Reduced generalization of reward among individuals with subthreshold depression: Behavioral and EEG evidence

Altered stimulus generalization has been well-documented in anxiety disorders; however, there is a paucity of research investigating this phenomenon in the context of depression. Depression is characterized by impaired reward processing and heightened attention to negative stimuli. It is hypothesized that individuals with depression exhibit reduced generalization of reward stimuli and enhanced generalization of loss stimuli. Nevertheless, no study has examined this process and its underlying neural mechanisms. In the present study, we recruited 25 participants with subthreshold depression (SD group) and 24 age-matched healthy controls (HC group). Participants completed an acquisition task, in which they learned to associate three distinct pure tones (conditioned stimuli, CSs) with a reward, a loss, or no outcome. Subsequently, a generalization session was conducted, during which similar tones (generalization stimuli, GSs) were presented, and participants were required to classify them as a reward tone, a loss tone, or neither. The results revealed that the SD group exhibited reduced generalization errors in the early phase of generalization, suggesting a diminished ability to generalize reward-related stimuli. The event-related potential (ERP) results indicated that the SD group exhibited decreased generalization of positive valence to reward-related GSs and heightened generalization of negative valence to loss-related GSs, as reflected by the N1 and P2 components. However, the late positive potential (LPP) was not modulated by depression in reward generalization or loss generalization. These findings suggested that individuals with subthreshold depression may have a blunted or reduced ability to generalize reward stimuli, shedding light on potential treatment strategies targeting this particular process.

A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras

Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. The imaging examinations and modalities used can vary between subjects depending on their individualized clinical pathways. Different imaging modalities may have various fields of view and are sensitive to signals from other tissues/organs, or both. Hence, missing/partially overlapping anatomical information is often available across individuals. We introduce a generative shape model for multipart anatomical structures, learnable from sets of unpaired datasets, i.e., where each substructure in the shape assembly comes from datasets with missing or partially overlapping substructures from disjoint subjects of the same population. The proposed generative model can synthesize complete multipart shape assemblies coined virtual chimeras (VCs). We applied this framework to build VCs from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a graph neural network-based generative shape compositional framework, which comprises two components, a part-aware generative shape model that captures the variability in shape observed for each structure of interest in the training population and a spatial composition network that assembles/composes the structures synthesized by the former into multipart shape assemblies (i.e., VCs). We also propose a novel self-supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance (MR) images in the UK Biobank (UKBB). When trained with complete and partially overlapping data, our approach significantly outperforms a principal component analysis (PCA)-based shape model (trained with complete data) in terms of generalizability and specificity. This demonstrates the superiority of the proposed method, as the synthesized cardiac virtual populations are more plausible and capture a greater degree of shape variability than those generated by the PCA-based shape model.

Test-time bi-directional adaptation between image and model for robust segmentation

BACKGROUND AND OBJECTIVE

Deep learning models often suffer from performance degradations when deployed in real clinical environments due to appearance shifts between training and testing images. Most extant methods use training-time adaptation, which almost require target domain samples in the training phase. However, these solutions are limited by the training process and cannot guarantee the accurate prediction of test samples with unforeseen appearance shifts. Further, it is impractical to collect target samples in advance. In this paper, we provide a general method of making existing segmentation models robust to samples with unknown appearance shifts when deployed in daily clinical practice.

METHODS

Our proposed test-time bi-directional adaptation framework combines two complementary strategies. First, our image-to-model (I2M) adaptation strategy adapts appearance-agnostic test images to the learned segmentation model using a novel plug-and-play statistical alignment style transfer module during testing. Second, our model-to-image (M2I) adaptation strategy adapts the learned segmentation model to test images with unknown appearance shifts. This strategy applies an augmented self-supervised learning module to fine-tune the learned model with proxy labels that it generates. This innovative procedure can be adaptively constrained using our novel proxy consistency criterion. This complementary I2M and M2I framework demonstrably achieves robust segmentation against unknown appearance shifts using existing deep-learning models.

RESULTS

Extensive experiments on 10 datasets containing fetal ultrasound, chest X-ray, and retinal fundus images demonstrate that our proposed method achieves promising robustness and efficiency in segmenting images with unknown appearance shifts.

CONCLUSIONS

To address the appearance shift problem in clinically acquired medical images, we provide robust segmentation by using two complementary strategies. Our solution is general and amenable for deployment in clinical settings.

