Deep Closing: Enhancing Topological Connectivity in Medical Tubular Segmentation.

Accurately segmenting tubular structures, such as blood vessels or nerves, holds significant clinical implications across various medical applications. However, existing methods often exhibit limitations in achieving satisfactory topological performance, particularly in terms of preserving connectivity. To address this challenge, we propose a novel deep-learning approach, termed Deep Closing, inspired by the well-established classic closing operation. Deep Closing first leverages an AutoEncoder trained in the Masked Image Modeling (MIM) paradigm, enhanced with digital topology knowledge, to effectively learn the inherent shape prior of tubular structures and indicate potential disconnected regions. Subsequently, a Simple Components Erosion module is employed to generate topology-focused outcomes, which refines the preceding segmentation results, ensuring all the generated regions are topologically significant. To evaluate the efficacy of Deep Closing, we conduct comprehensive experiments on 4 datasets: DRIVE, CHASE DB1, DCA1, and CREMI. The results demonstrate that our approach yields considerable improvements in topological performance compared with existing methods. Furthermore, Deep Closing exhibits the ability to generalize and transfer knowledge from external datasets, showcasing its robustness and adaptability. The code for this paper has been available at: https://github.com/5k5000/DeepClosing.

Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation.

Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bladder cancer. Deep Convolution Neural Networks (DCNNs) have been successfully used for bladder tumor segmentation, but the segmentation based on DCNN is data-hungry for model training and ignores clinical knowledge. From the clinical view, bladder tumors originate from the mucosal surface of bladder and must rely on the bladder wall to survive and grow. This clinical knowledge of tumor location is helpful to improve the bladder tumor segmentation. To achieve this, we propose a novel bladder tumor segmentation method, which incorporates the clinical logic rules of bladder tumor and bladder wall into DCNNs to harness the tumor segmentation. Clinical logical rules provide a semantic and human-readable knowledge representation and are easy for knowledge acquisition from clinicians. In addition, incorporating logical rules of clinical knowledge helps to reduce the data dependency of the segmentation network, and enables precise segmentation results even with limited number of annotated images. Experiments on bladder MR images collected from the collaborating hospital validate the effectiveness of the proposed bladder tumor segmentation method.

EtOS2K as a C1 Source: Solvent- and Temperature-Controlled Selective Synthesis of Quinoline-2-thione and Quinoline-2-one Derivatives.

Herein, we disclosed a highly chemoselective synthesis of quinoline-2-one and quinoline-2-thione derivatives using EtOS2K as the C1 source. Quinoline-2-one derivatives were synthesized selectively with NaCl as a catalyst in the solvent DMSO/H2O, while quinoline-2-thione derivatives were produced without the need for any catalyst in an environmentally friendly solvent EtOH/H2O. The reaction conditions were mild and had good functional group tolerance.

Total Synthesis of (+)-Isolysergol.

The enantioselective synthesis of (+)-isolysergol was completed in 18 steps, and an overall yield of 11% was obtained from (2R)-(+)-phenyloxirane as a chiral pool. Key features of the synthesis include a stereoselective intramolecular 1,3-dipolar addition of nitrone with terminal olefin and a Cope elimination to furnish the D ring. A rhodium-catalyzed intramolecular [3 + 2] annulation of a benzene ring with α-imino carbenoid was designed to afford the 3,4-fused indole scaffold at the late stage of the synthesis.

National Experiences and Trust in China's National Government Among Hong Kong Chinese Youth.

Trust in the national central government is particularly imperative for promotion in outlying regions, such as by enhancing their residents' national cultural and territorial experiences. The contributions of such experiences, albeit grounded on contact and cultural theories, require empirical investigation. Such investigation engaged a survey of 2277 Chinese youths aged 18-29 years in Hong Kong, an outlying region returning to China's sovereignty. Results evidenced that experience with Mainland Chinese territory in the Greater Bay Area during junior secondary schooling predicted recent trust in China's national government, particularly in those born in Hong Kong. Meanwhile, the cultural experience of Chinese enculturation during schooling predicted the trust conditionally with migrant status or the territorial experience. These results imply the value of enhancing national cultural and territorial experiences to promote youth's trust in the central government.

