No harmful effect of propranolol administered prior to fear memory extinction in rats and humans.

Exposure therapy is an evidence-based treatment option for anxiety-related disorders. Many patients also take medication that could, in principle, affect exposure therapy efficacy. Clinical and laboratory evidence indeed suggests that benzodiazepines may have detrimental effects. Large clinical trials with propranolol, a common beta-blocker, are currently lacking, but several preclinical studies do indicate impaired establishment of safety memories. Here, we investigated the effects of propranolol given prior to extinction training in 9 rat studies (N = 215) and one human study (N = 72). A Bayesian meta-analysis of our rat studies provided strong evidence against propranolol-induced extinction memory impairment during a drug-free test, and the human study found no significant difference with placebo. Two of the rat studies actually suggested a small beneficial effect of propranolol. Lastly, two rat studies with a benzodiazepine (midazolam) group provided some evidence for a harmful effect on extinction memory, i.e., impaired extinction retention. In conclusion, our midazolam findings are in line with prior literature (i.e., an extinction retention impairment), but this is not the case for the 10 studies with propranolol. Our data thus support caution regarding the use of benzodiazepines during exposure therapy, but argue against a harmful effect of propranolol on extinction learning.

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

What a relief! The pleasure of threat avoidance.

Relief, a pleasurable experience, is often triggered by successful threat avoidance. Although relief is regarded as the positive reinforcer for avoidance behavior, its rewarding nature remains to be demonstrated. In our study, 50 participants responded to cues associated with different magnitudes of monetary values or electrical stimuli. Successful responses to those cues resulted in monetary gains (i.e., rewards) or omissions of electrical stimulation (i.e., relief), followed by a pleasantness rating scale. We also measured physiological arousal via skin conductance. As expected, we found that for reward and relief similarly, higher magnitudes elicited more successful responses, higher pleasantness ratings, and higher skin conductance responses. Moreover, differential reward/relief response patterns predicted later choices between reward and relief cues. These findings indicate that relief induced by threat omissions is functionally equivalent to receiving a reward, confirming that relief is a positive reinforcer for threat avoidance behaviors, which provides a new theoretical perspective on the learning process of active threat avoidance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Effects of whole-body vibration training on cognitive function: A systematic review.

Background: Whole-body vibration (WBV) training is a novel training method that stimulates the human neuromuscular system by the use of vibration, the frequency and amplitude of which are controlled, thereby inducing adaptive changes in the body. WBV training is widely used as a clinical prevention and rehabilitation tool in physical medicine and neuro-rehabilitation as a clinical prevention and rehabilitation tool. Objectives: The aim of the present study was to review the effects of WBV on cognitive function, provide an evidence-based foundation for future research on WBV training, and promote additional popularization and use of the methodology in clinical practice. Methods: A systematic review of articles extracted from the following six databases was conducted: PubMed, Web of Science, China National Knowledge Infrastructure, Embase, Cochrane, and Scopus. A literature search was performed on articles in which the effects of WBV on cognitive function were evaluated. Results: Initially, a total of 340 studies were initially identified, among which 18 articles that satisfied the inclusion criteria were selected for inclusion in the systematic review. Participants were allocated into two groups: patients with cognitive impairment and healthy individuals. The results demonstrated that WBV was both positive and ineffective in its influence on cognitive function. Conclusion: The majority of studies suggested that WBV may be a useful strategy for the management of cognitive impairment and should be considered for inclusion in rehabilitation programs. However, the impact of WBV on cognition requires additional, larger, and adequately powered studies. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=376821, identifier CRD42022376821.

Inception Convolution and Feature Fusion for Person Search.