The relationship between autistic traits and empathy in adolescents: An ERP study

Based on the mind-blindness hypothesis, a large number of studies have shown that individuals with autism-spectrum disorder (ASD) and autistic traits have empathy deficits. However, the recent double empathy theory contradicts the mind-blindness hypothesis and suggests that individuals with ASD and autistic traits do not necessarily lack empathy. Thus, the presence of empathy deficits in individuals with ASD and autistic traits is still controversial. We recruited 56 adolescents (28 high autistic traits, 28 low autistic traits, 14-17 years old) in this study to explore the relationship between empathy and autistic traits. The study participants were required to undertake the pain empathy task, during which the electroencephalograph (EEG) activities were recorded. Our results show that empathy was negatively associated with autistic traits at the questionnaire, behavioral, and EEG levels. Our results also suggested that empathy deficits in adolescents with autistic traits may be manifested mainly in the late stages of cognitive control processing.

RecON: Online learning for sensorless freehand 3D ultrasound reconstruction

Sensorless freehand 3D ultrasound (US) reconstruction based on deep networks shows promising advantages, such as large field of view, relatively high resolution, low cost, and ease of use. However, existing methods mainly consider vanilla scan strategies with limited inter-frame variations. These methods thus are degraded on complex but routine scan sequences in clinics. In this context, we propose a novel online learning framework for freehand 3D US reconstruction under complex scan strategies with diverse scanning velocities and poses. First, we devise a motion-weighted training loss in training phase to regularize the scan variation frame-by-frame and better mitigate the negative effects of uneven inter-frame velocity. Second, we effectively drive online learning with local-to-global pseudo supervisions. It mines both the frame-level contextual consistency and the path-level similarity constraint to improve the inter-frame transformation estimation. We explore a global adversarial shape before transferring the latent anatomical prior as supervision. Third, we build a feasible differentiable reconstruction approximation to enable the end-to-end optimization of our online learning. Experimental results illustrate that our freehand 3D US reconstruction framework outperformed current methods on two large, simulated datasets and one real dataset. In addition, we applied the proposed framework to clinical scan videos to further validate its effectiveness and generalizability.

Impact of observational and direct learning on fear conditioning generalization in humans

Humans gain knowledge about threats not only from their own experiences but also from observing others' behavior. A neutral stimulus is associated with a threat stimulus for several times and the neutral stimulus will evoke fear responses, which is known as fear conditioning. When encountering a new event that is similar to one previously associated with a threat, one may feel afraid and produce fear responses. This is called fear generalization. Previous studies have mostly focused on fear conditioning and generalization based on direct learning, but few have explored how observational fear learning affects fear conditioning and generalization. To the best of our knowledge, no previous study has focused on the neural correlations of fear conditioning and generalization based on observational learning. In the present study, 58 participants performed a differential conditioning paradigm in which they learned the associations between neutral cues (i.e., geometric figures) and threat stimuli (i.e., electric shock). The learning occurred on their own (i.e., direct learning) and by observing other participant's responses (i.e., observational learning); the study used a within-subjects design. After each learning condition, a fear generalization paradigm was conducted by each participant independently while their behavioral responses (i.e., expectation of a shock) and electroencephalography (EEG) recordings or responses were recorded. The shock expectancy ratings showed that observational learning, compared to direct learning, reduced the differentiation between the conditioned threatening stimuli and safety stimuli and the increased shock expectancy to the generalization stimuli. The EEG indicated that in fear learning, threatening conditioned stimuli in observational and direct learning increased early discrimination (P1) and late motivated attention (late positive potential [LPP]), compared with safety conditioned stimuli. In fear generalization, early discrimination, late motivated attention, and orienting attention (alpha-event-related desynchronization [alpha-ERD]) to generalization stimuli were reduced in the observational learning condition. These findings suggest that compared to direct learning, observational learning reduces differential fear learning and increases the generalization of fear, and this might be associated with reduced discrimination and attentional function related to generalization stimuli.

Medical Image Segmentation Using Transformer Networks

Deep learning models represent the state of the art in medical image segmentation. Most of these models are fully-convolutional networks (FCNs), namely each layer processes the output of the preceding layer with convolution operations. The convolution operation enjoys several important properties such as sparse interactions, parameter sharing, and translation equivariance. Because of these properties, FCNs possess a strong and useful inductive bias for image modeling and analysis. However, they also have certain important shortcomings, such as performing a fixed and pre-determined operation on a test image regardless of its content and difficulty in modeling long-range interactions. In this work we show that a different deep neural network architecture, based entirely on self-attention between neighboring image patches and without any convolution operations, can achieve more accurate segmentations than FCNs. Our proposed model is based directly on the transformer network architecture. Given a 3D image block, our network divides it into non-overlapping 3D patches and computes a 1D embedding for each patch. The network predicts the segmentation map for the block based on the self-attention between these patch embeddings. Furthermore, in order to address the common problem of scarcity of labeled medical images, we propose methods for pre-training this model on large corpora of unlabeled images. Our experiments show that the proposed model can achieve segmentation accuracies that are better than several state of the art FCN architectures on two datasets. Our proposed network can be trained using only tens of labeled images. Moreover, with the proposed pre-training strategies, our network outperforms FCNs when labeled training data is small.

