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Evolution of deep learning tooth segmentation from CT/CBCT images: a systematic review and meta-analysis
No full textBackground: Deep learning has been utilized to segment teeth from computed tomography (CT) or cone-beam CT (CBCT). However, the performance of deep learning is unknown due to multiple models and diverse evaluation metrics. This systematic review and meta-analysis aims to evaluate the evolution and performance of deep learning in tooth segmentation. Methods: We systematically searched PubMed, Web of Science, Scopus, IEEE Xplore, arXiv.org, and ACM for studies investigating deep learning in human tooth segmentation from CT/CBCT. Included studies were assessed using the Quality Assessment of Diagnostic Accuracy Study (QUADAS-2) tool. Data were extracted for meta-analyses by random-effects models. Results: A total of 30 studies were included in the systematic review, and 28 of them were included for meta-analyses. Various deep learning algorithms were categorized according to the backbone network, encompassing single-stage convolutional models, convolutional models with U-Net architecture, Transformer models, convolutional models with attention mechanisms, and combinations of multiple models. Convolutional models with U-Net architecture were the most commonly used deep learning algorithms. The integration of attention mechanism within convolutional models has become a new topic. 29 evaluation metrics were identified, with Dice Similarity Coefficient (DSC) being the most popular. The pooled results were 0.93 [0.93, 0.93] for DSC, 0.86 [0.85, 0.87] for Intersection over Union (IoU), 0.22 [0.19, 0.24] for Average Symmetric Surface Distance (ASSD), 0.92 [0.90, 0.94] for sensitivity, 0.71 [0.26, 1.17] for 95% Hausdorff distance, and 0.96 [0.93, 0.98] for precision. No significant difference was observed in the segmentation of single-rooted or multi-rooted teeth. No obvious correlation between sample size and segmentation performance was observed. Conclusions: Multiple deep learning algorithms have been successfully applied to tooth segmentation from CT/CBCT and their evolution has been well summarized and categorized according to their backbone structures. In future, studies are needed with standardized protocols and open labelled datasets
Association of Perceived Acoustic Environment Exposure and Nonrestorative Sleep with Cognitive Functioning Among Chinese Adults: A Cross-Sectional Survey
No full textDespite the growing emphasis on cognitive health, evidence regarding individual and environmental factors influencing cognitive functioning remains limited. We aimed to examine the association of personal and environmental factors, specifically perceived acoustic environment exposure and nonrestorative sleep, with cognitive functioning among Chinese adults. Between July and August 2022, we recruited 970 Chinese adults from all districts in Hong Kong for our cross-sectional study. Univariable and structured multiphase linear regression analyses were conducted to identify the contributory factors. Among 970 Chinese adults, the structured multiphase linear regression model revealed that being in their 30s (β = 0.160; 95% CI: 0.004, 0.315) and 40s (β = 0.186; 95% CI: 0.029, 0.343), being female (β = 0.095; 95% CI: 0.018, 0.173), being former smokers, and having medical consultations, medical histories (eczema, hearing problems, and insomnia), perceived acoustic environment exposure (traffic and mechanical sounds (β = 0.011; 95% CI: 0.008, 0.013), nature and music sounds (β = 0.004; 95% CI: 0.001, 0.007), and human sounds (β = 0.002; 95% CI: 0.0004, 0.005)), and psychological symptoms (perceived stress, depressive, and anxiety symptoms) were associated with worse cognitive functioning. Moreover, more nonrestorative sleep (β = −0.015; 95% CI: −0.022, −0.007) was also associated with worse cognitive functioning. This study revealed that increased perceived acoustic environment exposure and a higher degree of nonrestorative sleep were associated with poorer cognitive functioning among Chinese adults. This underscores the need for public health strategies and policies aimed at fostering a healthy acoustic environment and promoting sleep hygiene education in the community
Bismuth drug eradicates multi-drug resistant Burkholderia cepacia complex via aerobic respiration