Typicality-Aware Adaptive Similarity Matrix for Unsupervised Learning.

Graph-based clustering approaches, especially the family of spectral clustering, have been widely used in machine learning areas. The alternatives usually engage a similarity matrix that is constructed in advance or learned from a probabilistic perspective. However, unreasonable similarity matrix construction inevitably leads to performance degradation, and the sum-to-one probability constraints may make the approaches sensitive to noisy scenarios. To address these issues, the notion of typicality-aware adaptive similarity matrix learning is presented in this study. The typicality (possibility) rather than the probability of each sample being a neighbor of other samples is measured and adaptively learned. By introducing a robust balance term, the similarity between any pairs of samples is only related to the distance between them, yet it is not affected by other samples. Therefore, the impact caused by the noisy data or outliers can be alleviated, and meanwhile, the neighborhood structures can be well captured according to the joint distance between samples and their spectral embeddings. Moreover, the generated similarity matrix has block diagonal properties that are beneficial to correct clustering. Interestingly, the results optimized by the typicality-aware adaptive similarity matrix learning share the common essence with the Gaussian kernel function, and the latter can be directly derived from the former. Extensive experiments on synthetic and well-known benchmark datasets demonstrate the superiority of the proposed idea when comparing with some state-of-the-art methods.

The effect of maternal empathy on infants' attachment security: Moderation by maternal emotion regulation and infant temperament.

This study examined the relationship between maternal empathy and infants' attachment security and tested the moderating effects of maternal emotion regulation and infant negative emotional temperament. Participants were sampled from 215 families whose infants were 6 months old (T1) and from 170 families of the same cohort whose infants were 14 months old (T2). At T1, mothers were measured for their empathy (empathic concern and perspective taking), emotion regulation (reappraisal and suppression), and infant negative emotional temperament (sadness, falling reactivity, fear, and distress to limitations). At T2, mothers were again measured for their emotion regulation, and infant attachment was measured using the Strange Situation Procedure. Maternal empathic concern (EC) and perspective taking (PT) were found to be positively related to infant attachment security, while maternal reappraisal and infant temperamental sadness moderated the relationship between maternal PT and infant attachment security. No significant moderation effects were found between maternal EC and infant attachment security. Simple slope results showed that for infants with higher maternal reappraisal or infant sadness, maternal PT could positively predict infant attachment security. These findings highlight the importance of how the individual characteristics of mothers and infants may interact with maternal empathy during the critical period of infant attachment formation.

PENet: Prior evidence deep neural network for bladder cancer staging.

Bladder cancer is a heterogeneous, complicated, and widespread illness with high rates of morbidity, death, and expense if not treated adequately. The accurate and exact stage of bladder cancer is fundamental for treatment choices and prognostic forecasts, as indicated by convincing evidence from randomized trials. The extraordinary capability of Deep Convolutional Neural Networks (DCNNs) to extract features is one of the primary advantages offered by these types of networks. DCNNs work well in numerous real clinical medical applications as it demands costly large-scale data annotation. However, a lack of background information hinders its effectiveness and interpretability. Clinicians identify the stage of a tumor by evaluating whether the tumor is muscle-invasive, as shown in images by the tumor's infiltration of the bladder wall. Incorporating this clinical knowledge in DCNN has the ability to enhance the performance of bladder cancer staging and bring the prediction into accordance with medical principles. Therefore, we introduce PENet, an innovative prior evidence deep neural network, for classifying MR images of bladder cancer staging in line with clinical knowledge. To do this, first, the degree to which the tumor has penetrated the bladder wall is measured to get prior distribution parameters of class probability called prior evidence. Second, we formulate the posterior distribution of class probability according to Bayesian Theorem. Last, we modify the loss function based on posterior distribution of class probability which parameters include both prior evidence and prediction evidence in the learning procedure. Our investigation reveals that the prediction error and the variance of PENet may be reduced by giving the network prior evidence that is consistent with the ground truth. Using MR image datasets, experiments show that PENet performs better than image-based DCNN algorithms for bladder cancer staging.