Attachment voices promote safety learning in humans: A critical role for P2

Humans have evolved to seek the proximity of attachment figures during times of threat in order to obtain a sense of safety. In this context, we examined whether or not the voice of an intimate partner (termed "attachment voice") could reduce fear-learning of conditioned stimuli (CS+) and enhance learning of safety signals (CS-). Although the ability to learn safety signals is vital for human survival, few studies have explored how attachment voices affect safety learning. To test our hypothesis, we recruited thirty-five young couples and performed a classic Pavlovian conditioning experiment, recording behavioral and electroencephalographic (EEG) data. The results showed that compared with a stranger's voice, the voices of the partners reduced expectancy of the unconditioned stimulus (a shock) during fear-conditioning, as well as the magnitude of P2 event-related potentials within the EEG responses, provided the voices were safety signals. Additionally, behavioral and EEG responses to the CS+ and CS- differed more when the participants heard their partner's voice than when they heard the stranger's voice. Thus, attachment voices, even as pure vowel sounds without any semantic information, enhanced acquisition of conditioned safety (CS-). These findings may provide implications for investigating other new techniques to improve clinical treatments for fear- and anxiety-related disorders and for psychological interventions against the mental health effects of the public health emergency.

Agent With Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound

Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine the clinical diagnosis. 2D US has to perform scanning for each SP, which is time-consuming and operator-dependent. While 3D US containing multiple SPs in one shot has the inherent advantages of less user-dependency and more efficiency. Automatically locating SP in 3D US is very challenging due to the huge search space and large fetal posture variations. Our previous study proposed a deep reinforcement learning (RL) framework with an alignment module and active termination to localize SPs in 3D US automatically. However, termination of agent search in RL is important and affects the practical deployment. In this study, we enhance our previous RL framework with a newly designed adaptive dynamic termination to enable an early stop for the agent searching, saving at most 67% inference time, thus boosting the accuracy and efficiency of the RL framework at the same time. Besides, we validate the effectiveness and generalizability of our algorithm extensively on our in-house multi-organ datasets containing 433 fetal brain volumes, 519 fetal abdomen volumes, and 683 uterus volumes. Our approach achieves localization error of 2.52mm/10.26^° , 2.48mm/10.39^° , 2.02mm/10.48^° , 2.00mm/14.57^° , 2.61mm/9.71^° , 3.09mm/9.58^° , 1.49mm/7.54^° for the transcerebellar, transventricular, transthalamic planes in fetal brain, abdominal plane in fetal abdomen, and mid-sagittal, transverse and coronal planes in uterus, respectively. Experimental results show that our method is general and has the potential to improve the efficiency and standardization of US scanning.

AW3M: An auto-weighting and recovery framework for breast cancer diagnosis using multi-modal ultrasound

Recently, more clinicians have realized the diagnostic value of multi-modal ultrasound in breast cancer identification and began to incorporate Doppler imaging and Elastography in the routine examination. However, accurately recognizing patterns of malignancy in different types of sonography requires expertise. Furthermore, an accurate and robust diagnosis requires proper weights of multi-modal information as well as the ability to process missing data in practice. These two aspects are often overlooked by existing computer-aided diagnosis (CAD) approaches. To overcome these challenges, we propose a novel framework (called AW3M) that utilizes four types of sonography (i.e. B-mode, Doppler, Shear-wave Elastography, and Strain Elastography) jointly to assist breast cancer diagnosis. It can extract both modality-specific and modality-invariant features using a multi-stream CNN model equipped with self-supervised consistency loss. Instead of assigning the weights of different streams empirically, AW3M automatically learns the optimal weights using reinforcement learning techniques. Furthermore, we design a light-weight recovery block that can be inserted to a trained model to handle different modality-missing scenarios. Experimental results on a large multi-modal dataset demonstrate that our method can achieve promising performance compared with state-of-the-art methods. The AW3M framework is also tested on another independent B-mode dataset to prove its efficacy in general settings. Results show that the proposed recovery block can learn from the joint distribution of multi-modal features to further boost the classification accuracy given single modality input during the test.

Irrelevant task suppresses the N170 of automatic attention allocation to fearful faces

Recent researches have provided evidence that stimulus-driven attentional bias for threats can be modulated by top-down goals. However, it is highlight essential to indicate whether and to what extent the top-down goals can affect the early stage of attention processing and its early neural mechanism. In this study, we collected electroencephalographic data from 28 healthy volunteers with a modified spatial cueing task. The results revealed that in the irrelevant task, there was no significant difference between the reaction time (RT) of the fearful and neutral faces. In the relevant task, we found that RT of fearful faces was faster than that of neutral faces in the valid cue condition, whereas the RT of fearful faces was slower than that of neutral faces in the invalid cue condition. The N170 component in our study showed a similar result compared with RT. Specifically, we noted that in the relevant task, fearful faces in the cue position of the target evoked a larger N170 amplitude than neutral faces, whereas this effect was suppressed in the irrelevant task. These results suggest that the irrelevant task may inhibit the early attention allocation to the fearful faces. Furthermore, the top-down goals can modulate the early attentional bias for threatening facial expressions.