No full textBurkholderia cepacia complex (Bcc) is a group of Gram-negative opportunistic pathogens highly responsible for chronic pulmonary infection in cystic fibrosis (CF). Current therapies involving double or triple antibiotic combinations can rarely eradicate the pathogen in chronically infected patients owing to its intrinsic resistance to a variety of antibiotics. Herein, we show that a bismuth drug (and related compounds) could inhibit the growth of clinically antibiotic-resistant Bcc strains, with MIC (ca. 25 μg mL−1) comparable to that for Helicobacter pylori, and the combination of a bismuth drug and antibiotics also demonstrated excellent activity against biofilm and persisters of Bcc. Importantly, the in vitro antimicrobial activity of a bismuth drug could be well translated into in vivo evidenced by about 50% survival rates in the Galleria mellonella infection model. Transcriptomics analysis shows the dynamic responses of Bcc to bismuth treatment. Using a homemade metalloproteomic approach, we could identify 26 BiIII-binding proteins (15 cytosolic proteins and 11 membrane proteins). Further mechanistic studies reveal that bismuth drugs initially target the TCA cycle through the binding and inactivation of a series of enzymes including malate dehydrogenase (MDH), malate synthase (AceB), and succinyl coenzyme A synthetase (SCS), then interfere oxidative phosphorylation through binding to terminal oxidases, i.e., CyoC and CydA, to disrupt electron transport chain, eventually, disrupt protein translation and ribosome via binding and down-regulation of key proteins. Our studies highlight the great potential of bismuth drugs and/or compounds to treat multidrug-resistant Bcc infections.published_or_final_versio
Persistent breather and dynamical symmetry in a unitary Fermi gas
No full textSO(2,1) dynamical symmetry makes a remarkable prediction that the breathing oscillation of a scale-invariant quantum gas in an isotropic harmonic trap is isentropic and can persist indefinitely. In two dimensions, this symmetry is broken due to quantum anomaly in the strongly interacting range, and consequently the lifetime of the breathing mode becomes finite. The persistent breather in a strongly interacting system has so far not been realized. Here, we experimentally achieve the long-lived breathing mode in a three-dimensional unitary Fermi gas, which is protected by the SO(2,1) symmetry. The nearly perfect SO(2,1) symmetry is realized by loading the ultracold Fermi gas in an isotropic trap and tuning the interatomic interaction to resonance. The breathing mode oscillates at twice the trapping frequency even for large excitation amplitudes. The ratio of damping rate to oscillation frequency is as small as 0.002, providing an interacting persistent breather. The oscillation frequency and damping rate are nearly constant for different atomic densities and temperatures, demonstrating the robustness of the SO(2,1) symmetry in three dimensions. The factors that lead to the residual damping have also been clarified. This work opens the way to study many-body nonequilibrium dynamics related to the dynamical symmetry
Relationships Between Spontaneous Alpha Oscillation and Brain Response Amid the Complexity of Brain Adaptation and Spectral Signal Composition
No full textThe brain operates as a complex dynamic system, continuously generating both structured spontaneous activity and stimulus-evoked responses. Because these activities originate from the same neural architecture, they are hypothesized to be interconnected. However, research has yet to establish a definitive relationship between spontaneous and response patterns, as findings have been mixed and inconclusive. We argue that this ambiguity stems from significant theoretical and methodological challenges in characterizing the relevant variables amidst the brain's complexity. In this study, we investigated the cross-individual correlation between spontaneous Alpha amplitude and the magnitude of brain responses to simple stimuli. Our analysis revealed a robust correlation, but only after accounting for two key confounding factors inherent to the brain's complex dynamics: (1) strong adaptation effects across repeated stimulus exposures and (2) the mixture of aperiodic and band-specific dynamic activity signals. These results demonstrate a close association between the strength of Alpha oscillations—a primary brain rhythm implicated in various functions—and the magnitude of stimuli-evoked responses. Specifically, individuals with higher resting-state Alpha amplitudes exhibit stronger brain responses. This discovery not only highlights methodological challenges in relating spontaneous and evoked brain activity, but also demonstrates that they can be addressed. Our findings have significant implications for research aimed at understanding the mechanistic models and functional roles of the brain's dynamic system, shedding light on future investigations into the interplay between intrinsic and evoked neural dynamics
