Land-use classification based on high-resolution remote sensing imagery and deep learning models.

High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet's superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.

Small object detection algorithm incorporating swin transformer for tea buds.

Accurate identification of small tea buds is a key technology for tea harvesting robots, which directly affects tea quality and yield. However, due to the complexity of the tea plantation environment and the diversity of tea buds, accurate identification remains an enormous challenge. Current methods based on traditional image processing and machine learning fail to effectively extract subtle features and morphology of small tea buds, resulting in low accuracy and robustness. To achieve accurate identification, this paper proposes a small object detection algorithm called STF-YOLO (Small Target Detection with Swin Transformer and Focused YOLO), which integrates the Swin Transformer module and the YOLOv8 network to improve the detection ability of small objects. The Swin Transformer module extracts visual features based on a self-attention mechanism, which captures global and local context information of small objects to enhance feature representation. The YOLOv8 network is an object detector based on deep convolutional neural networks, offering high speed and precision. Based on the YOLOv8 network, modules including Focus and Depthwise Convolution are introduced to reduce computation and parameters, increase receptive field and feature channels, and improve feature fusion and transmission. Additionally, the Wise Intersection over Union loss is utilized to optimize the network. Experiments conducted on a self-created dataset of tea buds demonstrate that the STF-YOLO model achieves outstanding results, with an accuracy of 91.5% and a mean Average Precision of 89.4%. These results are significantly better than other detectors. Results show that, compared to mainstream algorithms (YOLOv8, YOLOv7, YOLOv5, and YOLOx), the model improves accuracy and F1 score by 5-20.22 percentage points and 0.03-0.13, respectively, proving its effectiveness in enhancing small object detection performance. This research provides technical means for the accurate identification of small tea buds in complex environments and offers insights into small object detection. Future research can further optimize model structures and parameters for more scenarios and tasks, as well as explore data augmentation and model fusion methods to improve generalization ability and robustness.

Prediction of PM2.5 concentration based on the CEEMDAN-RLMD-BiLSTM-LEC model.

Air quality has emerged as a critical concern in recent years, with the concentration of PM2.5 recognized as a vital index for assessing it. The accuracy of predicting PM2.5 concentrations holds significant value for effective air quality monitoring and management. In response to this, a combined model comprising CEEMDAN-RLMD-BiLSTM-LEC has been introduced, analyzed, and compared against various other models. The combined decomposition method effectively underlines the fundamental characteristics of the data compared to individual decomposition techniques. Additionally, local error correction (LEC) efficiently addresses the issue of prediction errors induced by excessive disturbances. The empirical results of nine steps indicate that the combined CEEMDAN-RLMD-BiLSTM-LEC model outperforms single prediction models such as RLMD and CEEMDAN, reducing MAE, RMSE, and SAMPE by 36.16%, 28.63%, 45.27% and 16.31%, 6.15%, 37.76%, respectively. Moreover, the inclusion of LEC in the model further diminishes MAE, RMSE, and SMAPE by 20.69%, 7.15%, and 44.65%, respectively, exhibiting commendable performance in generalization experiments. These findings demonstrate that the combined CEEMDAN-RLMD-BiLSTM-LEC model offers high predictive accuracy and robustness, effectively handling noisy data predictions and severe local variations. With its wide applicability, this model emerges as a potent tool for addressing various related challenges in the field.

A synergistic future for AI and ecology.

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence-they are critical for both persisting and thriving in an uncertain future.

Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai-Tibet Plateau.

Ali Network data based on the Qinghai-Tibetan Plateau (QTP) can provide representative coverage of the climate and surface hydrometeorological conditions in the cold and arid region of the QTP. Among them, the plateau soil moisture can effectively quantify the uncertainty of coarse resolution satellite and soil moisture models. With the objective of constructing an "end-to-end" soil moisture prediction model for the Tibetan Plateau, a combined prediction model based on time series decomposition and a deep neural network is proposed in this article. The model first performs data preprocessing and seasonal-trend decomposition using loess (STL) to obtain the trend component, seasonal component and random residual component of the original time series in an additive way. Subsequently, the bidirectional gated recurrent unit (BiGRU) is used for the trend component, and the long short-term memory (LSTM) is used for the seasonal and residual components to extract the time series information. The experiments based on the measured data demonstrate that the use of STL decomposition and the combination model can effectively extract the information in soil moisture series using its concise and clear structure. The proposed model in this article has a stable performance improvement of 5-30% over a single model and existing prediction models in different prediction time domains. In long-range prediction, the proposed model also achieves the best accuracy in the shape and temporal domains described by using dynamic time warping (DTW) index and temporal distortion index (TDI). In addition, the generalization performance experiments show that the combined method proposed in this article has strong reference value for time series prediction of natural complex systems.

