Chronic social defeat stress gives rise to social avoidance through fear learning.

Chronic social defeat stress (CSDS), a widely used rodent model of stress, reliably leads to decreased social interaction in stress susceptible animals. Here, we investigate a role for fear learning in this response using male 129 Sv/Ev mice, a strain that is more vulnerable to CSDS than the commonly used C57BL/6 strain. We first demonstrate that defeated 129 Sv/Ev mice avoid a CD-1 mouse, but not a conspecific, indicating that motivation to socialize is intact in this strain. CD-1 avoidance is characterized by approach behavior that results in running in the opposite direction, activity that is consistent with a threat response. We next test whether CD-1 avoidance is subject to the same behavioral changes found in traditional models of Pavlovian fear conditioning. We find that associative learning occurs across 10 days CSDS, with defeated mice learning to associate the color of the CD-1 coat with threat. This leads to the gradual acquisition of avoidance behavior, a conditioned response that can be extinguished with 7 days of repeated social interaction testing (5 tests/day). Pairing a CD-1 with a tone leads to second-order conditioning, resulting in avoidance of an enclosure without a social target. Finally, we show that social interaction with a conspecific is a highly variable response in defeated mice that may reflect individual differences in generalization of fear to other social targets. Our data indicate that fear conditioning to a social target is a key component of CSDS, implicating the involvement of fear circuits in social avoidance.

A lighter hybrid feature fusion framework for polyp segmentation.

Colonoscopy is widely recognized as the most effective method for the detection of colon polyps, which is crucial for early screening of colorectal cancer. Polyp identification and segmentation in colonoscopy images require specialized medical knowledge and are often labor-intensive and expensive. Deep learning provides an intelligent and efficient approach for polyp segmentation. However, the variability in polyp size and the heterogeneity of polyp boundaries and interiors pose challenges for accurate segmentation. Currently, Transformer-based methods have become a mainstream trend for polyp segmentation. However, these methods tend to overlook local details due to the inherent characteristics of Transformer, leading to inferior results. Moreover, the computational burden brought by self-attention mechanisms hinders the practical application of these models. To address these issues, we propose a novel CNN-Transformer hybrid model for polyp segmentation (CTHP). CTHP combines the strengths of CNN, which excels at modeling local information, and Transformer, which excels at modeling global semantics, to enhance segmentation accuracy. We transform the self-attention computation over the entire feature map into the width and height directions, significantly improving computational efficiency. Additionally, we design a new information propagation module and introduce additional positional bias coefficients during the attention computation process, which reduces the dispersal of information introduced by deep and mixed feature fusion in the Transformer. Extensive experimental results demonstrate that our proposed model achieves state-of-the-art performance on multiple benchmark datasets for polyp segmentation. Furthermore, cross-domain generalization experiments show that our model exhibits excellent generalization performance.

Vocal usage learning and vocal comprehension learning in harbor seals.

BACKGROUND: Which mammals show vocal learning abilities, e.g., can learn new sounds, or learn to use sounds in new contexts? Vocal usage and comprehension learning are submodules of vocal learning. Specifically, vocal usage learning is the ability to learn to use a vocalization in a new context; vocal comprehension learning is the ability to comprehend a vocalization in a new context. Among mammals, harbor seals (Phoca vitulina) are good candidates to investigate vocal learning. Here, we test whether harbor seals are capable of vocal usage and comprehension learning. RESULTS: We trained two harbor seals to (i) switch contexts from a visual to an auditory cue. In particular, the seals first produced two vocalization types in response to two hand signs; they then transitioned to producing these two vocalization types upon the presentation of two distinct sets of playbacks of their own vocalizations. We then (ii) exposed the seals to a combination of trained and novel vocalization stimuli. In a final experiment, (iii) we broadcasted only novel vocalizations of the two vocalization types to test whether seals could generalize from the trained set of stimuli to only novel items of a given vocal category. Both seals learned all tasks and took ≤ 16 sessions to succeed across all experiments. In particular, the seals showed contextual learning through switching the context from former visual to novel auditory cues, vocal matching and generalization. Finally, by responding to the played-back vocalizations with distinct vocalizations, the animals showed vocal comprehension learning. CONCLUSIONS: It has been suggested that harbor seals are vocal learners; however, to date, these observations had not been confirmed in controlled experiments. Here, through three experiments, we could show that harbor seals are capable of both vocal usage and comprehension learning.