With the rapid advancement of deep learning theory and hardware device computing capacity, computer vision tasks, such as object detection and instance segmentation, have entered a revolutionary phase in recent years. As a result, extremely challenging integrated tasks, such as person search, might develop quickly. The majority of efficient network frameworks, such as Seq-Net, are based on Faster R-CNN. However, because of the parallel structure of Faster R-CNN, the performance of re-ID can be significantly impacted by the single-layer, low resolution, and occasionally overlooked check feature diagrams retrieved during pedestrian detection. To address these issues, this paper proposed a person search methodology based on an inception convolution and feature fusion module (IC-FFM) using Seq-Net (Sequential End-to-end Network) as the benchmark. First, we replaced the general convolution in ResNet-50 with the new inception convolution module (ICM), allowing the convolution operation to effectively and dynamically distribute various channels. Then, to improve the accuracy of information extraction, the feature fusion module (FFM) was created to combine multi-level information using various levels of convolution. Finally, Bounding Box regression was created using convolution and the double-head module (DHM), which considerably enhanced the accuracy of pedestrian retrieval by combining global and fine-grained information. Experiments on CHUK-SYSU and PRW datasets showed that our method has higher accuracy than Seq-Net. In addition, our method is simpler and can be easily integrated into existing two-stage frameworks.

No joy - why bother? Higher anhedonia relates to reduced pleasure from and motivation for threat avoidance.

Anhedonia impairs various components of the pleasure cycle, including wanting, liking, and the learning of pleasure-related associations. While successfully controlling threats might be inherently pleasurable, it remains unclear whether anhedonia affects this form of pleasure as well. With aversive pictures as threats, we conducted an online study ( N = 200) to investigate the role of anhedonia during active avoidance learning process. Participants first learned cue-threat associations for different cues (threat vs. safety cues). In a subsequent avoidance learning phase, these cues signaled either avoidable, unavoidable, or no threat; participants could perform avoidance responses to prevent the upcoming threats during those cue presentations. Subjective relief pleasantness was measured after each threat omission. We found that higher trait anticipatory and consummatory anhedonia were both associated with lower relief pleasantness. Higher trait anticipatory anhedonia was also associated with fewer avoidance attempts. Since reduced threat-controlling behavior is reminiscent of a learned-helplessness state, the current results contribute to a better understanding of the connections between anhedonia and learned helplessness that have mostly been studied separately in the context of mood disturbance.

More engagement in inefficient avoidance through partial reinforcement.

BACKGROUND AND OBJECTIVES: In anxiety-related disorders, excessive avoidance often coexists with an impaired sense of control over external threats. In contrast, lab studies have shown that avoidance responding increase with higher objective controllability over threat, accompanied with more confidence in the effectiveness of the avoidance response. One reason for this divergence could be that those lab studies are overly simplistic with a single, avoidable threat. METHODS: We conducted an experiment that additionally included a completely uncontrollable threat, and we manipulated the reinforcement rate of the avoidance response to the (semi-)controllable threat (75% versus 100%). RESULTS: The 100% group showed increased avoidance to the controllable threat and decreased avoidance to the unavoidable threat over learning. Interestingly, compared to the 100% group, the 75% group displayed less confidence in their avoidance to the controllable threat and they avoided the uncontrollable threat more often. LIMITATIONS: Only two reinforcement rates of effective avoidance were included, which may limit the generalizability of the current findings. Perceived control was not directly measured. CONCLUSIONS: Lower reinforcement rates create ambiguity between effective and ineffective situations of avoidance, which engenders generalization of unpredictability from effective to ineffective situation, thereby driving up ineffective avoidance rates. Partially reinforced effective avoidance responses and elevated ineffective avoidance responses together lead to more exposure to uncontrollable threat, weakening the sense of control over the threat, which could further increase avoidance behaviors. Controllability is often overlooked in avoidance research but can be key to understanding the development of maladaptive avoidance behaviors.

A computer-aid multi-task light-weight network for macroscopic feces diagnosis.

The abnormal traits and colors of feces typically indicate that the patients are probably suffering from tumor or digestive-system diseases. Thus a fast, accurate and automatic health diagnosis system based on feces is urgently necessary for improving the examination speed and reducing the infection risk. The rarity of the pathological images would deteriorate the accuracy performance of the trained models. In order to alleviate this problem, we employ augmentation and over-sampling to expand the samples of the classes that have few samples in the training batch. In order to achieve an impressive recognition performance and leverage the latent correlation between the traits and colors of feces pathological samples, a multi-task network is developed to recognize colors and traits of the macroscopic feces images. The parameter number of a single multi-task network is generally much smaller than the total parameter number of multiple single-task networks, so the storage cost is reduced. The loss function of the multi-task network is the weighted sum of the losses of the two tasks. In this paper, the weights of the tasks are determined according to their difficulty levels that are measured by the fitted linear functions. The sufficient experiments confirm that the proposed method can yield higher accuracies, and the efficiency is also improved.