The effects of social anxiety on recognition memory for social threat words: An ERP study.

In this study, the effect of social anxiety on item recognition memory was examined by adopting a study-test paradigm. Participants with high and low social anxiety (31 HSA vs. 30 LSA) memorized neutral target and threat target (NT vs. TT) words while threat distracters were simultaneously presented. The behavioral results did not exhibit group differences in recognition performance. The event-related potentials (ERP) results showed that the HSA and LSA participants all did not exhibit significant old/new effects for neutral targets, while only the LSA participants exhibited significant old/new effects for threat targets. For the distracters, the HSA participants did not exhibit evident old/new effects under the NT and TT conditions; while LSA participants showed a reversed LPC old/new effect for the threat distracters under the NT condition. The old/new effects for threat targets were impaired in HSA participants but presented in LSA participants. These findings suggest that social anxiety modulates the effect of recognition memory for social threat words.

Expectations of immediate and delayed reward differentially affect cognitive task performance.

The current study used a modified Monetary Incentive Delay task to examine the neural mechanisms underlying anticipating and receiving an immediate or delayed reward and examined the influence of pursuing these rewards on cognitive task performance. A pre-cue indicating the potential of gaining a monetary reward (immediate-, delayed-, vs. no-reward) was followed by a target stimulus requiring a fast and accurate response. Then, response-contingent feedback was presented indicating whether or not the participant would receive the corresponding reward. Linear mixed-effect models revealed the fastest behavioural responses and the strongest neural activity, as reflected in event-related-potentials and event-related-spectral-perturbation responses, for immediate reward, followed by delayed reward, with the slowest behavioural responses and the weakest neural activities observed in the no-reward condition. Expectations related to the cue-P3 component and the cue-delta activities predicted behavioural performance, especially in the immediate reward condition. Moreover, exploratory analyses revealed that depression moderated the relationship between target-locked neural activity and behavioural performance in the delayed reward condition, with lower neural activity being related to worse behavioural performance amongst participants scoring high on depression. These results indicate that differential value representations formed through delay discounting directly affect neural responses in reward processing and directly influence the effort invested in the current task, which is reflected by behavioural responses and is in agreement with the expected value of control theory.

Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory.

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.

Transperineal Parallel Biopsy of the Prostate: A New Approach of Tissue Sampling for Precision Medicine.

PURPOSE: Through an observational study to present a new approach for obtaining high-quality samples for the targeted therapy of prostate cancer. PATIENTS AND METHODS: Parallel biopsy, which was defined as collecting the tissue from the same site by two biopsies, was performed on patients with elevated PSA. Each tissue was stained by ink to identify the pathological characteristics, including Gleason score and tumor tissue ratio. Kendall tau-b test and intraclass correlation coefficient test were used to compare the consistency between each paired sample. Then, based on the pathology of the biopsies, high-quality tissues would be selected for sequencing, and PyClone model was used to track the clonal evolution. RESULTS: In total, 252 pairs of biopsies were collected. The consistency of Gleason score between each paired biopsy is 0.777 (p<0.01), and the consistency of tumor tissue ratio is 0.853 (p<0.01). With the application of parallel biopsy, on average five nonsynonymous mutations could be identified in patients with castration-resistant prostate cancer. Six out of eight had at least one biology-relevant alteration in patients, guiding further treatment. Meanwhile, clonal evolution was constructed to investigate the progress of tumor. CONCLUSION: Parallel biopsy is a reliable approach to collect high-quality tissue and shows potential application in precision medicine.

Cultural Differences in the Hedonic Rewards of Recalling Kindness: Priming Cultural Identity with Language.