Searching collaborative agents for multi-plane localization in 3D ultrasound

3D ultrasound (US) has become prevalent due to its rich spatial and diagnostic information not contained in 2D US. Moreover, 3D US can contain multiple standard planes (SPs) in one shot. Thus, automatically localizing SPs in 3D US has the potential to improve user-independence and scanning-efficiency. However, manual SP localization in 3D US is challenging because of the low image quality, huge search space and large anatomical variability. In this work, we propose a novel multi-agent reinforcement learning (MARL) framework to simultaneously localize multiple SPs in 3D US. Our contribution is four-fold. First, our proposed method is general and it can accurately localize multiple SPs in different challenging US datasets. Second, we equip the MARL system with a recurrent neural network (RNN) based collaborative module, which can strengthen the communication among agents and learn the spatial relationship among planes effectively. Third, we explore to adopt the neural architecture search (NAS) to automatically design the network architecture of both the agents and the collaborative module. Last, we believe we are the first to realize automatic SP localization in pelvic US volumes, and note that our approach can handle both normal and abnormal uterus cases. Extensively validated on two challenging datasets of the uterus and fetal brain, our proposed method achieves the average localization accuracy of 7.03^∘/1.59mm and 9.75^∘/1.19mm. Experimental results show that our light-weight MARL model has higher accuracy than state-of-the-art methods.

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. We evaluated our method quantitatively based on several performance measures and expert evaluations. Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique. We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned in the gestational age range of 16 to 39 weeks (28.6± 5.3). With a computation time of less than 1 minute per fetal brain, our method can facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.

Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis

Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Recent studies have shown that label noise can significantly impact the performance of deep learning models in many machine learning and computer vision applications. This is especially concerning for medical applications, where datasets are typically small, labeling requires domain expertise and suffers from high inter- and intra-observer variability, and erroneous predictions may influence decisions that directly impact human health. In this paper, we first review the state-of-the-art in handling label noise in deep learning. Then, we review studies that have dealt with label noise in deep learning for medical image analysis. Our review shows that recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image analysis community. To help achieve a better understanding of the extent of the problem and its potential remedies, we conducted experiments with three medical imaging datasets with different types of label noise, where we investigated several existing strategies and developed new methods to combat the negative effect of label noise. Based on the results of these experiments and our review of the literature, we have made recommendations on methods that can be used to alleviate the effects of different types of label noise on deep models trained for medical image analysis. We hope that this article helps the medical image analysis researchers and developers in choosing and devising new techniques that effectively handle label noise in deep learning.

Hybrid attention for automatic segmentation of whole fetal head in prenatal ultrasound volumes

BACKGROUND AND OBJECTIVE

Biometric measurements of fetal head are important indicators for maternal and fetal health monitoring during pregnancy. 3D ultrasound (US) has unique advantages over 2D scan in covering the whole fetal head and may promote the diagnoses. However, automatically segmenting the whole fetal head in US volumes still pends as an emerging and unsolved problem. The challenges that automated solutions need to tackle include the poor image quality, boundary ambiguity, long-span occlusion, and the appearance variability across different fetal poses and gestational ages. In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes.

METHODS

The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features in a composite and hierarchical way. With little computation overhead, HAS proves to be effective in addressing boundary ambiguity and deficiency. To enhance the spatial consistency in segmentation, we further organize multiple segmentors in a cascaded fashion to refine the results by revisiting context in the prediction of predecessors.

RESULTS

Validated on a large dataset collected from 100 healthy volunteers, our method presents superior segmentation performance (DSC (Dice Similarity Coefficient), 96.05%), remarkable agreements with experts (-1.6±19.5 mL). With another 156 volumes collected from 52 volunteers, we ahieve high reproducibilities (mean standard deviation 11.524 mL) against scan variations.

CONCLUSION

This is the first investigation about whole fetal head segmentation in 3D US. Our method is promising to be a feasible solution in assisting the volumetric US-based prenatal studies.

