anthracyclines (doxorubicin, daunorubicin, epirubicin, and idarubicin), though being the most commonly used chemotherapy drugs and the mainstay of therapy in solid and hematological neoplasms. Advances in the field of cardio-oncology have expanded our understanding of the molecular mechanisms underlying anthracycline-induced cardiotoxicity (AIC). AIC has a complex pathogenesis that includes a variety of aspects such as oxidative stress, autophagy, and inflammation. Emerging evidence has strongly suggested that the loss of mitochondrial quality control (MQC) plays an important role in the progression of AIC. Mitochondria are vital organelles in the cardiomyocytes that serve as the key regulators of reactive oxygen species (ROS) production, energy metabolism, cell death, and calcium buffering. However, as mitochondria are susceptible to damage, the MQC system, including mitochondrial dynamics (fusion/fission), mitophagy, mitochondrial biogenesis, and mitochondrial protein quality control, appears to be crucial in maintaining mitochondrial homeostasis. In this review, we summarize current evidence on the role of MQC in the pathogenesis of AIC and highlight the therapeutic potential of restoring the cardiomyocyte MQC system in the prevention and intervention of AIC.

Performance management has always been one of the most important problems in human resource management. This paper mainly uses the classic example of "Prisoner's Dilemma" in game theory and the theory of dynamic game, through analyzing the process of horizontal game and vertical game in enterprise development, reveals the direct influence of game behavior in enterprise development, and aims at the adverse influence caused by it in enterprise development. This paper gives a set of suggestions on establishing and improving the game theory criterion in enterprise performance management, and uses the constraint system and game theory criterion to help the company's leadership and management team form an effective equilibrium solution to ensure the completion of the performance management goal, and then achieve the company's strategic goal.

We aim to investigate the underlying neuropathological mechanisms of alternations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 SCD patients, 95 MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of grey matter volumes between regions. Structural connectivity strength and topographic metrics were compared, and the relationship to disease severity was assessed. We found significantly decreased structural connectivity strength of MBN between the SMN and VAN subnetworks was observed for SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency decreased in the MCI group compared to the SCD group. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with global efficiency. In summary, the SCD and MCI groups showed different patterns of disruptions in the morphological grey matter network. The present study could provide new sight into the mechanism of AD in terms of MBN.

BackgroundNonsuicidal self-injury (NSSI) and suicidal attempt (SA) frequently occur during depressed adolescents, but the underlying neurobiological mechanisms are poorly understood. This study sought to investigate neurobiological alternations that underpin adolescent NSSI with or without a history of SA, specifically using functional connectivity (FC) analysis.MethodsWe collected self-reported questionnaires and resting-state fMRI data from depressed adolescents (N = 114), and they were divided into three groups: clinical control (non-self-harm), NSSI-only, and NSSI+SA based on their history of self-harm. Alternations of FC patterns were identified through support vector machine-based classification.ResultsAdolescent NSSI with or without a history of SA exhibited both overlapping and divergent neurobiological alternations. Referring to the alternations of FC in the NSSI group, the alternations of intra-/inter-network FCs among the Control, SalVAtten, Limbic, and Default networks persist in depressed adolescents with a history of both NSSI and SA, while some new alternations appear, including inter-network FCs among the Limbic, DorsAttn, and Visual networks.LimitationsCross-sectional design. Retrospective self-report on the history of self-harm behaviors and severity of depression symptoms. The modest sample size.ConclusionsThe emphasis of this study on the network basis could extend the existing evidence at a circuit level, and may explain an underlying neurobiological mechanism pertaining to the continuum of self-destructiveness in adolescents.

Individual pain sensitivity is modulated by the brain’s structural and functional features, but its heritability remains unclear. This paper conducted a brain-wide genome-wide association study (GWAS) to explore the genetic bases of neuroimage phenotypes of pain sensitivity. In total, 432 normal participants were divided into high and low pain sensitivity groups according to the laser quantitative test threshold. Then, the brain’s gray matter density (GMD) features correlated with pain sensitivity were identified. Next, GWAS was performed on each GMD phenotype using quality-controlled genotypes. Based on the heatmap and hierarchical clustering results, the right insula was identified for further refined analysis in terms of subregions GMD and resting-state functional connectivity (rs-FC) phenotypes. The results indicate that the right insula GMD in the high sensitivity group is significantly lower than that in the low sensitivity group. Also, the TT/TC group at locus rs187974 has lower right insula GMD than the CC group. Further, loci at gene CYP2D6 may lead to a variation of rs-FC between the right insula and left putamen. In conclusion, our study suggests that the right insula and multiple candidate loci may be importantly involved in pain sensitivity modulation, which may guide the future development of precision pain therapeutics.