Reinforced Palmprint Reconstruction Attacks in Biometric Systems.

Biometric signals can be acquired with different sensors and recognized in secure identity management systems. However, it is vulnerable to various attacks that compromise the security management in many applications, such as industrial IoT. In a real-world scenario, the target template stored in the database of a biometric system can possibly be leaked, and then used to reconstruct a fake image to fool the biometric system. As such, many reconstruction attacks have been proposed, yet unsatisfactory naturalness, poor visual quality or incompleteness remains as major limitations. Thus, two reinforced palmprint reconstruction attacks are proposed. Any palmprint image, which can be easily obtained, is used as the initial image, and the region of interest is iteratively modified with deep reinforcement strategies to reduce the matching distance. In the first attack, Modification Constraint within Neighborhood (MCwN) limits the modification extent and suppresses the reckless modification. In the second attack, Batch Member Selection (BMS) selects the significant pixels (SPs) to compose the batch, which are simultaneously modified to a slighter extent to reduce the matching number and the visual-quality degradation. The two reinforced attacks can satisfy all the requirements, which cannot be simultaneously satisfied by the existing attacks. The thorough experiments demonstrate that the two attacks have a highly successful attack rate for palmprint systems based on the most state-of-the-art coding-based methods.

Study on the chemical composition of Caesalpinia sinensis.

Five compounds were isolated from the methanolic extract of Caesalpinia sinensis stems and leaves including a new cassane-type butenolide norditerpenoid compound (1) and a new type of biphenyl compound (2); the compounds were identified as Norcaesalpin-one (1), 4'-hexyl 3-methyl 6-methoxy-[1,1'-biphenyl]-3,4'-dicarboxylate (2), rhapontigenin (3), 3-deoxysappanchalcone (4), isoliquiritigenin (5). Compounds 1-5 were first isolated from C. sinensis. Their structures were elucidaded on the basis of MS, IR, NMR spectroscopic, X-ray diffraction data analyses. The NGF-induced PC12 differentiation assay was performed on compound 1, and the results showed that compound 1 had a promotive effect on PC12 cell differentiation, with a differentiation rate of 12.32%. In addition, compounds 1-5 were evaluated for their cytotoxic activities against four human cancer cell lines (including A-549, BGC-823, MDA-MB-231, HepG2), and the results showed that compounds 3-5 showed inhibitory activity against these cancer cell lines with IC50 values ranging from 22.96 to 74.92 µmol/L, compound 4 showing the best activity against human malignant melanoma cells A375 with an IC50 value of 22.96 µmol/L.

Short-Term High-Intensity Interval Exercise Promotes Motor Cortex Plasticity and Executive Function in Sedentary Females.

Previous research has demonstrated that regular exercise modulates motor cortical plasticity and cognitive function, but the influence of short-term high-intensity interval training (HIIT) remains unclear. In the present study, the effect of short-term HIIT on neuroplasticity and executive function was assessed in 32 sedentary females. Half of the participants undertook 2 weeks of HIIT. Paired-pulse transcranial magnetic stimulation (ppTMS) was used to measure motor cortical plasticity via short intracortical inhibition (SICI) and intracortical facilitation (ICF). We further adapted the Stroop task using functional near-infrared spectroscopy (fNIRS) to evaluate executive function in the participants. The results indicated that, compared with the control group, the HIIT group exhibited decreased ICF. In the Stroop task, the HIIT group displayed greater activation in the left dorsolateral prefrontal cortex (DLPFC) and left orbitofrontal cortex (OFC) even though no significant difference in task performance was observed. These findings indicate that short-term HIIT may modulate motor cortical plasticity and executive function at the neural level.

No persistent attenuation of fear memories in humans: A registered replication of the reactivation-extinction effect.