Social exclusion influences conditioned fear acquisition and generalization: A mediating effect from the medial prefrontal cortex

Fear acquisition and generalization play key roles in promoting the survival of mammals and contribute to anxiety disorders. While previous research has provided much evidence for the repercussions of social exclusion on mental health, how social exclusion affects fear acquisition and generalization has received scant attention. In our study, participants were divided into two groups according to two Cyberball paradigm conditions (exclusion/inclusion). Both groups underwent a Pavlovian conditioning paradigm, functional near-infrared spectroscopy (fNIRS), and skin conductance response (SCR) assessments. We aimed to determine the effects of social exclusion on fear acquisition and generalization and whether modulation of the medial prefrontal cortex (mPFC) mediates this relationship. Our results showed that socially excluded participants featured significantly higher and lower shock risk scores to safety stimuli (conditioned stimulus, CS-) and threatening stimuli (CS+), respectively, than did socially included subjects during fear acquisition. The exclusion group had increased skin conductance responses (SCRs) to CS and exhibited heightened shock risk and increased SCRs to generalized stimuli compared with the inclusion group. The fNIRS results demonstrated that the CS ​+ ​evoked larger oxy-Hb changes in the mPFC in the inclusion group than in the exclusion group during fear acquisition. Furthermore, the oxy-Hb of left mPFC of CS ​+ ​mediated the effect on the association between social exclusion and perceived risk of CS+ in the fear acquisition. Our results indicate that social exclusion impairs fear acquisition and generalization via the mediation of the mPFC and that social exclusion increases susceptibility to anxiety disorders through bias processing of fear discrimination in fear acquisition and generalization. By studying the role of social relationship in fear acquisition and generalization, our research provides new insights into the pathological mechanisms of anxiety disorder.

Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multi-level features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D.

Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images

BACKGROUND

Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs).

METHODS

This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category "3", Category "4A", Category "4B", Category "4C", and Category "5". Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage.

RESULTS

We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category "3", 0.940 on Category "4A", 0.734 on Category "4B", 0.922 on Category "4C", and 0.876 on Category "5".

CONCLUSION

The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign.

Responses of Transgenic Melatonin-Enriched Goats on LPS Stimulation and the Proteogenomic Profiles of Their PBMCs

The anti-inflammatory activity of melatonin (MT) has been well documented; however, little is known regarding endogenously occurring MT in this respect, especially for large animals. In the current study, we created a MT-enriched animal model (goats) overexpressing the MT synthetase gene Aanat. The responses of these animals to lipopolysaccharide (LPS) stimulation were systematically studied. It was found that LPS treatment exacerbated the inflammatory response in wild-type (WT) goats and increased their temperature to 40 °C. In addition, their granulocyte counts were also significantly elevated. In contrast, these symptoms were not observed in transgenic goats with LPS treatment. The rescue study with MT injection into WT goats who were treated with LPS confirmed that the protective effects in transgenic goats against LPS were attributed to a high level of endogenously produced MT. The proteomic analysis in the peripheral blood mononuclear cells (PBMCs) isolated from the transgenic animals uncovered several potential mechanisms. MT suppressed the lysosome formation as well as its function by downregulation of the lysosome-associated genes Lysosome-associated membrane protein 2 (LAMP2), Insulin-like growth factor 2 receptor (IGF2R), and Arylsulfatase B (ARSB). A high level of MT enhanced the antioxidant capacity of these cells to reduce the cell apoptosis induced by the LPS. In addition, the results also uncovered previously unknown information that showed that MT may have protective effects on some human diseases, including tuberculosis, bladder cancer, and rheumatoid arthritis, by downregulation of these disease-associated genes. All these observations warranted further investigations.

Comparative analysis of the blood transcriptomes between wolves and dogs

Dogs were domesticated by human and originated from wolves. Their evolutionary relationships have attracted much scientific interest due to their genetic affinity but different habitats. To identify the differences between dogs and wolves associated with domestication, we analysed the blood transcriptomes of wolves and dogs by RNA-Seq. We obtained a total of 30.87 Gb of raw reads from two dogs and three wolves using RNA-Seq technology. Comparisons of the wolf and dog transcriptomes revealed 524 genes differentially expressed genes between them. We found that some genes related to immune function (DCK, ICAM4, GAPDH and BSG) and aerobic capacity (HBA1, HBA2 and HBB) were more highly expressed in the wolf. Six differentially expressed genes related to the innate immune response (CCL23, TRIM10, DUSP10, RAB27A, CLEC5A and GCH1) were found in the wolf by a Gene Ontology enrichment analysis. Immune system development was also enriched only in the wolf group. The ALAS2, HMBS and FECH genes, shown to be enriched by the Kyoto Encyclopedia of Genes and Genomes analysis, were associated with the higher aerobic capacity and hypoxia endurance of the wolf. The results suggest that the wolf might have greater resistance to pathogens, hypoxia endurance and aerobic capacity than dogs do.