Treatment-Induced Recovery Patterns Between Nouns and Verbs in Mandarin-English Bilingual Adults With Aphasia.

PURPOSE: This study aimed to investigate treatment effects of naming therapy targeting nouns and verbs in Mandarin-English bilingual adults with aphasia (BWA). METHOD: Twelve Mandarin-English bilingual adults with chronic aphasia completed a 40-hr semantic-based naming treatment for either nouns or verbs. Eight of these participants completed both noun and verb treatment, and the other four completed either noun or verb treatment. Participants were trained in either Mandarin or English for both treatment cycles. Weekly naming probes were measured to capture the direct treatment gain and within- and cross-language generalizations. Performance on the standardized language assessments was analyzed to examine further generalizations beyond the word level and to standardized naming tasks. RESULTS: Responses in the weekly naming probes showed significant treatment gains in both noun and verb treatment, but the effect was greater in verb treatment. Generalization to semantically related items was captured in noun treatment only. Cross-language generalization was identified in both noun and verb treatment with a larger effect in verb treatment. Additionally, widespread generalizations beyond the word level and to standardized naming tasks were found following both noun and verb treatment, but the effect was larger following noun treatment in discourse and verb naming tasks. CONCLUSIONS: Findings from this study suggested robust treatment effects of semantic-based naming treatment targeting nouns and verbs in Mandarin-English BWA. However, patterns of treatment gains and generalizations differed between these word categories. This study provides strong evidence of bilingual aphasia rehabilitation in Mandarin-English BWA. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.23818299.

Prediction From Minimal Experience: How People Predict the Duration of an Ongoing Epidemic.

People are known for good predictions in domains they have rich experience with, such as everyday statistics and intuitive physics. But how well can they predict for problems they lack experience with, such as the duration of an ongoing epidemic caused by a new virus? Amid the first wave of COVID-19 in China, we conducted an online diary study, asking each of over 400 participants to predict the remaining duration of the epidemic, once per day for 14 days. Participants' predictions reflected a reasonable use of publicly available information but were meanwhile biased, subject to the influence of negative affect and future time perspectives. Computational modeling revealed that participants neither relied on prior distributions of epidemic durations as in inferring everyday statistics, nor on mechanistic simulations of epidemic dynamics as in computing intuitive physics. Instead, with minimal experience, participants' predictions were best explained by similarity-based generalization of the temporal pattern of epidemic statistics. In two control experiments, we further confirmed that such cognitive algorithm is not specific to the epidemic scenario and that minimal and rich experience do lead to different prediction behaviors for the same observations. We conclude that people generalize patterns in recent history to predict the future under minimal experience.

Origin identification of Cornus officinalis based on PCA-SVM combined model.

Infrared spectroscopy can quickly and non-destructively extract analytical information from samples. It can be applied to the authenticity identification of various Chinese herbal medicines, the prediction of the mixing amount of defective products, and the analysis of the origin. In this paper, the spectral information of Cornus officinalis from 11 origins was used as the research object, and the origin identification model of Cornus officinalis based on mid-infrared spectroscopy was established. First, principal component analysis was used to extract the absorbance data of Cornus officinalis in the wavenumber range of 551~3998 cm-1. The extracted principal components contain more than 99.8% of the information of the original data. Second, the extracted principal component information was used as input, and the origin category was used as output, and the origin identification model was trained with the help of support vector machine. In this paper, this combined model is called PCA-SVM combined model. Finally, the generalization ability of the PCA-SVM model is evaluated through an external test set. The three indicators of Accuracy, F1-Score, and Kappa coefficient are used to compare this model with other commonly used classification models such as naive Bayes model, decision trees, linear discriminant analysis, radial basis function neural network and partial least square discriminant analysis. The results show that PCA-SVM model is superior to other commonly used models in accuracy, F1 score and Kappa coefficient. In addition, compared with the SVM model with full spectrum data, the PCA-SVM model not only reduces the redundant variables in the model, but also has higher accuracy. Using this model to identify the origin of Cornus officinalis, the accuracy rate is 84.8%.