With sixth generation (6G) communication technologies, target sensing can be finished in milliseconds. The mobile tracking-oriented Internet of Things (MTT-IoT) as a kind of emerging application network can detect sensor nodes and track targets within their sensing ranges cooperatively. Nevertheless, huge data processing and low latency demands put tremendous pressure on the conventional architecture where sensing data is executed in the remote cloud and the short transmission distance of 6G channels presents new challenges into the design of network topology. To cope with the above difficulties, this paper proposes a new resource allocation scheme to perform delicate node scheduling and accurate tracking in multitarget tracking mobile networks. The dynamic tracking problem is formulated as an infinite horizon Markov Decision Process (MDP), where the state space that considers energy consumption, system responding delay, and target important degree is extended. A model-free reinforcement learning is applied to obtain satisfied tracking actions by frequent iterations, in which smart agents interact with the complicated environment directly. The performance of each episode is evaluated by the action-value function in search of the optimal reward. Simulation results demonstrate that the proposed scheme shows excellent tracking performance in terms of energy cost and tracking delay.

Previous functional magnetic resonance imaging (fMRI) studies have shown that brain responses to nociceptive pain, non‐nociceptive somatosensory, visual, and auditory stimuli are extremely similar. Actually, perception of external sensory stimulation requires complex interactions among distributed cortical and subcortical brain regions. However, the interactions among these regions elicited by nociceptive pain remain unclear, which limits our understanding of mechanisms of pain from a brain network perspective. Task fMRI data were collected with a random sequence of intermixed stimuli of four sensory modalities in 80 healthy subjects. Whole‐brain psychophysiological interaction analysis was performed to identify task‐modulated functional connectivity (FC) patterns for each modality. Task‐modulated FC strength and graph‐theoretical‐based network properties were compared among the four modalities. Lastly, we performed across‐sensory‐modality prediction analysis based on the whole‐brain task‐modulated FC patterns to confirm the specific relationship between brain patterns and sensory modalities. For each sensory modality, task‐modulated FC patterns were distributed over widespread brain regions beyond those typically activated or deactivated during the stimulation. As compared with the other three sensory modalities, nociceptive stimulation exhibited significantly different patterns (more widespread and stronger FC within the cingulo‐opercular network, between cingulo‐opercular and sensorimotor networks, between cingulo‐opercular and emotional networks, and between default mode and emotional networks) and global property (smaller modularity). Further, a cross‐sensory‐modality prediction analysis found that task‐modulated FC patterns could predict sensory modality at the subject level successfully. Collectively, these results demonstrated that the whole‐brain task‐modulated FC is preferentially modulated by pain, thus providing new insights into the neural mechanisms of pain processing., This study aimed to reveal whether nociceptive pain could elicit different task‐modulated functional connectivity (FC) patterns from other sensory modalities (non‐nociceptive somatosensory, visual, and auditory). According to the results, nociceptive stimulation exhibited significantly different regional and global task‐modulated FC features from those of the other three sensory modalities, and the task‐modulated FC patterns were predictive of sensory modality at the subject level. These results could provide new insights from the perceptive of task‐modulated brain network into the neural mechanisms of pain processing.

The purpose is to explore the brain’s structural difference in local morphology and between-region networks between two types of peripheral neuropathic pain (PNP): postherpetic neuralgia (PHN) and lower back pain (LBP). A total of 54 participants including 38 LBP and 16 PHN patients were enrolled. The average pain scores were 7.6 and 7.5 for LBP and PHN. High-resolution structural T1 weighted images were obtained. Both grey matter volume (GMV) and morphological connectivity (MC) were extracted. An independent two-sample t-test with false discovery rate (FDR) correction was used to identify the brain regions where LBP and PHN patients showed significant GMV difference. Next, we explored the differences of MC network between LBP and PHN patients and detected the group differences in network properties by using the two-sample t-test and FDR correction. Compared with PHN, LBP patients had significantly larger GMV in temporal gyrus, insula and fusiform gyrus (p p p p