P2X7 receptors modulate acquisition of cue fear extinction and contextual background memory generalization in male mice.

The purinergic P2X7 receptors (P2X7R) are activated by adenosine triphosphate (ATP) in several brain regions, particularly those involved with emotional control and the regulation of fear-related memories. Here, we investigate the role of P2X7R in fear learning memory, specifically in the acquisition and consolidation phases of the cued fear conditioning paradigm. C57Bl/6 wildtype (WT) male mice that received a single i.p. injection of the selective P2X7R antagonist A438079 prior the conditioning session showed generalization of cued fear memory and impaired fear extinction recall in the test session, while those treated prior the extinction session exhibited a similar behavior profile accompanied by resistance in the extinction learning. However, no effects were observed when this drug was administered immediately after the conditioning, extinction, or before the test session. Our results with P2X7R knockout (P2X7 KO) mice showed a behavioral profile that mirrored the collective effects observed across all pharmacological treatment conditions. This suggests that the P2X7R KO model effectively replicates the behavioral changes induced by the pharmacological interventions, demonstrating that we have successfully isolated the role of P2X7R in the fear and extinction phases of memory. These findings highlight the role of P2X7R in the acquisition and recall of extinction memory and supports P2X7R as a promising candidate for controlling abnormal fear processing, with potential applications for stress exposure-related disorders such as post-traumatic stress disorder (PTSD).

A lightweight model design approach for few-shot malicious traffic classification.

Classifying malicious traffic, which can trace the lineage of attackers' malicious families, is fundamental to safeguarding cybersecurity. However, the deep learning approaches currently employed require substantial volumes of data, conflicting with the challenges in acquiring and accurately labeling malicious traffic data. Additionally, edge network devices vulnerable to cyber-attacks often cannot meet the computational demands required to deploy deep learning models. The rapid mutation of malicious activities further underscores the need for models with strong generalization capabilities to adapt to evolving threats. This paper introduces an innovative few-shot malicious traffic classification method that is precise, lightweight, and exhibits enhanced generalization. By refining traditional transfer learning, the source model is segmented into public and private feature extractors for stepwise transfer, enhancing parameter alignment with specific target tasks. Neuron importance is then sorted based on the task of each feature extractor, enabling precise pruning to create an optimal lightweight model. An adversarial network guiding principle is adopted for retraining the public feature extractor parameters, thus strengthening the model's generalization power. This method achieves an accuracy of over 97% on few-shot datasets with no more than 15 samples per class, has fewer than 50 K model parameters, and exhibits superior generalization compared to baseline methods.

Facilitation of motor adaptation using multiple gait rehabilitation interventions.

Introduction: The rate of adjustment in a movement, driven by feedback error, is referred to as the adaptation rate, and the rate of recovery of a newly adapted movement to its unperturbed condition is called the de-adaptation rate. The rates of adaptation and de-adaptation are dependent on the training mechanism and intrinsic factors such as the participant's sensorimotor abilities. This study investigated the facilitation of the motor adaptation and de-adaptation processes for spatiotemporal features of an asymmetric gait pattern by sequentially applying split-belt treadmill (SBT) and asymmetric rhythmic auditory cueing (ARAC). Methods: Two sessions tested the individual gait characteristics of SBT and ARAC, and the remaining four sessions consisted of applying the two interventions sequentially during training. The adjustment process to the second intervention is referred to as "re-adaptation" and is driven by feedback error associated with the second intervention. Results: Ten healthy individuals participated in the randomized six-session trial. Spatiotemporal asymmetries during the adaptation and post-adaptation (when intervention is removed) stages were fitted into a two-component exponential model that reflects the explicit and implicit adaptation processes. A double component was shown to fit better than a single-component model. The decay constants of the model were indicative of the corresponding timescales and compared between trials. Results revealed that the explicit (fast) component of adaptation to ARAC was reduced for step length and step time when applied after SBT. Contrarily, the explicit component of adaptation to SBT was increased when it was applied after ARAC for step length. Additionally, the implicit (slow) component of adaptation to SBT was inhibited when applied incongruently after ARAC for step time. Discussion: These outcomes show that the role of working motor memory as a translational tool between different gait interventions is dependent on (i) the adaptation mechanisms associated with the interventions, (ii) the targeted motor outcome of the interventions; the effects of factors (i) and (ii) are specific to the explicit and implicit components of the adaptation processes; these effects are unique to spatial and temporal gait characteristics.