A Tensor-Based Frequency Features Combination Method for Brain-Computer Interfaces.

With the development of the brain-computer interface (BCI) community, motor imagery-based BCI system using electroencephalogram (EEG) has attracted increasing attention because of its portability and low cost. Concerning the multi-channel EEG, the frequency component is one of the most critical features. However, insufficient extraction hinders the development and application of MI-BCIs. To deeply mine the frequency information, we proposed a method called tensor-based frequency feature combination (TFFC). It combined tensor-to-vector projection (TVP), fast fourier transform (FFT), common spatial pattern (CSP) and feature fusion to construct a new feature set. With two datasets, we used different classifiers to compare TFFC with the state-of-the-art feature extraction methods. The experimental results showed that our proposed TFFC could robustly improve the classification accuracy of about 5% ( ). Moreover, visualization analysis implied that the TFFC was a generalization of CSP and Filter Bank CSP (FBCSP). Also, a complementarity between weighted narrowband features (wNBFs) and broadband features (BBFs) was observed from the averaged fusion ratio. This article certificates the importance of frequency information in the MI-BCI system and provides a new direction for designing a feature set of MI-EEG.

Vagus nerve stimulation promotes extinction generalization across sensory modalities.

Traumatic experiences involve complex sensory information, and individuals with trauma-related psychological disorders, such as posttraumatic stress disorder (PTSD), can exhibit abnormal fear to numerous different stimuli that remind them of the trauma. Vagus nerve stimulation (VNS) enhances extinction of auditory fear conditioning in rat models for PTSD. We recently found that VNS-paired extinction can also promote extinction generalization across different auditory cues. Here we tested whether VNS can enhance extinction of olfactory fear and promote extinction generalization across auditory and olfactory sensory modalities. Male Sprague Dawley rats were implanted with a stimulating cuff on the cervical vagus nerve. Rats then received two days of fear conditioning where olfactory (amyl acetate odor) and auditory (9 kHz tones) stimuli were concomitantly paired with footshock. Twenty-four hours later, rats were given three days of sham or VNS-paired extinction (5 stimulations, 30-sec trains at 0.4 mA) overlapping with presentation of either the olfactory or the auditory stimulus. Two days later, rats were given an extinction retention test where avoidance of the olfactory stimulus or freezing to the auditory stimulus were measured. VNS-paired with exposure to the olfactory stimulus during extinction reduced avoidance of the odor in the retention test. VNS-paired with exposure to the auditory stimulus during extinction also decreased avoidance of the olfactory cue, and VNS paired with exposure to the olfactory stimulus during extinction reduced freezing when the auditory stimulus was presented in the retention test. These results indicate that VNS enhances extinction of olfactory fear and promotes extinction generalization across different sensory modalities. Extinction generalization induced by VNS may therefore improve outcomes of exposure-based therapies.

Generalizing experimental results by leveraging knowledge of mechanisms.

We show how experimental results can be generalized across diverse populations by leveraging knowledge of local mechanisms that produce the outcome of interest, only some of which may differ in the target domain. We use structural causal models and a refined version of selection diagrams to represent such knowledge, and to decide whether it entails the invariance of probabilities of causation across populations, which then enables generalization. We further provide: (i) bounds for the target effect when some of these conditions are violated; (ii) new identification results for probabilities of causation and the transported causal effect when trials from multiple source domains are available; as well as (iii) a Bayesian approach for estimating the transported causal effect from finite samples. We illustrate these methods both with simulated data and with a real example that transports the effects of Vitamin A supplementation on childhood mortality across different regions.

Avoiding Based on Shades of Gray: Generalization of Pain-Related Avoidance Behavior to Novel Contexts.

