Different effects of continuous and pulsed Benzo[a]pyrene exposure on metabolism and antioxidant defense of large yellow croaker: Depend on exposure duration.

This study aims to compare differential effects of continuous and pulsed BaP exposures on metabolism and antioxidant defense in the liver of large yellow croaker. Fish were subjected to BaP for 4 days and 36 days in three exposure regimes with the same time-averaged concentration of BaP: 4 µg/L BaP continuously, 8 µg/L BaP for 24 h every other day or 16 µg/L BaP for 24 h every 4 days. Our results showed that compared to pulsed BaP exposures, continuous BaP exposure reduced BaP metabolism (CYP1A, CYP3A and AHR transcriptional expressions, GSH content, GSH/GSSG ratio, EROD and GST activities) and antioxidant defense (T-SOD activity) on day 4, resulting to the increases in MDA and PC contents, indicating that continuous BaP exposure induced more severe oxidative damage during the early stage of exposure. But continuous BaP exposure reduced MDA and PC contents by improving BaP metabolism and antioxidant defense during the late stage of exposure. CYP1B transcriptional expression and CAT activity were unsuitable biomarkers of both continuous and pulsed BaP exposures. In conclusion, our results demonstrated differential effects of continuous and pulsed exposures on BaP metabolism and antioxidant responses, which were depend on exposure duration.

A Demodulation Algorithm for Periodically In-Plane Vibrating MEMS Based on a Stroboscopic Micro-Visual System.

A high-frequency short-pulsed stroboscopic micro-visual system was employed to capture the transient image sequences of a periodically in-plane working micro-electro-mechanical system (MEMS) devices. To demodulate the motion parameters of the devices from the images, we developed the feature point matching (FPM) algorithm based on Speeded-Up Robust Features (SURF). A MEMS gyroscope, vibrating at a frequency of 8.189 kHz, was used as a testing sample to evaluate the performance of the proposed algorithm. Within the same processing time, the SURF-based FPM method demodulated the velocity of the in-plane motion with a precision of 10−5 pixels of the image, which was two orders of magnitude higher than the template-matching and frame-difference algorithms.

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.

Improved Molecular Packing of Self-Assembled Monolayer Charge Injectors for Perovskite Light-Emitting Diodes.

Self-assembled monolayers (SAMs) have shown great potential as hole injection materials for perovskite light-emitting diodes due to their low parasitic absorption and ability to adjust energy level alignment. However, the head and anchoring groups on SAM molecules with significant differences in polarity can lead to the formation of micelles in the commonly used alcoholic processing solvent, inhibiting the formation of an intact SAM. In this work, the introduction of methyl groups on carbazole in the phosphonic-acid-based SAM materials is found to facilitate energy level alignment and promote the formation of compact SAMs. The alternative molecular structure also enhances the solvent resistance of poly(9-vinylcarbazole), suppressing interfacial defect densities and nonradiative recombination processes in the emissive perovskites. PeLEDs based on the methyl-containing SAMs exhibit ∼30% enhancement in efficiency. These findings contribute to a better understanding of the design of SAM materials for PeLED applications.

Differential effects of oxytetracycline on detoxification and antioxidant defense in the hepatopancreas and intestine of Chinese mitten crab under cadmium stress.