Recent theory suggests that members of interdependent (collectivist) cultures prioritize in-group happiness, whereas members of independent (individualist) cultures prioritize personal happiness (Uchida et al. Journal of Happiness Studies, 5(3), 223-239 Uchida et al., 2004). Thus, the well-being of friends and family may contribute more to the emotional experience of individuals with collectivist rather than individualist identities. We tested this hypothesis by asking participants to recall a kind act they had done to benefit either close others (e.g., family members) or distant others (e.g., strangers). Study 1 primed collectivist and individualist cultural identities by asking bicultural undergraduates (N = 357) from Hong Kong to recall kindnesses towards close versus distant others in both English and Chinese, while Study 2 compared university students in the USA (n = 106) and Hong Kong (n = 93). In Study 1, after being primed with the Chinese language (but not after being primed with English), participants reported significantly improved affect valence after recalling kind acts towards friends and family than after recalling kind acts towards strangers. Extending this result, in Study 2, respondents from Hong Kong (but not the USA) who recalled kind acts towards friends and family showed higher positive affect than those who recalled kind acts towards strangers. These findings suggest that people with collectivist cultural identities may have relatively more positive and less negative emotional experiences when they focus on prosocial interactions with close rather than weak ties. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-020-00029-3.

The role of beauty as currency belief in acceptance of cosmetic surgery and career aspirations among Chinese young women.

The current study tested the "beauty as currency" hypothesis in the framework of Objectification theory with a sample of Chinese young women. Four hundred and four college women completed a pencil-and-paper questionnaire. We hypothesized that beauty as currency would be associated with acceptance of cosmetic surgery and career aspirations through the serial meditation of self-objectification and body surveillance. The results indicated that self-objectification and body surveillance mediated the relation between women's belief in beauty as currency and acceptance of cosmetic surgery. Body surveillance mediated the relationship between beauty as currency and career aspirations. These findings provide further evidence for the "beauty as currency" hypothesis, suggesting the feminine beauty ideology may lead to women's higher acceptance of cosmetic surgery and lower career aspirations via the self-objectifying process. Our study provides some implications for understanding the effect of women's ideologies on gender system change.

CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor.

RATIONALE AND OBJECTIVES: Tumor grading of nonfunctional pancreatic neuroendocrine tumors (NF-pNETs) determines the choice of clinical treatment and management. The pathological grade of pancreatic neuroendocrine tumors is usually assessed on postoperative specimens. The goal of our study is to establish a tumor grade (G) prediction model for preoperative G1/2 NF-pNETs using radiomics for multislice spiral CT image analysis. MATERIALS AND METHODS: This retrospective study included a primary cohort of 59 patients and an independent validation cohort of 40 consecutive patients; their multislice spiral CT images were collected from October 2012 to October 2016 and October 2016 to June 2018, respectively. All 99 patients were diagnosed with clinicopathologically confirmed NF-pNETs. Most significant radiomic features were selected using the minimum redundancy and maximum relevance algorithm. Support vector machine classifier with a radial basis function-based predictive model was subsequently developed for clinical use. RESULTS: A total of 585 radiomics features were extracted from every phase for each patient. Six of these radiomics features were identified as most discriminant features for G1 and G2 tumors and used to construct the tumor grade prediction model. The prediction model resulted in the area under the curve values of 0.968 (95% CI: 0.900-0.991) and 0.876 (95% CI: 0.700-0.963) for the training cohort and validation cohort, respectively. Sensitivity and specificity were 96.4% and 83.9%, and 90.9% and 88.9% for the training and validation cohorts, respectively. The decision curves indicated that if the threshold probability is above 0.1, using the rad-score in the current study on G1/2 NF-pNETs is more beneficial than the treat-all-patients scheme or the treat-none scheme. CONCLUSION: Radiomics developed with a combination of nonenhanced and portal venous phases can achieve favorable predictive accuracy for histological grade for G1/G2 NF-pNETs.

Deep pancreas segmentation with uncertain regions of shadowed sets.

Pancreas segmentation is a challenging task in medical image analysis especially for the patients with pancreatic cancer. First, the images often have poor contrast and blurred boundaries. Second, there exist large variations in gray scale, texture, location, shape and size among pancreas images. It becomes even worse with cases of pancreatic cancer. Besides, as an inevitable phenomenon, some of the slices have disconnected topology in pancreas part. All these problems lead to high segmentation uncertainties and make the results inaccurate. Existing pancreas segmentation methods rarely achieve sufficiently accurate and robust results especially for cancer cases. To tackle these problems, we propose a 2D deep learning-based method which can involve uncertainties in the process of segmentation iteratively. The proposed method describes the uncertain regions of pancreatic MRI images based on shadowed sets theory. The results are further corrected through increasing the weights of uncertain regions in iterative training. We evaluate our approach on a challenging pancreatic cancer MRI images dataset collected from the Changhai Hospital, and also validate our approach on the NIH pancreas segmentation dataset. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods in terms of the Dice similarity coefficient of 73.88% on cancer MRI dataset and 84.37% on NIH dataset respectively.