Avoidance behavior is protective, yet in the absence of genuine bodily threat, it may become disabling. Therefore, we investigated whether avoidance generalizes to novel safe contexts based on the similarity with the acquisition context. Healthy participants performed arm movements using a robotic arm to reach a target. Three trajectories (T1-3) led to the target. During acquisition, a painful stimulus could be partly/completely prevented by performing more effortful trajectories (ie, longer and more force needed), T2/T3, in the pain-avoidance context (eg, black background); in the yoked context (eg, white background), the same reinforcement schedule was applied irrespective of the chosen trajectories. Generalization of avoidance was tested in 2 novel contexts (eg, shades of gray backgrounds). We assessed self-reported pain-expectancy and pain-related fear for all trajectories, and avoidance behavior (ie, maximal deviation from T1). Results confirm that fear and expectancy ratings reflect the response-outcome contingencies and differential learning selectively generalized to the novel context resembling the original pain-avoidance context. Furthermore, a linear trend in avoidance behavior across contexts emerged, which is indicative of a generalization gradient. Participants avoided more in the context resembling the original pain-avoidance context than in the one resembling the yoked context, but this effect was not statistically significant. PERSPECTIVE: Perspective: We demonstrated acquisition of pain-related avoidance behavior in a within-subjects design, showing modulation of pain-related fear and pain-expectancy by context and providing limited evidence that avoidance selectively generalizes to novel, similar contexts. These results provide insight regarding the underlying mechanisms of the spreading of protective behavior in chronic pain patients.

Unimodal and Bimodal Access to Sensory Working Memories by Auditory and Visual Impulses.

It is unclear to what extent sensory processing areas are involved in the maintenance of sensory information in working memory (WM). Previous studies have thus far relied on finding neural activity in the corresponding sensory cortices, neglecting potential activity-silent mechanisms, such as connectivity-dependent encoding. It has recently been found that visual stimulation during visual WM maintenance reveals WM-dependent changes through a bottom-up neural response. Here, we test whether this impulse response is uniquely visual and sensory-specific. Human participants (both sexes) completed visual and auditory WM tasks while electroencephalography was recorded. During the maintenance period, the WM network was perturbed serially with fixed and task-neutral auditory and visual stimuli. We show that a neutral auditory impulse-stimulus presented during the maintenance of a pure tone resulted in a WM-dependent neural response, providing evidence for the auditory counterpart to the visual WM findings reported previously. Interestingly, visual stimulation also resulted in an auditory WM-dependent impulse response, implicating the visual cortex in the maintenance of auditory information, either directly or indirectly, as a pathway to the neural auditory WM representations elsewhere. In contrast, during visual WM maintenance, only the impulse response to visual stimulation was content-specific, suggesting that visual information is maintained in a sensory-specific neural network, separated from auditory processing areas.SIGNIFICANCE STATEMENT Working memory is a crucial component of intelligent, adaptive behavior. Our understanding of the neural mechanisms that support it has recently shifted: rather than being dependent on an unbroken chain of neural activity, working memory may rely on transient changes in neuronal connectivity, which can be maintained efficiently in activity-silent brain states. Previous work using a visual impulse stimulus to perturb the memory network has implicated such silent states in the retention of line orientations in visual working memory. Here, we show that auditory working memory similarly retains auditory information. We also observed a sensory-specific impulse response in visual working memory, while auditory memory responded bimodally to both visual and auditory impulses, possibly reflecting visual dominance of working memory.

Hear it, fear it: Fear generalizes from conditioned pictures to semantically related sounds.

Fear generalization is thought to be an important mechanism in the acquisition and maintenance of anxiety disorders. Previous studies have investigated fear generalization within one sensory modality - mainly within the visual domain. However, a growing body of evidence shows that emotional information is processed in more than one sensory modality. Based on network theories, we expected that fear may also generalize from stimuli in one sensory modality to another. To test our hypothesis, 42 participants underwent a differential conditioning paradigm, during which pictures were either presented with (vCS+) or without (vCS-) an aversive electric stimulus. After the acquisition phase, generalization was tested in the crossmodal group (n = 21) by presenting sounds which were semantically congruent to the visual vCS+ (i.e., the aGS+) or the vCS- (i.e., the aGS-). As a control, the unimodal group (n = 21) saw the pictures again. For the crossmodal group, we could show that US expectancy ratings generalized from conditioned pictures (vCS+) to semantically related sounds (aGS+). Moreover, when the vCS+ was presented during extinction, fear of the aGS+ extinguished, whereas extinction training with the aGS+ was found to be less effective for the vCS+. The findings are relevant for crossmodal fear acquisition and exposure therapy.

Validating the use of a smartphone app for remote administration of a fear conditioning paradigm.