This study aims to evaluate the effects of oxytetracycline (OTC) on detoxification and oxidative defense in the hepatopancreas and intestine of Chinese mitten crab (Eriocheir sinensis) under cadmium (Cd) stress. The crab was exposed to 0.6 µM Cd, 0.6 µM OTC, and 0.6 µM Cd plus 0.6 µM OTC for 42 days. Our results showed that in the intestine, OTC alone enhanced protein carboxylation (PC) and malondialdehyde (MDA) contents, which was associated with the increased OTC accumulation. Compared to Cd alone, Cd plus OTC increased Cd and OTC contents, and reduced detoxification (i.e., glutathione (GSH) content, gene expressions of cytochrome P450 (CYP) isoforms, 7-ethoxyresorufin O-deethylase (EROD) activity, mRNA levels and activities of glutathione peroxidase (GPx), glutathione reductase (GR) and glutathione-S-transferase (GST)), and antioxidant defense (i.e., gene expressions and activities of catalase (CAT) and superoxide dismutase (SOD)) in the intestine, leading to the increased in PC and MDA contents, suggesting that OTC had a synergistic effect on Cd-induced oxidative damage. In the hepatopancreas, although OTC alone increased OTC accumulation, it did not affect PC and MDA contents. Compared to Cd alone, Cd plus OTC reduced MDA content, which was closely related to the improvement of detoxification (i.e., GSH content, mRNA levels of CYP isoforms, EROD activity, gene expressions and activities of GPx, GR and GST), and antioxidant defense (gene expressions and activities of CAT and SOD, metallothionein content). Aryl hydrocarbon receptor (AhR) and nuclear factor E2-related factor 2 (Nrf2) transcriptional expressions were positively correlated with most detoxification- and antioxidant-related gene expressions, respectively, indicating that AhR and Nrf2 were involved in the regulation of these gene expressions. Our results unambiguously demonstrated that OTC had tissue-specific effects on Cd-induced toxicological effect in E. sinensis, which contributed to accurately evaluating Cd toxicity modulated by TCs in crab.

Bright and Stable Red Perovskite LEDs under High Current Densities.

Perovskite LEDs (PeLEDs) have emerged as a next-generation light-emitting technology. Recent breakthroughs were made in achieving highly stable near-infrared and green PeLEDs. However, the operational lifetimes (T50) of visible PeLEDs under high current densities (>10 mA cm-2) remain unsatisfactory (normally <100 h), limiting the possibilities in solid-state lighting and AR/VR applications. This problem becomes more pronounced for mixed-halide (e.g., red and blue) perovskite emitters in which critical challenges such as halide segregation and spectral instability are present. Here, we demonstrate bright and stable red PeLEDs based on mixed-halide perovskites, showing measured T50 lifetimes of up to ∼357 h at currents of ≥25 mA cm-2, a record for the operational stability of visible PeLEDs under high current densities. The devices produce intense and stable emission with a maximum luminance of 28,870 cd m-2 (radiance: 1584 W sr-1 m-2), which is record-high for red PeLEDs. Key to this demonstration is the introduction of sulfonamide, a dipolar molecular stabilizer that effectively interacts with the ionic species in the perovskite emitters. It suppresses halide segregation and migration into the charge-transport layers, resulting in enhanced stability and brightness of the mixed-halide PeLEDs. These results represent a substantial step toward bright and stable PeLEDs for emerging applications.

Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities. To this end, we develop a hybrid multimodality fusion (HMF) framework with cross-domain knowledge transfer for joint MRI and PET representation learning, feature fusion, and cognitive decline progression forecasting. Our HMF consists of three modules: 1) a module to impute missing PET images, 2) a module to extract multimodality features from MRI and PET images, and 3) a module to fuse the extracted multimodality features. To address the issue of small sample sizes, we employ a cross-domain knowledge transfer strategy from the ADNI dataset, which includes 795 subjects, to independent small-scale AD-related cohorts, in order to leverage the rich knowledge present within the ADNI. The proposed HMF is extensively evaluated in three AD-related studies with 272 subjects across multiple disease stages, such as subjective cognitive decline and mild cognitive impairment. Experimental results demonstrate the superiority of our method over several state-of-the-art approaches in forecasting progression trajectories of AD-related cognitive decline.

Regioselective intramolecular cyclization of o-alkynyl arylamines with the in situ formation of ArXCl to construct poly-functionalized 3-selenylindoles.

