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Noisy domain adaptation aims to solve the problem that the source dataset contains noisy labels in domain adaptation. Previous methods handle noisy labels by selecting the small-loss samples with inconsistent predictions between two models and discarding the consistent samples, resulting in many noises contained in the selected samples. By jointly considering the consistent and inconsistent samples, we propose a model-agnostic module, named Seek Common Ground While Reserving Differences (SCGWRD), to reduce the impact of noisy samples. The proposed SCGWRD module consists of Seek Common Ground (SCG) component and Reserve Differences (RD) component by utilizing the outputs of two symmetrical domain adaptation models. As the common samples with consistent predictions between two models are more likely to be clean samples, the SCG component applies the small-loss strategy to select the reliable samples with consistent predictions. Unlike SCG, the RD component maintains the divergences between two models with mutual learning and reduces the effect of noisy data using the samples with different predictions and small losses. Evaluations on three benchmarks demonstrate the effectiveness and robustness of the proposed SCGWRD module for noisy domain adaptation.

Many bioconjugation strategies for DNA oligonucleotides and antibodies suffer limitations, such as site���specificity, stoichiometry and hydrolytic instability of the conjugates, which makes them unsuitable for biological applications. Here, we report a new platform for the preparation of DNA���antibody bioconjugates with a simple benzoylacrylic acid pentafluorophenyl ester reagent. Benzoylacrylic���labelled oligonucleotides prepared with this reagent can be site���specifically conjugated to a range of proteins and antibodies through accessible cysteine residues. The homogeneity of the prepared DNA���antibody bioconjugates was confirmed by a new LC���MS protocol and the bioconjugate probes were used in fluorescence or super���resolution microscopy cell imaging experiments. This work demonstrates the versatility and robustness of our bioconjugation protocol that gives site���specific, well���defined and plasma���stable DNA���antibody bioconjugates for biological applications.

Many bioconjugation strategies for DNA oligonucleotides and antibodies suffer limitations, such as site-specificity, stoichiometry and hydrolytic instability of the conjugates, which makes them unsuitable for biological applications. Here, we report a new platform for the preparation of DNA-antibody bioconjugates with a simple benzoylacrylic acid pentafluorophenyl ester reagent. Benzoylacrylic-labelled oligonucleotides prepared with this reagent can be site-specifically conjugated to a range of proteins and antibodies through accessible cysteine residues. The homogeneity of the prepared DNA-antibody bioconjugates was confirmed by a new LC-MS protocol and the bioconjugate probes were used in fluorescence or super-resolution microscopy cell imaging experiments. This work demonstrates the versatility and robustness of our bioconjugation protocol that gives site-specific, well-defined and plasma-stable DNA-antibody bioconjugates for biological applications.

The age of the population is shifting toward the elderly range, which may lead to an increased risk of mild cognitive impairment (MCI). The aims of this study are to evaluate the cognitive function in elderly people using the Montreal Cognitive Assessment (MoCA), to identify the relationship between cognitive function and different characteristics, and to evaluate the efficacy of the intervention after six months of cognitive training. In this study, we included 2886 subjects aged ≧60 years in the baseline survey, and 140 subjects with MCI who participated in the baseline survey were randomly divided into an intervention group (N = 70) and a control group (N = 70). The control group was not provided any intervention measures, and the intervention group was administered cognitive training. The education level, monthly income, sleep time, exercise time, reading times, and time spent engaging in community activities and performing housework were positively correlated with MoCA scores, but age was negatively correlated with MoCA scores. The total MoCA score of the intervention group increased from 19.77 ± 2.24 points to 21.09 ± 2.20 points after six months of cognitive training, but the score of the control group decreased from 20.41 ± 2.10 points to 19.17 ± 2.57 points. The two-way repeated-measures ANOVA revealed a very significant effect of the interaction between time and cognitive training on the total MoCA score. Seventeen participants in the intervention group improved to normal levels, and no participants progressed to dementia after six months of cognitive training. Thus, the efficacy of the intervention was statistically significant. Our study concludes that older age is associated with a cognitive decline. Factors that are more likely to protect against cognitive decline included a higher education level and monthly income, sufficient sleep time, regular physical exercise and reading, frequently engaging in community activities, and continuing to perform housework. Moreover, the cognitive training intervention is effective and may help to decrease the deterioration of cognitive function in patients with MCI, and the interaction between intervention time and cognitive training significantly improves cognitive function.

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multi-source domains to one target domain. Inspired by single-source domain adaptation, existing methods solve MSDA by aligning the data distributions between the target domain and each source domain. However, aligning the target domain with the dissimilar source domain would harm the representation learning. To address the above issue, an intuitive motivation of MSDA is using the attention mechanism to enhance the positive effects of the similar domains, and suppress the negative effects of the dissimilar domains. Therefore, we propose Attention-Based Multi-Source Domain Adaptation (ABMSDA) by considering the domain correlations to alleviate the effects caused by dissimilar domains. To obtain the domain correlations between source and target domains, ABMSDA firstly trains a domain recognition model to calculate the probability that the target images belong to each source domain. Based on the domain correlations, Weighted Moment Distance (WMD) is proposed to pay more attention on the source domains with higher similarities. Furthermore, Attentive Classification Loss (ACL) is developed to constrain that the feature extractor can generate the alignment and discriminative visual representations. The evaluations on two benchmarks demonstrate the effectiveness of the proposed model, e.g. , an average of 6.1% improvement on the challenging DomainNet dataset.