A domain generalization network for imbalanced machinery fault diagnosis.

Traditional models for Imbalanced Fault Diagnosis (IFD) face challenges in practical applications due to domain shifts caused by varying working conditions and machinery. Domain Generalization (DG) models provide an advantage over traditional approaches by learning class-discriminative and domain-invariant feature representations, allowing them to generalize to unseen target data. However, the scarcity of fault samples relative to healthy ones limits their application in real-world industrial scenarios. In this paper, we propose a Domain Mixed-Enhanced Domain Generalization Network (DEMDGN) that enhances IFD performance by utilizing mixup-based data augmentation and domain-based discrepancy metrics to align feature distributions across multiple heterogeneous source domains. By creating domain-invariant features, DEMDGN allows robust fault diagnosis under varying conditions. Extensive experiments on one marine machinery dataset and two bearing datasets demonstrate that the proposed method effectively addresses class imbalance and domain shift problems, achieving superior diagnostic performance.

Invasive Ant Detection: Evaluating Honeybee Learning and Discrimination Abilities for Detecting Solenopsis invicta Odor.

Invasive red imported fire ants (Solenopsis invicta) create a serious threat to public safety, agriculture, biodiversity, and the local economy, necessitating early detection and surveillance, which are currently time-consuming and dependent on the inspector's expertise. This study marks an initial investigation into the potential of honeybees (Apis mellifera) to detect and discriminate the odor of S. invicta through the olfactory conditioning of proboscis extension responses. Deceased S. invicta were used as conditioned stimuli to ensure relevance to non-infested areas. The results showed that the bees rapidly learned to respond to deceased ant odors, with response levels significantly increasing at higher odor intensities. Bees exhibited generalization across the odors of 25 minor workers, 21 median workers, 1 major worker, and 1 female alate. When conditioned with deceased ant odors, bees effectively recognized live ants, particularly when trained on a single minor worker. Discrimination abilities varied by species and were higher when S. invicta was paired with Polyrhachis dives and Nylanderia yaeyamensis, and lower with S. geminata, Pheidole rabo, and Pheidole fervens. Notably, discrimination improved significantly with the application of latent inhibition. These findings suggest that trained honeybees have the potential to detect S. invicta. Further refinement of this approach could enhance its effectiveness for detection and surveillance.

Prenatal immune activation in rats and adult exposure to inescapable shocks reveal sex-dependent effects on fear conditioning that might be relevant for schizophrenia.

Prenatal infection is considered a relevant factor for neurodevelopmental alterations and psychiatric diseases. Administration of bacterial and viral components during pregnancy in rodents results in maternal immune activation (MIA), leading to schizophrenia-like neurochemical and behavioral changes. Despite some evidence for abnormal fear conditioning in schizophrenia, only a few animal studies have focused on this issue. Therefore, we addressed the impact of the administration of the viral mimetic polyI:C to pregnant Long-Evans rats on the adult offspring response to inescapable shocks (IS) and contextual fear conditioning. In males, polyI:C induced a greater endocrine (plasma ACTH) response to IS and both polyI:C and IS enhanced fear conditioning and generalization to a completely different novel environment (hole-board), with no additive effects, probably due to a ceiling effect. In contrast, a modest impact of polyI:C and a lower impact of IS on contextual fear conditioning and generalization was observed in females. Thus, the present results demonstrate that polyI:C dramatically affected fear response to IS in adult males and support the hypothesis that males are more sensitive than females to this treatment. This model might allow to explore neurobiological mechanisms underlying abnormal responsiveness to fear conditioning and stressors in schizophrenia.

The assessment of gender differences in perceptual fear generalization and related processes.