Individual pain sensitivity is influenced by many factors, such as the differentiation of brain regional features and genetic variations. However, its heritability remains largely unclear. The present study conducted a brain-wide genome-wide association study (GWAS) to explore the genetic bases of structural and functional neuroimage phenotypes of pain sensitivity. In total 432 normal participants were firstly divided into high and low pain sensitivity groups according to laser quantitative test threshold and related brain regions’ gray matter density (GMD) features were identified. Consequently, GWAS was performed on each GMD phenotype using quality-controlled genotypes. Based on the heatmap and hierarchical clustering results from GWAS, the right insula was selected for further refined analysis in terms of sub-regions GMD and resting-state functional connectivity (rs-FC) phenotypes. The results indicate that the right insula GMD in the high sensitivity group is significantly lower than the low sensitivity group. Also, the TT/TC group at locus rs187974 may lead to a lower GMD in the right insula than the CC group. Meanwhile, loci at gene CYP2D6 may lead to a variation of rs-FC between the right insula and left putamen. In conclusion, our study suggests multiple candidate loci associated with pain sensitivity that may influence brain phenotypes.

Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional magnetic resonance imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we use 10-minute movie clips as ecologically valid emotion-evoking procedures in n=52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (400 atlas based parcels). Employing machine-learning based decoding and cross validation procedures allowed to develop predictive FC profiles that can accurately distinguish sustained happiness and sadness and that generalize across movies and subjects. Both functional brain network-based and subnetwork-based emotion prediction results suggest that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results show that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibit a strong contribution to emotion prediction. To further estimate the cumulative effect of naturalistic long-term movie-based video-evoking emotions, we divide the 10-min episode into three stages: early stimulation (1 ~ 200 s), middle stimulation (201 ~ 400 s), and late stimulation (401 ~ 600 s) and examine the emotion prediction performance at different stimulation stages. We found that the late stimulation has a stronger predictive ability (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrates that sustained sadness and happiness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidate the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.

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Inferring dynamic functional connectivity (dFC) from functional magnetic resonance imaging (fMRI) is crucial to understand the time-variant functional inter-relationships among brain regions. Because of the sparse property of functional connectivity networks, sparsity-promoting dFC estimation methods, which are mainly based on l 1 -norm regularization, are gaining popularity. However, l 1 -norm regularization cannot provide the maximum sparsity solution as the most natural sparsity promoting norm, the l 0 -norm. But l 0 -norm is seldom used to infer sparse dFC because an efficient algorithm to address the non-convexity problem of l 0 -norm is lacking. In this work, we develop a new l 0 -norm regularization-based inverse covariance estimation method for estimating dFC from fMRI. This novel method employs l 0 -norm regularizations on both spatial and temporal scales to enhance the spatial sparsity and temporal smoothness of dFC estimates. To overcome the non-convexity of l 0 -norm, we further propose an effective optimization algorithm based on the coordinate descent (CD). The performance of the proposed l 0 -norm-based sparse-smooth regularization (L0-SSR) method is examined using a series of synthetic datasets concerning various types of network topology. We further apply the proposed L0-SSR method to real fMRI data recorded in block-design motor tasks from 45 participants for the exploration of task induced dFC. Results on synthetic and real-world fMRI data show that, the L0-SSR method can achieve more accurate and interpretable dFC estimates than conventional l 1 -norm-based dFC estimation methods. Hence, the proposed L0-SSR method could serve as a powerful analytical tool to infer highly complex, variable, and sparse dFC patterns.

Background Dynamic functional connectivity (dFC) based on resting-state fMRI has attracted interest in the field of bipolar disorder (BD), because dFC can better capture the evolving processes of emotion and cognition, which are typically impaired in BD. However, previous dFC studies of BD have typically focused on specific seed brain regions or specific functional brain networks, and they have ignored global dynamic information interaction in the whole brain. This study is aimed to reveal aberrant and interpretable whole-brain dFC patterns of BD. Methods The resting-state fMRI data collected from 35 euthymic BD patients and 30 healthy people. We developed a new dFC inference pipeline, including the sliding-window method, k-means clustering, a new permutation with zero-inflated Poisson regression method, and a similarity analysis for interpretable states, to examine the different patterns of dFC states between BD patients and healthy participants. Results BD patients had significantly more frequent transitions between two specific dFC states, which were respectively close to high-level cognitive networks and low-level sensory networks, than healthy controls (p Limitations The size of samples and other BD types need to be expanded to validate the results of this study. Possible confounding effect of medication. Conclusions This study detected aberrant dFC pattern of BD, which indicated the increased lability of the processes of cognition and emotion in BD, and this finding could improve our understanding of the neuropathological mechanism of BD.