Automatic Processing of Emotional Words in the Absence of Awareness: The Critical Role of P2

It has been long debated to what extent emotional words can be processed in the absence of awareness. Behavioral studies have shown that the meaning of emotional words can be accessed even without any awareness. However, functional magnetic resonance imaging studies have revealed that emotional words that are unconsciously presented do not activate the brain regions involved in semantic or emotional processing. To clarify this point, we used continuous flash suppression (CFS) and event-related potential (ERP) techniques to distinguish between semantic and emotional processing. In CFS, we successively flashed some Mondrian-style images into one participant's eye steadily, which suppressed the images projected to the other eye. Negative, neutral, and scrambled words were presented to 16 healthy participants for 500 ms. Whenever the participants saw the stimuli—in both visible and invisible conditions—they pressed specific keyboard buttons. Behavioral data revealed that there was no difference in reaction time to negative words and to neutral words in the invisible condition, although negative words were processed faster than neutral words in the visible condition. The ERP results showed that negative words elicited a larger P2 amplitude in the invisible condition than in the visible condition. The P2 component was enhanced for the neutral words compared with the scrambled words in the visible condition; however, the scrambled words elicited larger P2 amplitudes than the neutral words in the invisible condition. These results suggest that the emotional processing of words is more sensitive than semantic processing in the conscious condition. Semantic processing was found to be attenuated in the absence of awareness. Our findings indicate that P2 plays an important role in the unconscious processing of emotional words, which highlights the fact that emotional processing may be automatic and prioritized compared with semantic processing in the absence of awareness.

Preliminary evaluation on the use of homing pigeons as a biomonitor in urban areas

This study evaluates the usefulness of homing pigeons as a biomonitor of polycyclic aromatic hydrocarbons (PAHs) in urban environments. The mean concentrations of total PAHs in liver and lung tissues were greater in pigeons from Beijing compared to pigeons from Chengdu, however, this difference was only statistically significant for PAH concentrations in liver tissue (P < 0.05). Similarly, the severity of anthracosis or pneumoconiosis in lung tissue and hepatitis in liver tissue was greater in pigeons from Beijing compared to pigeons from Chengdu. Low molecular weight PAHs dominated the contribution of individual PAHs in both tissues. Significant differences (P < 0.05) were observed for most low and moderate molecular weights PAHs in liver and for some low and high molecular weights PAHs in lung between the two cites. The profile patterns of individual PAHs were similar between lung tissue of pigeons and between local ambient airs in summer for both cities, whereas the profile patterns between liver tissue and pigeon food were less similar. These data suggest that homing pigeons may be of value as a biomonitor of environmental pollution in urban areas.

Exposure of traffic police to Polycyclic aromatic hydrocarbons in Beijing, China

Exposure of on-duty traffic police in Beijing to polycyclic aromatic hydrocarbons (PAHs) was investigated during the summer, 2004 using a personal sampling technique in measuring both particulate and gaseous phase PAHs. The results were then compared with those from two control sites away from the street. Exposure levels to gaseous and particulate PAHs for the traffic police were found to be 1525 +/- 759 ngm(-3) and 148 +/- 118 ngm(-3), respectively, representing 2-2.5 times higher levels than those at the control sites. The daily inhalation exposure of the police was estimated to be 277 ngkg(-1)d(-1). Most of the PAHs exposure came from the vapor phase, particularly NAP, FLO and PHE. Based on calculated PAH diagnostic ratios, the major source of PAHs exposure was from vehicle exhaust. The effects of temperature, humidity and atmospheric stability on exposure levels are also discussed.

An evaluation of sources of nitrogen in shallow groundwater using (15)N abundance technique

A (15)N abundance technique was employed to identify the source of NO(3)-N in groundwater under three commercial citrus production sites in central Florida. Water samples were collected from 0 to 300 and 300 to 600 cm depths in the surficial aquifer and analyzed for NO(3)-N and delta N-15 (delta (15)N). Groundwater samples were also collected in a residential area adjacent to one of the citrus groves and analyzed for NO(3)-N and delta (15)N. The delta (15)N values were in the range of (+)1 to (+)10% in both depths underneath the citrus groves. The range of delta (15)N measured in this study represents the range expected for groundwater that was impacted by NO(3)-N originated from mineralization of organic N from the soil as well as from the crop residue. There are occasional high delta (15)N values which are indicative of the effects of NH(3) volatilization losses of applied fertilizer N. The range of delta (15)N values for groundwater samples collected from the residential area adjacent to the citrus groves was very similar to that from the groundwater underneath the citrus groves. Thus, the source of NO(3)-N that impacted the groundwater under the citrus groves also impacted the groundwater in the adjacent residential area.