Humour processing deficits in individuals with social anhedonia.

Humour processing comprises the humour comprehension and the humour appreciation phases. Patients with schizophrenia have impaired humour processing. However, it is unclear whether such deficits affect subclinical populations such as individuals with social anhedonia. Our study recruited forty-eight individuals with high levels of social anhedonia (HSA, screened by the Revised Chapman Social Anhedonia Scale) and 50 individuals with low levels of social anhedonia (LSA). Participants completed behavioural tasks which tapped into humour comprehension and appreciation, and a set of questionnaires assessing their sense of humour, humour styles and subjective experiential pleasure. Using signal detection theory analysis, the d' and ß values were generated to measure the detection of humour signal in the comprehension phase and the inner criteria of the humour appreciation respectively. The results showed that the HSA and LSA groups did not differ in humour signal detection (d') but the HSA group had significantly higher inner criteria of humour appreciation (ß) than the LSA group. The ß value was correlated with experiential anticipatory pleasure in all participants. The HSA group had significantly lower within-group coherence than the LSA group when processing humour. Our findings suggested that individuals with social anhedonia have impaired humour processing.

Motivation deficits in individuals with social anhedonia.

Patients with schizophrenia have been reported to exhibit anhedonia, a reduced hedonic capacity and deficits in motivation for reward pursuit. However, it is unclear whether these deficits also exist in at-risk individuals prone to psychosis or not. The present study compared 26 individuals with social anhedonia and 28 healthy controls using a grip Effort-based Pleasure Experience Task (E-PET). The findings showed that individuals with social anhedonia did not increase their hard task choices with the elevation of reward magnitude and probability while healthy controls did. Higher reward probability and magnitude did not lead to more anticipatory pleasure in individuals with social anhedonia. The mean anticipatory pleasure experience ratings in individuals with social anhedonia were significantly lower than controls. Our results suggest that individuals with social anhedonia already exhibit motivational deficits during reward pursuit.

Functional Region Annotation of Liver CT Image Based on Vascular Tree.

Anatomical analysis of liver region is critical in diagnosis and treatment of liver diseases. The reports of liver region annotation are helpful for doctors to precisely evaluate liver system. One of the challenging issues is to annotate the functional regions of liver through analyzing Computed Tomography (CT) images. In this paper, we propose a vessel-tree-based liver annotation method for CT images. The first step of the proposed annotation method is to extract the liver region including vessels and tumors from the CT scans. And then a 3-dimensional thinning algorithm is applied to obtain the spatial skeleton and geometric structure of liver vessels. With the vessel skeleton, the topology of portal veins is further formulated by a directed acyclic graph with geometrical attributes. Finally, based on the topological graph, a hierarchical vascular tree is constructed to divide the liver into eight segments according to Couinaud classification theory and thereby annotate the functional regions. Abundant experimental results demonstrate that the proposed method is effective for precise liver annotation and helpful to support liver disease diagnosis.

To Be or Not To Be Humorous? Cross Cultural Perspectives on Humor.

Humor seems to manifest differently in Western and Eastern cultures, although little is known about how culture shapes humor perceptions. The authors suggest that Westerners regard humor as a common and positive disposition; the Chinese regard humor as a special disposition particular to humorists, with controversial aspects. In Study 1, Hong Kong participants primed with Western culture evaluate humor more positively than they do when primed with Chinese culture. In Study 2a, Canadians evaluate humor as being more important in comparison with Chinese participants. In Study 2b, Canadians expect ordinary people to possess humor, while Chinese expect specialized comedians to be humorous. The implications and limitations are discussed.