Fear conditioning models key processes related to the development, maintenance and treatment of anxiety disorders and is associated with group differences in anxiety. However, laboratory administration of tasks is time and cost intensive, precluding assessment in large samplesnecessary for the analysis of individual differences. This study introduces a newly developed smartphone app that delivers a fear conditioning paradigm remotely using a loud human scream as an aversive stimulus. Three groups of participants (total n = 152) took part in three studies involving a differential fear conditioning experiment to assess the reliability and validity of a smartphone administered fear conditioning paradigm. This comprised of fear acquisition, generalisation, extinction, and renewal phases during which online US-expectancy ratings were collected during every trial with evaluative ratings of negative affect at three time points. We show that smartphone app delivery of a fear conditioning paradigm results in a pattern of fear learning comparable to traditional laboratory delivery and is able to detect individual differences in performance that show comparable associations with anxiety to the prior group differences literature.

Increasing Verbal Interaction in Children with Autism Spectrum Disorders Using Audio Script Procedure.

This study aimed at investigating the effectiveness of audio script and script-fading procedure in teaching initiation to children with ASD. Three children with ASD and a parent of each child participated in the study. A nonconcurrent multiple baseline design across children was used. The findings showed that the initiation emitted by the children increased during audio script and script-fading procedure. Children also generalized initiation across different conditions and maintained the acquired skills. Finally, the social validity findings showed that the opinions of the parents regarding the procedure were overall positive. Results were discussed in terms of recommendations for practitioners and future research.

Distinct Patterns of Neural Habituation and Generalization in Children and Adolescents With Autism With Low and High Sensory Overresponsivity.

OBJECTIVE: Sensory overresponsivity (SOR), an atypical negative reaction to sensory stimuli, is highly prevalent in autism spectrum disorder (ASD). Previous work has related SOR to increased brain response in sensory-limbic regions. This study investigated where these atypical responses fall in three fundamental stages of sensory processing: arousal (i.e., initial response), habituation (i.e., change in response over time), and generalization of response to novel stimuli. Different areas of atypical response would require distinct intervention approaches. METHODS: Functional MRI was used to examine these patterns of neural habituation to two sets of similar mildly aversive auditory and tactile stimuli in 42 high-functioning children and adolescents with ASD (21 with high levels of SOR and 21 with low levels of SOR) and 27 age-matched typically developing youths (ages 8-17). The relationship between SOR and change in amygdala-prefrontal functional connectivity across the sensory stimulation was also examined. RESULTS: Across repeated sensory stimulation, high-SOR participants with ASD showed reduced ability to maintain habituation in the amygdala and relevant sensory cortices and to maintain inhibition of irrelevant sensory cortices. These results indicate that sensory habituation is a dynamic, time-varying process dependent on sustained regulation across time, which is a particular deficit in high-SOR participants with ASD. However, low-SOR participants with ASD also showed distinct, nontypical neural response patterns, including reduced responsiveness to novel but similar stimuli and increases in prefrontal-amygdala regulation across the sensory exposure. CONCLUSIONS: The results suggest that all children with autism have atypical brain responses to sensory stimuli, but whether they express atypical behavioral responses depends on top-down regulatory mechanisms. Results are discussed in terms of targeted intervention approaches.

The effect of transcutaneous vagus nerve stimulation on fear generalization and subsequent fear extinction.

Fear overgeneralization is thought to be one of the cardinal processes underlying anxiety disorders, and a determinant of the onset, maintenance and recurrence of these disorders. Animal studies have shown that stimulating the vagus nerve (VNS) affects neuronal pathways implicated in pattern separation and completion, suggesting it may reduce the generalization of a fear memory to novel situations. In a one-day study, 58 healthy students were subjected to a fear conditioning, fear generalization, and fear extinction paradigm. Participants were randomly assigned to receive either transcutaneous auricular VNS (tVNS; final N = 29) or sham stimulation (final N = 29) during the generalization and extinction phases. tVNS did not affect fear generalization, as reflected by US expectancy ratings and fear potentiated startle responses. However, participants who received tVNS reported lower US expectancy ratings to the CS+ during the extinction phase, possibly reflecting a stronger declarative extinction of fear. No effects of tVNS on fear potentiated startle responses during extinction were found. The pattern of findings regarding extinction of declarative fear suggest a facilitating effect of tVNS.