It has been proposed that memory retrieval can destabilize consolidated memories, after which they need to be reconsolidated in order to be retained. The presentation of relevant information during memory reconsolidation could then result in the modification of a destabilized memory trace, by allowing the memory trace to be updated before being reconsolidated. In line with this idea, Schiller et al. (2010) have demonstrated that memory retrieval shortly before extinction training can prevent the later recovery of conditioned fear responding that is observed after regular extinction training. Those findings have been the subject of considerable controversy, due in part to theoretical reasons but also due to a number of failures to obtain similar results in conceptual replication attempts. Here, we report the results of a highly powered, direct, independent replication of the critical conditions of Schiller et al. (2010, Experiment 1). Due to misrepresentation of the exclusion criteria in the original Schiller et al. (2010) report, data collection was considerably delayed. When we eventually managed to attain our pre-registered sample size, we found that we could not observe any benefit of reactivation-extinction over regular extinction training in preventing recovery of conditioned fear. The results of the present study, along with the mixed findings in the literature and the misreporting in Schiller et al. (2010), give cause to question whether there is robust evidence that reactivation-extinction prevents the return of fear in humans.

A Light-Weight Practical Framework for Feces Detection and Trait Recognition.

Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.

The Role of Philosophical Inquiry in Helping Students Engage in Learning.

Studies have characterized high school students as bored, alienated, and disconnected with their class and the learning process. In order to address this problem to improve student learning engagement, this study explores the impact of philosophical inquiry (PI) on the development of adolescents' academic engagement and adds to the scholarly research on Philosophy for Children (P4C). In determining an appropriate and holistic approach to investigating students' learning engagement and motivation from the perspective of psychology, this study involves multiple forms of data collection, specifically including surveys, student work, focus group interviews, classroom discussions, and reflective notes. Applying a qualitative method, this multiple case study developed a deeper understanding of the classroom contexts, conditions, discourses, tools, and practices that promote positive adolescent learning experiences. The study developed a conceptual framework of student academic engagement in a PI class and summarized reasons why the participants engaged in learning. First, students believed that maintaining a safe and positive classroom environment is a fundamental condition for learning. Second, they reported that asking questions, sharing ideas, listening attentively, thinking deeply, and making connections are the manifestations of an engaging classroom. Third, students reported that they transcended their learning experiences by living a new philosophy that was acquired in the process of the community of inquiry. The study found that PI enhanced social inclusion and active participation of the participant in the learning process.

Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation.

With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability.

[A new lupane-type triterpenoid from Dichroa hirsuta].

The chemical constituents from methanol extract of Dichroa hirsuta were separated by silica gel and Sephadex LH-20 column chromatography,high pressure preparative liquid chromatography( HPLC) and recrystallization. Their structures were elucidated by NMR and MS. Nine compounds were obtained and their structures were identified as 3ß,21α-O-diacetyl-lup-9( 11)-en-7ß-ol( 1),( Z)-methyl p-hydroxycinnamate( 2),cis-p-coumaric acid ethyl ester( 3),( E)-methyl p-hydroxycinnamate( 4),trans-p-coumaric acid ethyl ester( 5),4( 3 H)-quinazolinone( 6),7-hydroxycoumarin( 7),hydrangenol( 8) and thunberginol C( 9). Compound 1 is a new lupane-type triterpenoid,and compounds 1-5,8-9 were firstly isolated from this plant. Dual reporter assay results showed that compounds 2-5 could activate the Nrf2-ARE signaling pathway.

Rapid Formation of Aerobic Granular Sludge and Its Mechanism in a Continuous-Flow Bioreactor.