Coregistration of quantitative proton magnetic resonance spectroscopic imaging with neuropathological and neurophysiological analyses defines the extent of neuronal impairments in murine human immunodeficiency virus type-1 encephalitis

Relatively few immune-activated and virus-infected mononuclear phagocytes (MP; perivascular macrophages and microglia) may affect widespread neuronal dysfunction during human immunodeficiency virus type 1 (HIV-1)-associated dementia (HAD). Indeed, histopathological evidence of neuronal dropout often belies the extent of cognitive impairment. To define relationships between neuronal function and histopathology, proton magnetic resonance spectroscopic imaging (1H MRSI) and hippocampal long-term potentiation (LTP) were compared with neuronal and glial immunohistology in a murine model of HIV-1 encephalitis (HIVE). HIV-1(ADA)-infected human monocyte-derived macrophages (MDM) were stereotactically injected into the subcortex of severe combined immunodeficient (SCID) mice. Sham-operated and unmanipulated mice served as controls. Seven days after cell injection, brain histological analyses revealed a focal giant cell encephalitis, with reactive astrocytes, microgliosis, and neuronal dropout. Strikingly, significant reductions in N-acetyl aspartate concentration ([NAA]) and LTP levels in HIVE mice were in both injected and contralateral hemispheres and in brain subregions, including the hippocampus, where neuropathology was limited or absent. The data support the importance of 1H MRSI as a tool for assessing neuronal function for HAD. The data also demonstrate that a highly focal encephalitis can produce global deficits for neuronal function and metabolism.

Mutation analysis of the NMDAR2B (GRIN2B) gene in schizophrenia

NMDA receptor dysfunction may be involved in the pathophysiology of schizophrenia. Based on this hypothesis, we screened 48 Japanese patients with schizophrenia for mutations in the coding region of the NMDAR2B subunit gene (GRIN2B). An association study between the identified DNA sequence variants and schizophrenia was performed in 268 Japanese patients with schizophrenia and 337 Japanese control subjects. Eight single nucleotide polymorphisms were detected, all of which were synonymous. The association sample showed statistically significant excesses of homozygosity for the polymorphisms in the 3' region of the last exon in the patients with schizophrenia (P = 0.004) and higher frequency of the G allele of the 366C/G polymorphism (corrected P = 0.04) in the patients than in the controls. Although we did not detect NMDAR2B protein variants, our findings support the possibility that the GRIN2B gene or a locus in linkage disequilibrium with it may confer susceptibility to schizophrenia. Replication studies in independent samples are warranted.

[Determination of trace barium in biological samples by Zeeman graphite AAS with coated graphite tube]

Using a tungstate-coated graphite tube, trace barium in biological samples was determined by Zeeman graphite AAS. The sensitivity of Ba can be significantly improved. The precision and the lifetime of graphite tube have been improved by adding matrix modifier. The method is simple. The recovery and precision are satisfactory.

Attempted transmission of Babesia major by Boophilus microplus

Two experiments was carried out to determine if Babesia major could be transmitted by Boophilus microplus. In experiment 1, a Babesia-free batch of laboratory reared Bo. microplus larvae were applied to an intact calf infected by inoculation with a B. major stabilate. The calf showed a B. major parasitaemia while the larvae, nymphs and adult ticks were engorging. The engorged females were cultured and batches were incubated at one of the three following temperatures: 24, 28 or 32 degrees C. Approximately 10,000 larvae derived from each of the females were used to infest each of three splenectomized calves. In experiment 2, Babesia-free Bo. microplus larvae were applied to a splenectomized calf; the calf was injected with B. major stabilate and showed a B. major parasitaemia during the adult stage of tick development. The engorged females were incubated at room temperature and the resulting larvae (approximately 10,000) were used to infest a splenectomized calf. Examination of blood films for the presence of B. major from the four calves infested by the second generation larvae in the two experiments were negative.

A study of effects of WGA and ConA on RBC membrane receptors using a new ektacytometric method

With a new ektacytometry, we studied the relation between the microstructure of red blood cell (RBC) membrane and the rheological properties of RBCs in a shear flow field of low viscosity. The main contributions of this paper are as follows: 1. The hemorheological meanings of the orientation index (DI)or and the small deformation index (DI)d were explored. (DI)or is an overall rheological index depending on the deformability and morphology of RBCs. The better the physiological shape of RBCs is maintained, the greater the (DI)or is. (DI)d can be used to describe the lipid fluidity of RBC membrane. Such an explanation for the meaning of (DI)d has been forcefully supported by our experiments using electron spin resonance (ESR) and fluorescence polarization. 2. The influence of wheat germ agglutinin (WGA) of different concentrations on the lipid fluidity of membrane is different from that of concanavalin A (ConA). The lipid fluidity of membrane changes with WGA concentration treating RBCs and there is a maximum value for the membrane fluidity at a specific concentration of WGA. However, the deformability of membrane described by the integrate deformation index (IDI) monotonically decreased with the increase in WGA concentration treating RBCs. 3. It is concluded that the increase in the lipid fluidity of red cell membrane is not necessarily associated with the improvement of RBC deformability.