Social Perceptions of Quack in Qing Dynasty and Its Transformation in the Late Qing Period / 의사학

“Yong-yi” means “quack” in English, which generally refers to a doctor who does not have good medical skills. In the Ming and Qing dynasties in China, various criticism about “Yong-yi” became popularized, and by the late Qing period, “quacks” had become a serious social issue. The theory of traditional Chinese medicine was developed during the Ming and Qing dynasties, and local medical resources also increased. Moreover, the prevalence of medical book publishing led to the openness and generalization of medical knowledge. As a result, not only the number of doctors increased, but also the number of doctors who lack medical knowledge and clinical experience increased. However, at the outset, “Yong-yi” did not only mean doctors with poor medical skills. “Yong-yi” also reflected conflicts and contradictions between doctors. Doctors consistently criticized quacks in an attempt to maintain their identity as a “good” doctor or a Confucian doctor. In this sense, “Yong-yi” was used among physicians as an expression of discrimination and exclusion. The concept of “quackery” was also determined by the relationship between patients and doctors. In general, itinerant doctors, midwives and shaman doctors were regarded as “Yong-yi”; however, they served the medical needs of various patients. Thus, to some extent, “Yong-yi” were also useful medical resources. On the contrary, in certain situations, “shiyi,” physicians who serviced a family for generations and were generally believed to be reliable and as trustworthy doctors, were also labelled as quacks, especially when the patient did not trust them or was not satisfied with the treatment. Therefore, doctors' thoughts about “Yong-yi” did not always coincide with patients' thoughts about “Yong-yi.” However, by the late Qing period, the description of quacks in media reports found a singular connotation, and the divergent social image of quacks disappeared. By this time, quacks were uniformly described as ignorant and irresponsible Chinese medicine practitioners. Specifically, in one murder case in which a “Yong-yi” was accused as the murderer, the report unilaterally reported the patient's claims. Consequently, Chinese medicine practitioners who failed in their treatment of patients became labeled as “quack” doctors. In newspaper reports, “Yong-yi” no longer simply referred to individual cases of “quacks” but had come to represent the entirety of the Chinese medicine practitioner community. On the contrary, Western medical doctors who replaced the status of traditional doctors were positively portrayed. Pictorials also had similar perspectives with newspapers, supporting the narrative of the news with ironic drawings and articles. Overall, media reports regarding “Yong-yi” did not focus on reporting facts, but they had the purpose of making quacks a serious social problem.

Coherent Activity between the Prelimbic and Auditory Cortex in the Slow-Gamma Band Underlies Fear Discrimination.

The medial prefrontal cortex and the basolateral amygdala (BLA) are essential for discriminating between harmful and safe stimuli. The primary auditory cortex (Te1) sends projections to both sites, but whether and how it interacts with these areas during fear discrimination are poorly understood. Here we show that in male rats that can differentiate between a new tone and a threatening one, the selective optogenetic inhibition of Te1 axon terminals into the prelimbic (PL) cortex shifted discrimination to fear generalization. Meanwhile, no effects were detected when Te1 terminals were inhibited in the BLA. Using a combination of local field potential and multiunit recordings, we show that in animals that discriminate successfully between a new tone and a harmful one, the activity of the Te1 and the PL cortex becomes immediately and tightly synchronized in the slow-gamma range (40-70 Hz) at the onset of the new tone. This enhanced synchronization was not present in other frequency ranges, such as the theta range. Critically, the level of gamma synchrony predicted the behavioral choice (i.e., no freezing or freezing) of the animals. Moreover, in the same rats, gamma synchrony was absent before the fear-learning trial and when animals should discriminate between an olfactory stimulus and the auditory harmful one. Thus, our findings reveal that the Te1 and the PL cortex dynamically establish a functional connection during auditory fear-discrimination processes, and that this corticocortical oscillatory mechanism drives the behavioral choice of the animals.SIGNIFICANCE STATEMENT Identifying neural networks that infer safety versus danger is of great interest in the scientific field. Fear generalization reduces the chances of an animal's survival and leads to psychiatric diseases, such as post-traumatic stress disorders and phobias in humans. Here we demonstrate that animals able to differentiate a new tone from a previous threating tone showed synchronization between the prefrontal and primary auditory cortices. Critically, this connectivity precedes and predicts the behavioral outcome of the animal. Optogenetic inhibition of this functional connectivity leads to fear generalization. To the best of our knowledge, this study is the first to demonstrate that a corticocortical dialogue occurring between sensory and prefrontal areas is a key node for fear-discrimination processes.