In this study we aimed to investigate gender differences in fear generalization tendencies in humans and, inspired by recent findings in animal research, examine whether any such differences could stem from differences in memory precision. Forty men and forty women underwent a differential fear conditioning procedure using geometric shapes as cues. Subsequently, generalized fear responses were assessed across a spectrum of perceptually similar shapes. Throughout generalization testing, perceptual memory accuracy was repeatedly probed using a stimulus recreation task. Using statistical and computational modeling, we found strong evidence for the absence of gender differences in fear learning and generalization behavior. The evidence for gender differences in related processes such as perception and memory was inconclusive. Although some of our findings hinted at the possibility that women may be more perceptive of physical differences between stimuli and have more accurate memory than men, those observations were not consistently replicated across experimental conditions and analytical approaches. Our results contribute to the emerging literature on gender differences in perceptual fear generalization in humans and underscore the need for further systematic research to explore the interplay between gender and mechanisms associated with fear generalization across different experimental contexts.

Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation.

PURPOSE: To explore the accuracy and feasibility of a proposed deep learning (DL) algorithm for clinical target volume (CTV) delineation in cervical cancer radiotherapy and evaluate whether it can perform well in external cervical cancer and endometrial cancer cases for generalization validation. METHODS: A total of 332 patients were enrolled in this study. A state-of-the-art network called ResCANet, which added the cascade multi-scale convolution in the skip connections to eliminate semantic differences between different feature layers based on ResNet-UNet. The atrous spatial pyramid pooling in the deepest feature layer combined the semantic information of different receptive fields without losing information. A total of 236 cervical cancer cases were randomly grouped into 5-fold cross-training (n = 189) and validation (n = 47) cohorts. External validations were performed in a separate cohort of 54 cervical cancer and 42 endometrial cancer cases. The performances of the proposed network were evaluated by dice similarity coefficient (DSC), sensitivity (SEN), positive predictive value (PPV), 95% Hausdorff distance (95HD), and oncologist clinical score when comparing them with manual delineation in validation cohorts. RESULTS: In internal validation cohorts, the mean DSC, SEN, PPV, 95HD for ResCANet achieved 74.8%, 81.5%, 73.5%, and 10.5 mm. In external independent validation cohorts, ResCANet achieved 73.4%, 72.9%, 75.3%, 12.5 mm for cervical cancer cases and 77.1%, 81.1%, 75.5%, 10.3 mm for endometrial cancer cases, respectively. The clinical assessment score showed that minor and no revisions (delineation time was shortened to within 30 min) accounted for about 85% of all cases in DL-aided automatic delineation. CONCLUSIONS: We demonstrated the problem of model generalizability for DL-based automatic delineation. The proposed network can improve the performance of automatic delineation for cervical cancer and shorten manual delineation time at no expense to quality. The network showed excellent clinical viability, which can also be even generalized for endometrial cancer with excellent performance.

Exploring transferability in psychiatric interventions: a conceptual framework.

The growing significance of generalization in psychiatric interventions stems from the need for effective and applicable treatments across diverse populations and settings. Addressing psychiatric disorders involves navigating the complex interplay of biological, cognitive, and behavioral factors, making it crucial to assess the transferability of interventions beyond controlled environments. To tackle this challenge, we propose a novel conceptual framework, the FIELD model (Function, Implement, Ecology, Level, and Durability). This model offers a comprehensive exploration of generalization by considering the function and tools used in interventions, the ecological contexts of their application, the various levels of impact, and the durability of effects over time. In this study, we explore the dimensions of the FIELD model, emphasizing the role of assessment tools as valuable indicators and the significance of effect sizes in quantifying the transfer of training effects. The FIELD model stands as a tool to enhance our understanding of psychiatric interventions, providing a systematic and nuanced approach to evaluate their generalization across diverse scenarios.

Sensorimotor adaptation to a non-uniform formant perturbation generalizes to untrained vowels.

When speakers learn to change the way they produce a speech sound, how much does that learning generalize to other speech sounds? Past studies of speech sensorimotor learning have typically tested the generalization of a single transformation learned in a single context. Here, we investigate the ability of the speech motor system to generalize learning when multiple opposing sensorimotor transformations are learned in separate regions of the vowel space. We find that speakers adapt to a non-uniform "centralization" perturbation, learning to produce vowels with greater acoustic contrast, and that this adaptation generalizes to untrained vowels, which pattern like neighboring trained vowels and show increased contrast of a similar magnitude.