PurposeTo evaluate the association of body stature with ocular biometrics and refraction in preschool children. MethodsA cross-sectional, school-based study was conducted in Shenzhen, China. Preschool children aged 3 to 6 from 10 randomly-selected kindergartens were recruited. Ocular biometric parameters were measured using non-contact partial-coherence laser interferometry. Cycloplegic refractions were obtained by a desktop autorefractor. Body height and weight were measured using standard procedures.ResultsA total of 373 preschoolers were included. Only the right eyes were analyzed. Ocular biometrics, including axial length (AL), anterior chamber depth (ACD), vitreous chamber depth (VCD), corneal radius curvature (CR), and axial length to corneal radius ratio (AL-to-CR ratio), were positively associated with height and weight (pConclusionTaller and heavier preschoolers had eyes with longer AL, deeper vitreous chamber, and flatter cornea. The significant associations between body stature and ocular biometric parameters reveal the driving influence of body development on the growth of eyeballs in preschoolers.

EEG signals exhibit commonality and variability across subjects, sessions, and tasks. But most existing EEG studies focus on mean group effects (commonality) by averaging signals over trials and subjects. The substantial intra- and inter-subject variability of EEG has often been overlooked. The recently significant technological advances in machine learning, especially deep learning, have brought technological innovations to EEG signal application in many aspects, but there are still great challenges in cross-session, cross-task, and cross-subject EEG decoding. In this work, an EEG-based biometric competition based on a large-scale M3CV (A Multi-subject, Multi-session, and Multi-task Database for investigation of EEG Commonality and Variability) database was launched to better characterize and harness the intra- and inter-subject variability and promote the development of machine learning algorithm in this field. In the M3CV database, EEG signals were recorded from 106 subjects, of which 95 subjects repeated two sessions of the experiments on different days. The whole experiment consisted of 6 paradigms, including resting-state, transient-state sensory, steady-state sensory, cognitive oddball, motor execution, and steady-state sensory with selective attention with 14 types of EEG signals, 120,000 epochs. With the learning tasks of the identification and verification, the performance metrics and baseline methods were introduced in the competition. In general, the proposed M3CV dataset and the EEG-based biometric competition aim to provide the opportunity to develop advanced machine learning algorithms for achieving an in-depth understanding of the commonality and variability of EEG signals across subjects, sessions, and tasks.

Background EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation training, but its effectiveness is controversial because some participants failed to learn self-regulation of FAA signals. It is desired to reveal neuroimaging predictors of learning ability in FAA neurofeedback, which cannot only improve our knowledge of the mechanisms of neurofeedback but can also enable personalized neurofeedback scheme. Previous studies have indicated that individual differences in neurofeedback training can be traced to neuroanatomical and neurofunctional baseline features. However, these studies only focused on regional measures of brain structure or function, and overlooked possible neural correlates of brain network measures. Besides, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far. Methods The present study designed a single-blind pseudo-controlled FAA neurofeedback experiment and collected multimodal neuroimaging data (structural magnetic resonance imaging and diffusion tensor imaging, and resting-state functional MRI) from healthy participants before neurofeedback training. We assessed the learning performance for evoked EEG modulations during training (performance index L1) and at rest (performance index L2), and investigated performance related predictors based on a combined analysis of multimodal brain networks and graph-theoretical features. Results Participants from both real and sham groups could increase their FAA during training blocks by maintaining a positive emotional state, but only the real group showed a significant increase in FAA at rest. The predictors of learning performance were different for evoked EEG modulations during training blocks and at rests: L1 was correlated with the graph-theoretical metrics (normalized clustering coefficient and local efficiency) of the right hemispheric gray matter network and functional brain network, while L2 was correlated with the graph-theoretical metrics (local and global efficiency) of the whole-brain and left hemispheric functional brain network. Conclusions The individual differences in FAA neurofeedback learning could be explained with individual variation in structural/functional architecture, and the correlated global topological metrics of different learning performance indices showed different laterality of hemispheric brain networks. These results provided an insight into the neural correlates of inter-individual differences in neurofeedback learning.