Based on the principle of self-coagulation of microorganisms, the flocculant-producing denitrifying bacterial TN-14 sludge was added to the continuous-flow reactor for treating domestic sewage. The bacterial TN-14 sludge acted as the main seed sludge to promote the rapid formation of aerobic granular sludge. The sludge morphology, sludge volume index (SVI) values, amounts of extracellular polymeric substances (EPS), and the role of calcium in the granulation process of the sludge were investigated. Results showed that brown aerobic granules with the particle size of 0.5 ~ 2.0 mm was successfully cultivated at 40 days, and its SVI30 decreased from 122.62 mL g-1 initially to 46.61 mL g-1 and remained at 44.28 ~ 60.51 mL g-1 afterwards. The protein (PN) content in sludge EPS increased from 76.4 mg g-1 initially to 512.3 mg g-1. Compared with PN, the polysaccharide (PS) content did not change much throughout the operation process of the bioreactor. Energy-dispersive spectrum (EDS) showed that Ca elements were deposited inside the granular sludge, and X-ray diffraction (XRD) showed that Ca elements existed in the granular sludge in the forms of CaCO3, K2CaP2O7, Ca2P2O7, and Ca4O(PO4)2. The formation mechanism of continuous-flow aerobic granular sludge was that bacterial TN-14 sludge could promote the EPS content of sludge, and PN content of EPS increases the hydrophobicity and settling performance of the sludge. Calcium mainly exists in the granular sludge in the form of inorganic calcium phosphate, and therefore plays the role of nucleation in sludge granulation.

[Rapid Start-up of Simultaneous Nitrification and Denitrification Coupled Phosphorus Removal Process and Its Performing Characteristics].

In this study, simultaneous nitrification and denitrification (SND) coupled Phosphorus removal process through gradually decreasing DO concentration was investigated by treating wastewater with a low COD/TN ratio (C/N = 3 : 1-4: 1) in a sequencing batch reactor (SBR) inoculated with aerobic granular sludge (AGS). Successful SND coupled Phosphorus phenomenon occurred after 20d at the DO concentration of 0.50-1.0 mg x L(-1). In the following 40 days, the average removal rates of COD, NH4(+) -N, TN and TP were 84.84% , 93.51%, 77.06% and 85.69%, and the NO3(-) -N and NO2(-) -N average accumulations in the effluent were only 4.01 mg x L(-1) and 3.17 mg x L(-1), respectively. The AGS had complete forms and good settling performances, and the sludge volume index (SVI) was about 55.22 mL x g(-1) at the end of starting-up stage. The results of different nitrogen sources showed that the removal rate of TN was in the order of NH4(+) -N > NO2(-) -N > NO3(-) -N, and the removal rate of TP was in the order of NO3(-) -N > NO2(-) -N > NH4(+) -N. The nitrogen and phosphorus removal of wastewater were mainly realized by simultaneous nitrification and denitrification and denitrifying phosphorus removal, respectively.

[Identification of a high ammonia nitrogen tolerant and heterotrophic nitrification-aerobic denitrification bacterial strain TN-14 and its nitrogen removal capabilities].

A new strain of high ammonia nitrogen tolerant and heterotrophic nitrification-aerobic denitrification bacterium TN-14 was isolated from the environment. Its physiological and biochemical characteristics and molecular identification, performences of heterotrophic nitrification-aerobic, the abilities of resistance to ammonia nitrogen as well as the decontamination abilities were studied, respectively. It was preliminary identified as Acinetobacter sp. according to its physiological and biochemical characteristics and molecular identification results. In heterotrophic nitrification system, the ammonia nitrogen and total nitrogen removal rate of the bacterial strain TN-14 could reach 97.13% and 93.53% within 24 h. In nitrates denitrification system, the nitrate concentration could decline from 94.24 mg · L(-1) to 39.32 mg · L(-1) within 24 h, where the removal rate was 58.28% and the denitrification rate was 2.28 mg · (L · h)(-1); In nitrite denitrification systems, the initial concentration of nitrite could be declined from 97.78 mg · L(-1) to 21.30 mg x L(-1), with a nitrite nitrogen removal rate of 78.22%, and a denitrification rate of 2.55 mg · (L· h)(-1). Meanwhile, strain TN-14 had the capability of flocculant production, and the flocculating rate could reach 94.74% when its fermentation liquid was used to treat 0.4% kaolin suspension. Strain TN-14 could grow at an ammonia nitrogen concentration as high as 1200 mg · L(-1). In the aspect of actual piggery wastewater treatment by strain TN-14, the removal rate of COD, ammonia nitrogen, TN and TP cloud reached 85.30%, 65.72%, 64.86% and 79.41%, respectively. Strain TN-14 has a good application prospect in biological treatment of real high- ammonia wastewater.