Overgeneralization of autonomic defensive reactions in obesity.

Generalizing defensive responses to new stimuli resembling learned threats is an adaptive process within an ever-changing environment. However, evaluation mechanisms excessively biased toward generalization (i.e., overgeneralization) may underlie anxiety-related symptoms. In the context of obesity, fear memory and fear generalization processes have never been investigated. In this study, participants with obesity and healthy participants as controls underwent a single-cue auditory fear conditioning paradigm and recognition memory tasks. We analyzed the autonomic reactions evoked by threat-predictive and new stimuli, as well as the recognition performance towards the same cues. We found that participants with obesity displayed similar autonomic defensive responses to a learned fearful stimulus, but enhanced reactions to new stimuli, when compared with the controls. We detected no significant differences between groups in recognition abilities. Our results provided the first evidence that obesity may widen fear generalization patterns. This alteration may encourage future research in investigating the link between emotional dysregulation and clinical anxiety-related symptoms in obesity.

Nucleus reuniens: Modulating emotional overgeneralization in peri-adolescents with anxiety.

BACKGROUND: Anxiety affects 4.4-million children in the USA with an onset between childhood and adolescence, a period marked by neural changes that impact emotions and memory. Negative overgeneralization - or responding similarly to innocuous events that share features with past aversive experiences - is common in anxiety but remains mechanistically underspecified. The nucleus reuniens (RE) has been considered a crucial candidate in the modulation of memory specificity. Our study investigated its activation and functional connectivity with the medial prefrontal cortex (mPFC) and hippocampus (HPC) as neurobiological mechanisms of negative overgeneralization in anxious youth. METHODS: As part of a secondary data analysis, we examined data from 34 participants between 9 and 14 years of age (mean age ± SD, 11.4 ± 2.0 years; 16 females) with varying degrees of anxiety severity. During the Study session participants rated images as negative, neutral, and positive. After 12 h, participants returned for a Test session, where they performed a memory recognition test with repeated (targets) and similar (lures) images. Labeling negative relative to neutral lures as "old" (false alarms) was our operational definition of negative overgeneralization. RESULTS: Negative relative to neutral false alarmed stimuli displayed elevated RE activation (at Study and Test) and increased functional connectivity with the Cornu Ammonis (CA) 1 (at Test). Elevated anxiety severity was associated with reductions in the RE-mPFC functional coupling for neutral relative to negative stimuli. Exploratory analyses revealed similar patterns in activation and functional connectivity with positive stimuli. CONCLUSIONS: Our findings demonstrate the importance of the RE in negative overgeneralization and anxiety.

High frequency of ambophily in a Brazilian campos de altitude.

BACKGROUND AND AIMS: Ambophily, an intriguing pollination system in which plant species present adaptations to both biotic and abiotic pollination, has been scarcely reported. Most studies have been conducted with a single or few related species from wind-pollinated genera. We here assess for the first time the frequency of ambophily at the community-level. METHODS: We evaluated pollen carried by wind in 63 animal-pollinated species from a Brazilian campos de altitude. For those with pollen carried by wind, we evaluated the contribution of wind and animals to seed production with controlled pollination experiments, as well as floral traits and floral visitor assemblages. KEY RESULTS: Pollen of 23 species were carried by wind (~37%). Animals and wind contributed to the reproduction of seven species (~11%), including one pollinated by hummingbirds, large bees and wind. These seven ambophilous species presented unrestrictive floral morphologies and generalist pollination. CONCLUSIONS: We found a high frequency of ambophily in a single community (11%), which represented an increment of ~5% of species relative to all ambophilous species reported in the literature so far. Investigating pollen transport by wind in zoophilous species combined with controlled experiments helped detect ambophily in species that are usually ignored in wind-pollination studies. Our results showed that putative zoophilous species may actually be ambophilous, suggesting that the selective pressures towards ambophily also occur in zoophilous lineages.

Word form generalization across voices: The role of infant sleep.