[Research respects and status of functional neuromuscular stimulation]

Functional Neuromuscular Stimulation (FNS) is a technique of restoring movements of paralyzed patients. This paper describes several respects of functional neuromuscular stimulation, such as stimulators, sensors, electrodes, muscle-skeletal models and control methods. Some novel control strategies are also discussed in this paper.

Experiments on the transmission of Babesia major and Babesia bigemina by Haemaphysalis punctata

Experiments on the transmission by Haemaphysalis punctata of three large Babesia strains were carried out. Three Babesia-free batches of laboratory reared H. punctata ticks were infected with two strains of Babesia major, B. major (Xingjiang strain), isolated with adult ticks of H punctata and B. major (Henan strain), isolated with H. longicornis) and a strain of Babesia bigemina by feeding them on the calves infected by inoculation of blood stabilates. H. punctata was shown to be capable of transmitting the B. major strains transovarially. The larvae, nymphs and adults developed from female ticks engorged on the calf infected with B. major (Xingjiang strain) transmitted the pathogen to splenectomised calves with prepatent periods of 15, 11 and 12 days, respectively. The calves infested with larvae and nymphs died of babesiosis with parasitemias of 400 and 710 per 1000 erythrocytes. The calf infested with adult ticks survived babesiosis, but the number of erythrocytes and the amount of haemoglobin were reduced greatly. H. punctata transmitted B. major (Henan strain) in the same way. The prepatent periods of the calves infested with larvae, nymph and adult ticks were 9, 10 and 12 days, respectively. Calves infested with larvae survived, but those infested with nymphal and adult ticks died of babesiosis with parasitemias of 410 and 100 per 1000 erythrocytes, respectively. H. punctata ticks did not transmit the B. bigemina strain to splenectomised calves. There were no clinical symptoms and no parasites were discovered in the blood films during a 2 month observation period after the calves were infested with larval, nymphal and adult ticks derived from female ticks engorged on calves inoculated with B. bigemina.

[Treatment of central retinal artery occlusion with thrombolysis via superselective ophthalmic artery catheterization]

OBJECTIVE

To investigate the therapeutic effect of thrombolysis via superselective ophthalmic artery catheterization for treatment of central retinal artery occlusion (CRAO).

METHOD

Urokinase was directly infused via ophthalmic artery (OA) or common carotid artery by catheterization after angiography into 4 eyes of 4 patients with CRAO, The times of onset ranging from 7 to 14 days.

RESULTS

The visual acuity was significantly improved in 2 eyes. No visual change was found in 2 eyes in which the beginning part of internal carotid artery (ICA) or OA was occluded. There was no complications.

CONCLUSIONS

Good results were obtained in the eyes in which thrombolysis was launched early in the course of the disease and urokinase was successfully infused into the OA. It seems that there is no effect for the eyes complicated with occlusion in ICA and OA.

Number, size, and regional distribution of motor neurons in the dorsolateral and retrodorsolateral nuclei as a function of sex and neonatal stimulation

Motor neurons were measured in the retrodorsolateral nucleus (RDLN) and the dorsolateral nucleus (DLN) of adult male and female rats that were reared with normal or reduced levels of maternal anogenital stimulation. In contrast with findings for the spinal nucleus of the bulbocavernosus, which is located in the same spinal segments, reduced stimulation had no effect on neuron number in either nucleus. However, several regional and sex differences were observed. Rostrally located neurons were larger in both the RDLN and the DLN; these location effects were greater in females. There was no sex difference in RDLN neuron size, but DLN neurons were larger in females, particularly in the rostral region. Females had significantly more cells in the RDLN, a nucleus previously considered nondimorphic, whereas males had more DLN neurons. Both regional and sex differences may reflect local differences in trophic factors from targets or afferents.

Maternal stimulation affects the number of motor neurons in a sexually dimorphic nucleus of the lumbar spinal cord

The role of maternal stimulation in the development of a lumbar motor nucleus (spinal nucleus of the bulboca vernosus, SNB) was investigated. The perineum, which has afferents to the lumbar region, is stimulated throughout early development by maternal licking, a behavior that is elicited by chemosignals secreted by the pups. In the present study, half of the dams were treated with intranasal zinc sulfate throughout the postpartum period, which led to a specific reduction in maternal stimulation of pup perineum by interfering with the reception of eliciting signals. Adult offspring of both sexes from anosmic dams had 11% fewer SNB motor neurons than normally stimulated controls, an effect which was most apparent in the rostral portion of the nucleus. There was no effect of treatment on neuron size. It was concluded that afferent input provided by species-typical maternal behavior contributes to the number of neurons that survive the neonatal period of normal cell death.