In this article, a practical and metal-free method for the synthesis of poly-functionalized 3-selenyl/sulfenyl/telluriumindoles from o-alkynyl arylamines has been achieved. In this protocol, the in situ formation of selenenyl chloride, sulfenyl chloride or tellurenyl chloride is considered as the key intermediate and the 3-selenyl/sulfenyl/telluriumindoles can be obtained in good to excellent yields. Furthermore, the product 2-phenyl-3-(phenylselanyl)-1-tosyl-1H-indole can be selectively oxidized to compounds 2-phenyl-3-(phenylseleninyl)-1-tosyl-1H-indole and 2-phenyl-3-(phenylselenonyl)-1-tosyl-1H-indole in good yields.

Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline.

Growing evidence shows that subjective cognitive decline (SCD) among elderly individuals is the possible pre-clinical stage of Alzheimer's disease (AD). To prevent the potential disease conversion, it is critical to investigate biomarkers for SCD progression. Previous learning-based methods employ T1-weighted magnetic resonance imaging (MRI) data to aid the future progression prediction of SCD, but often fail to build reliable models due to the insufficient number of subjects and imbalanced sample classes. A few studies suggest building a model on a large-scale AD-related dataset and then applying it to another dataset for SCD progression via transfer learning. Unfortunately, they usually ignore significant data distribution gaps between different centers/domains. With the prior knowledge that SCD is at increased risk of underlying AD pathology, we propose a domain-prior-induced structural MRI adaptation (DSMA) method for SCD progression prediction by mitigating the distribution gap between SCD and AD groups. The proposed DSMA method consists of two parallel feature encoders for MRI feature learning in the labeled source domain and unlabeled target domain, an attention block to locate potential disease-associated brain regions, and a feature adaptation module based on maximum mean discrepancy (MMD) for cross-domain feature alignment. Experimental results on the public ADNI dataset and an SCD dataset demonstrate the superiority of our method over several state-of-the-arts.

Understanding Clinical Progression of Late-Life Depression to Alzheimer's Disease Over 5 Years with Structural MRI.

Previous studies have shown that late-life depression (LLD) may be a precursor of neurodegenerative diseases and may increase the risk of dementia. At present, the pathological relationship between LLD and dementia, in particularly Alzheimer's disease (AD) is unclear. Structural MRI (sMRI) can provide objective biomarkers for the computer-aided diagnosis of LLD and AD, providing a promising solution to understand the clinical progression of brain disorders. But few studies have focused on sMRI-based predictive analysis of clinical progression from LLD to AD. In this paper, we develop a deep learning method to predict the clinical progression of LLD to AD up to 5 years after baseline time using T1-weighted structural MRIs. We also analyze several important factors that limit the diagnostic performance of learning-based methods, including data imbalance, small-sample-size, and multi-site data heterogeneity, by leveraging a relatively large-scale database to aid model training. Experimental results on 308 subjects with sMRIs acquired from 2 imaging sites and the publicly available ADNI database demonstrate the potential of deep learning in predicting the clinical progression of LLD to AD. To the best of our knowledge, this is among the first attempts to explore the complex pathophysiological relationship between LLD and AD based on structural MRI using a deep learning method.

Tuning Precursor-Amine Interactions for Light-Emitting Lead Bromide Perovskites.

Organic additives with amino moieties are effective in improving the properties of archetypical formamidinium (FA)-based hybrid perovskites for photovoltaic and light-emitting applications. However, a detailed understanding of how amino additives affect the perovskite materials is lacking, impeding developments in this area. Here, by investigating the interactions of lead bromide perovskite precursors with phenethylamine (PEA) and its derivatives with small variations in chemical structure, we reveal that only the secondary amine (N-methyl-2-phenylethylamine (N-PEA)) results in strengthened hydrogen bonds with FABr in precursor solutions, allowing the formation of high-quality perovskite films. The photoluminescence quantum efficiencies (PLQEs) of the resultant perovskite samples on widely used charge-transport substrates are retained to 82% of their original values, indicating reduced sensitivity to interfacial nonradiative traps critical to device applications. Using a standard device structure, green perovskite light-emitting diodes with peak external quantum efficiencies of 12.7% at ∼500 cd m-2 and operational lifetimes (T50) exceeding 10 h (at 100 cd m-2) are obtained.