Gender differences in resume language and gender gaps in salary expectations
No full textHow men and women present themselves in their resumes may affect their opportunity in job seeking. To investigate gender differences in resume writing and how they are associated with gender gaps in the labour market, we analysed 6.9 million resumes of Chinese job applicants in this study. Results reveal substantial gender resume differences, where women and men show distinct patterns in both simple language features and high-level semantic structures in the word embedding space of resumes. In particular, women tend to use shorter resumes, longer sentences and a more diverse set of unique words. Neural network models trained on resumes can predict gender with 80% accuracy, and the accuracy decreases with education levels and text standardization requirements. Moreover, while better language skills are associated with higher salary expectations, this positive relationship is magnified for men but weakened for women in women-dominated occupations. This study presents a new venue for the understanding of gender differences and provides empirical findings on how men and women are different in self-portraying and job seeking.</p
Statistical learning of artificial orthographic regularity arises from coordinated activity across distinct brain regions
No full textThe human brain possesses the ability to automatically extract statistical regularities from environmental inputs, including visual-graphic symbols and printed units. However, the specific brain regions underlying the statistical learning of these visual-graphic symbols or artificial orthography remain unclear. This study utilized functional magnetic resonance imaging (fMRI) with an artificial orthography learning paradigm to measure brain activities associated with the statistical learning of radical positional regularities embedded in pseudocharacters containing high (100%), moderate (80%), and low (60%) levels of consistency, along with a series of random abstract figures. Thirty adults passively viewed a continuous stream of these pseudocharacters. fMRI data revealed that the left occipital area and the visual word form area (VWFA) exhibited greater responses at the low consistency level than at the high and moderate levels, suggesting implicit statistical learning of positional regularities. Functional connectivity analysis further revealed significant correlations between the occipital lobe, the VWFA, and other brain regions, such as the middle temporal gyrus (MTG), the superior occipital gyrus (SOG), and the cerebellum. Moreover, neural activity showed a tendency to correlate with behavioral recognition performance. These findings demonstrate that the incidental acquisition of statistical regularities in artificial orthography arises from the coordinated activation of multiple distinct neural circuits
Degradation of IDH1 via Chaperone-mediated autophagy inhibits cell cycle progression in glioblastoma (Poster Presentation)
No full textLearning‐Based Rapid Phase‐Aberration Correction and Control for Robot‐Assisted MRI‐Guided Low‐/High‐Intensity Focused Ultrasound Treatments
Get PDFMagnetic resonance imaging (MRI)-guided focused ultrasound (MRg-FUS) is an effective and noninvasive procedure for treating diseases such as neurological disorders. Phase adjustment on ultrasound transducers can only achieve a limited focal-spot steering range. When treating large abdominopelvic targets, mechanical adjustment on the transducers' position and orientation is the prerequisite for enlarging the steering range. Therefore, we previously designed an MRI-guided robot to manipulate the transducers to offer sufficient focal-spot movement range. This could provide more modulation solutions to constructive ultrasound interference. However, full-wave ultrasound propagation inside a patient's heterogeneous abdominal media is complex and nonlinear, posing significant challenges in ultrasound modulation and beam motion control. Here, we propose a novel learning-based phase-aberration correction and model-free control framework for robot-assisted MRg-FUS treatments. The correction policy guarantees rapid aberration compensation within 5.0 ms. Submillimeter refocusing accuracy is achieved in both the liver (0.32 mm) and pancreas (0.51 mm), meeting clinical requirements for focal targeting. Our controller can accommodate nonlinear phase actuation with fast convergence (published_or_final_versio