This study aimed to investigate the association between first-trimester subchorionic hematoma (SCH) detected at 6-8 weeks of gestation after fresh embryo transfers and adverse pregnancy outcomes.We performed a retrospective cohort involving 3074 patients. All of them acquired singleton pregnancies after fresh embryo transfers in the first in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles. According to first-trimester ultrasound examinations at 6-8 weeks of gestation, we divided patients into SCH and non-SCH groups and compared their perinatal outcomes. Symptomatic patients with vaginal bleeding and asymptomatic patients were analyzed separately, and propensity score matching (PSM) and multivariable regression were adopted to control potential confounding factors.The incidence of SCH was 17.1% in 3074 women, and vaginal bleeding occurred in 92 SCH patients and 215 control patients. In the asymptomatic cohort, 415 women with SCH and 807 women without SCH were finally included after PSM. No significant differences were observed in livebirth rate (91.3% vs 92.9%, P = 0.314), miscarriage rate (8.4% vs 6.7%, P = 0.267), and preterm birth rate (4.8% vs 5.7%, P = 0.519) between two groups. Secondary outcomes including gestational hypertension or preeclampsia, gestational diabetes mellitus (GDM), gestational age (GA) at delivery, mode of delivery, sex of newborns and birthweight of newborns were comparable. For symptomatic patients, both univariable and multivariable regression analysis showed no significant association between SCH and pregnancy outcomes. A subgroup analysis including patients with SCH illustrated the symptom of vaginal bleeding rather than hematoma size was associated with livebirth.First-trimester SCH detected at 6-8 weeks of gestation was not associated with adverse pregnancy outcomes in singleton pregnancies after fresh embryo transfers. Vaginal bleeding was the risk factor of pregnancy loss for patients with SCH.

The dominant approach in investigating the individual reliability for event-related potentials (ERPs) is to extract peak-related features at electrodes showing the strongest group effects. Such a peak-based approach implicitly assumes ERP components showing a stronger group effect are also more reliable, but this assumption has not been substantially validated and few studies have investigated the reliability of ERPs beyond peaks. In this study, we performed a rigorous evaluation of the test-retest reliability of ERPs collected in a multisensory and cognitive experiment from 82 healthy adolescents, each having two sessions. By comparing group effects and individual reliability, we found that a stronger group-level response in ERPs did not guarantee higher reliability. A perspective of neural oscillation should be adopted for the analysis of reliability. Further, by simulating ERPs with an oscillation-based computational model, we found that the consistency between group-level ERP responses and individual reliability was modulated by inter-subject latency jitter and inter-trial variability. The current findings suggest that the conventional peak-based approach may underestimate the individual reliability in ERPs and a neural oscillation perspective on ERP reliability should be considered. Hence, a comprehensive evaluation of the reliability of ERP measurements should be considered in individual-level neurophysiological trait evaluation and psychiatric disorder diagnosis.

Background. There are distinct results for the relationship between new-onset atrial fibrillation (NOAF) and subsequent incident cancer. To date, no systematic analysis has been conducted on this issue. This study aims to explore the relationship between NOAF and the risk of developing cancer through a meta-analysis with a large sample size. Methods. Electronic databases, such as PubMed and EMBASE, were searched for published relevant studies on NOAF patients diagnosed with cancer after and during follow-ups, including reported records of baseline information and the statistical result of morbidity. Two investigators independently reviewed the articles and extracted the data using uniform standards and definitions. The meta-analysis was conducted using the Cochrane Program Review Manager. Results. This meta-analysis consisted of five cohort studies and one case-control study, which comprised 533,514 participants. The pooled relative risk (RR) for incident cancer was 1.24 (95% CI: 1.10–1.39, P=0.0003). The temporal trend analysis demonstrated that an increased risk of cancer was observed during the initial 90 days (RR: 3.44, 95% CI: 2.29–5.57, P<0.00001), but not after that. Lung cancer (RR: 1.51, 95% CI: 1.47–1.55, P<0.00001) was associated with NOAF, but not colorectal cancer and breast cancer. Conclusion. This meta-analysis provides evidence that NOAF is associated with increased risk of cancer. The risk of incident cancer particularly increases within 90 days after NOAF diagnosis, but not after that.

Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laboratory environment, which provides constructive technical supports for establishing real‐time emotion interaction systems. In this paper, we will focus on video‐triggered EEG‐based emotion recognition and present a systematical introduction of the current available video‐triggered EEG‐based emotion databases with the corresponding analysis methods. First, current video‐triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB‐HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video‐triggered EEG‐based emotion recognition will be systematically summarized and a brief review of current situation about video‐triggered EEG‐based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video‐triggered EEG‐emotion databases will be fully discussed.