Infant sleep plays a crucial role in various aspects of language development, including the generalization of visual and auditory stimuli. The relative role of daytime naps and nocturnal sleep in these memory generalization processes is debated, with some studies observing significant generalization following a post-encoding nap and others observing it following nocturnal sleep, but only in cases where a post-encoding nap had occurred on the previous day. We conducted an online experiment with 8-month-old infants to determine whether a nap immediately following auditory exposure to words spoken by one talker enhances infants' recognition of the same word forms produced by a different talker (i.e., word form generalization). This ability involves the extraction of constant auditory features from a pool of variable auditory instances and thus is an example of memory generalization. Results revealed a significant increase in word form generalization after a night's sleep, specifically in infants who napped shortly after initial exposure to the words. This study provides the first evidence for the combined role of post-encoding naps and nocturnal sleep in phonological learning across different acoustic contexts. Phonological learning is frequently overlooked in research about word learning; however, prior to a child's ability to associate words and their meanings and to use language referentially they must first encode and access the phonological forms of words and recognize them in running speech. Therefore, the findings from this study contribute significantly to our understanding of vocabulary acquisition by highlighting the importance of daytime naps in phonological learning.

SURVIVAL IN A WORLD OF COMPLEX DANGERS.

How do nomadic animals, including humans, continually adapt to the dual challenge of recurrent threats and the seemingly limitless and unpredictable, number of dangers in the natural world? We posit that human defensive behaviors are dynamically constructed to facilitate survival in capricious and itinerant environments. We first hypothesize that internal and external states result in state constructions that combine to form a meta-representation. When a threat is detected, it triggers the action construction. Action constructions are formed through two contiguous survival strategies: generalization strategies, which are used when encountering new threats and ecologies. Generalization strategies are associated with cognitive representations that have high dimensionality and which furnish flexible psychological constructs, including relations between threats, and imagination, and which converge through the construction of defensive states. We posit that generalization strategies drive 'explorative' behaviors including information seeking, where the goal is to increase knowledge that can be used to mitigate current and future threats. Conversely, specialization strategies entail lower dimensional representations, which underpin specialized, sometimes reflexive, or habitual survival behaviors that are 'exploitative'. Together, this captures a central adaptive feature of human survival systems: self-preservation in response to myriad threats.

Generalization limits of Graph Neural Networks in identity effects learning.

Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism to which they have been proven equivalent in terms of expressive power. In this work, we establish new generalization properties and fundamental limits of GNNs in the context of learning so-called identity effects, i.e., the task of determining whether an object is composed of two identical components or not. Our study is motivated by the need to understand the capabilities of GNNs when performing simple cognitive tasks, with potential applications in computational linguistics and chemistry. We analyze two case studies: (i) two-letters words, for which we show that GNNs trained via stochastic gradient descent are unable to generalize to unseen letters when utilizing orthogonal encodings like one-hot representations; (ii) dicyclic graphs, i.e., graphs composed of two cycles, for which we present positive existence results leveraging the connection between GNNs and the WL test. Our theoretical analysis is supported by an extensive numerical study.

A systematic review of generalization research in medical image classification.

Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, a fundamental questions remain: how can these models effectively handle domain shift? This question is crucial to limit DL models performance degradation. Medical data are dynamic and prone to domain shift, due to multiple factors. Two main shift types can occur over time: (1) covariate shift mainly arising due to updates to medical equipment and (2) concept shift caused by inter-grader variability. To mitigate the problem of domain shift, existing surveys mainly focus on domain adaptation techniques, with an emphasis on covariate shift. More generally, no work has reviewed the state-of-the-art solutions while focusing on the shift types. This paper aims to explore existing domain generalization methods for DL-based classification models through a systematic review of literature. It proposes a taxonomy based on the shift type they aim to solve. Papers were searched and gathered on Scopus till 10 April 2023, and after the eligibility screening and quality evaluation, 77 articles were identified. Exclusion criteria included: lack of methodological novelty (e.g., reviews, benchmarks), experiments conducted on a single mono-center dataset, or articles not written in English. The results of this paper show that learning based methods are emerging, for both shift types. Finally, we discuss future challenges, including the need for improved evaluation protocols and benchmarks, and envisioned future developments to achieve robust, generalized models for medical image classification.