Phenyl Derivatives Modulate the Luminescent Properties and Stability of CzBTM-Type Radicals.

The distinctive electron structures of luminescent radicals offer considerable potential for a diverse array of applications. Up to now, the luminescent properties of radicals have been modulated through the introduction of electron-donating substituents, predominantly derivatives of carbazole and polyaromatic amines with more and more complicated structures and redshifted luminescent spectra. Herein, four kinds of (N-carbazolyl)bis(2,4,6-tirchlorophenyl)-methyl (CzBTM) radicals, Ph2CzBTM, Mes2CzBTM, Ph2PyIDBTM, and Mes2PyIDBTM, were synthesized and characterized by introducing simple phenyl and 2,4,6-trimethylphenyl groups to CzBTM and PyIDBTM. These radicals exhibit rare blueshifted emission spectra compared to their parent radicals. Furthermore, modifications to CzBTM significantly enhanced the photoluminescence quantum yields (PLQYs), with a highest PLQY of 21% for Mes2CzBTM among CzBTM-type radicals. Additionally, the molecular structures, photophysical properties of molecular orbitals, and stability of the four radicals were systematically investigated. This study provides a novel strategy for tuning the luminescent color of radicals to shorter wavelengths and improving thermostability.

Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI.

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks.

Epidemiologic changes of a longitudinal surveillance study spanning 51 years of scrub typhus in mainland China.

Scrub typhus may be one of the world's most prevalent, neglected and serious, but easily treatable, febrile diseases. It has become a significant potential threat to public health in China. In this study we used national disease surveillance data to analyze the incidence and spatial-temporal distribution of scrub typhus in mainland China during 1952-1989 and 2006-2018. Descriptive epidemiological methods and spatial-temporal epidemiological methods were used to investigate the epidemiological trends and identify high-risk regions of scrub typhus infection. Over the 51-year period, a total of 182,991 cases and 186 deaths were notified. The average annual incidence was 0.13 cases/100,000 population during 1952-1989. The incidence increased sharply from 0.09/100,000 population in 2006 to 1.93/100,000 population in 2018 and then exponentially increased after 2006. The incidence was significantly higher in females than males (χ2 = 426.32, P < 0.001). Farmers had a higher incidence of scrub typhus than non-farmers (χ2 = 684.58, P < 0.001). The majority of cases each year were reported between July and November with peak incidence occurring during October each year. The trend surface analysis showed that the incidence of scrub typhus increased gradually from north to south, and from east and west to the central area. The spatial autocorrelation analysis showed that a spatial positive correlation existed in the prevalence of scrub typhus on a national scale, which had the characteristic of aggregated distribution (I = 0.533, P < 0.05). LISA analysis showed hotspots (High-High) were primarily located in the southern and southwestern provinces of China with the geographical area expanding annually. These findings provide scientific evidence for the surveillance and control of scrub typhus which may contribute to targeted strategies and measures for the government.

Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation.

Multi-site brain magnetic resonance imaging (MRI) has been widely used in clinical and research domains, but usually is sensitive to non-biological variations caused by site effects (e.g., field strengths and scanning protocols). Several retrospective data harmonization methods have shown promising results in removing these non-biological variations at feature or whole-image level. Most existing image-level harmonization methods are implemented through generative adversarial networks, which are generally computationally expensive and generalize poorly on independent data. To this end, this paper proposes a disentangled latent energy-based style translation (DLEST) framework for image-level structural MRI harmonization. Specifically, DLEST disentangles site-invariant image generation and site-specific style translation via a latent autoencoder and an energy-based model. The autoencoder learns to encode images into low-dimensional latent space, and generates faithful images from latent codes. The energy-based model is placed in between the encoding and generation steps, facilitating style translation from a source domain to a target domain implicitly. This allows highly generalizable image generation and efficient style translation through the latent space. We train our model on 4,092 T1-weighted MRIs in 3 tasks: histogram comparison, acquisition site classification, and brain tissue segmentation. Qualitative and quantitative results demonstrate the superiority of our approach, which generally outperforms several state-of-the-art methods.

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI.

Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively. Unfortunately, existing research seldom takes advantage of such brain anatomy prior. To this end, this paper proposes a brain anatomy-guided representation (BAR) learning framework for assessing the clinical progression of cognitive impairment with T1-weighted MRIs. The BAR consists of a pretext model and a downstream model, with a shared brain anatomy-guided encoder for MRI feature extraction. The pretext model also contains a decoder for brain tissue segmentation, while the downstream model relies on a predictor for classification. We first train the pretext model through a brain tissue segmentation task on 9,544 auxiliary T1-weighted MRIs, yielding a generalizable encoder. The downstream model with the learned encoder is further fine-tuned on target MRIs for prediction tasks. We validate the proposed BAR on two CI-related studies with a total of 391 subjects with T1-weighted MRIs. Experimental results suggest that the BAR outperforms several state-of-the-art (SOTA) methods. The source code and pre-trained models are available at https://github.com/goodaycoder/BAR.