Both the replication of protein aggregates and their spreading throughout the brain are implicated in the progression of Alzheimer’s disease (AD). However, the rates of these processes are unknown and the identity of the rate-determining process in humans has therefore remained elusive. By bringing together chemical kinetics with measurements of tau seeds and aggregates across brain regions, we are able to quantify their replication rate in human brains. Remarkably, we obtain comparable rates in several different datasets, with 5 different methods of tau quantification, from seed amplification assaysin vitroto tau PET studies in living patients. Our results suggest that the overall rate of accumulation of tau in neocortical regions is limited not by spreading between brain regions but by local replication, which doubles the number of seeds every ~5 years. Thus, we propose that limiting local replication constitutes the most promising strategy to control tau accumulation during AD.

Domain adaptation aims at learning from a source data distribution a well-performing model on a different target data distribution. Recently, the self-ensembling-based methods have been proved to be effective for unsupervised domain adaptation. However, they still have two shortcomings: 1) no explicitly constraint about the distributions between the source and target domains; 2) the Euclidean distance fails to measure the similarity between two distributions with no overlap. To solve those shortcomings, we propose a novel Category-level Adversarial Self-ensembling (CAS) model for domain adaptation, which contains two types of consistency constraints. The first one is how to constrain the descriptions for source and target domains to be aligned. Therefore, we adopt a minimax game with a discrepancy loss between the category information generated by two classifiers. As the self-ensembling consists of two sub-networks: student and teacher networks, the second one is the consistency between those two networks for the target samples. Aiming to overcome the disadvantage of the Euclidean metric, we employ the Wasserstein distance to measure the difference between two probabilistic distributions. Experiments on several benchmarks demonstrate that our proposed CAS is superior to existing methods.

The ubiquitin (Ub) system regulates a wide range of cellular signaling pathways. Several hundred E1, E2, and E3 enzymes are together responsible for protein ubiquitination, thereby controlling cellular activities. Due to the numerous enzymes and processes involved, studies of ubiquitination activities have been challenging. We here report a novel Förster resonance energy transfer (FRET)-based assay for studying the

In addition to increased aberrant protein aggregation, inflammation has been proposed as a key element in the pathogenesis and progression of Alzheimer’s disease. How inflammation interacts with other disease pathways and how protein aggregation increases during disease are not clear. We used single-molecule imaging approaches and membrane permeabilization assays to determine the effect of chronic exposure to tumour necrosis factor, a master proinflammatory cytokine, on protein aggregation in human-induced pluripotent stem cell-derived neurons harbouring monogenic Alzheimer’s disease mutations. We report that exposure of Alzheimer’s disease neurons, but not control neurons, to tumour necrosis factor induces substantial production of extracellular protein aggregates. Aggregates from Alzheimer’s disease neurons are composed of amyloid-β and α-synuclein and induce significant permeabilization of lipid membranes in an assay of pathogenicity. These findings provide support for a causal relationship between two crucial processes in Alzheimer’s disease pathogenesis and suggest that targeting inflammation, particularly tumour necrosis factor, may have beneficial downstream effects on ameliorating aberrant protein aggregation and accumulation., There is a complex interplay between neuroinflammation and protein aggregation in Alzheimer’s disease. We show that chronic exposure to the inflammatory cytokine tumour necrosis factor stimulates monogenic Alzheimer’s disease neurons to produce significantly more toxic protein aggregates, with no effect on healthy neurons, indicating a causal relationship between neuroinflammation and protein aggregation., Graphical Abstract Graphical Abstract

BACKGROUND The aim of this study was to determine the association between the incidence of road traffic accidents, psychological characteristics, and genotype in bus drivers in a Chinese population. MATERIAL AND METHODS Bus drivers who had been involved in road traffic accidents (n=106) (the study group), and bus drivers with no history of road traffic accidents (n=106) (the control group) completed demographic questionnaires, the Eysenck Personality Questionnaire (EPQ) and the Type-A behavior pattern (TABP) evaluation. Serum 5-hydroxytryptamine (5-HT) (serotonin), and 5-hydroxytryptophan (5-HTP) levels were measured by high-performance liquid chromatography-fluorescent detection (HPLC-FLD). Serotonin transporter promoter-linked polymorphism region (5-HTTLPR) and the 521 C/T single nucleotide polymorphism (SNP) in the regulatory region of the dopamine D4 receptor gene (DRD4-521 C/T) were measured using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). RESULTS After accounting for potential confounders, extroversion, psychopathy, neuroticism and time hurrying (impatience) were significant factors associated with road traffic accidents in bus drivers (adjusted OR: 1.268, 95% CI: 1.133-1.419; adjusted OR: 1.177, 95% CI: 1.028-1.347; adjusted OR: 1.092, 95% CI: 1.005-1.187; adjusted OR: 1.123, 95% CI: 1.025-1.230, respectively). Reduced serum levels of 5-HT and 5-HTP were significantly associated with the incidence of road traffic accidents (adjusted OR: 0.985, 95% CI: 0.973-0.997; adjusted OR: 0.982, 95% CI: 0.969-0.994, respectively). CONCLUSIONS Psychological characteristics associated with the 5-HTTLPR and DRD4-521 C/T genotypes, including extroversion, psychopathy, neuroticism, and time hurrying (impatience), and low serum levels of 5-HT and 5-HTP in bus drivers were associated with an increased risk of road traffic accidents.

The ubiquitin (Ub) system regulates a wide range of cellular signaling pathways. Several hundred E1, E2 and E3 enzymes are together responsible for protein ubiquitination, thereby controlling cellular activities. Due to the numerous enzymes and processes involved, studies on ubiquitination activities have been challenging. We here report a novel FRET-based assay to study thein vitrokinetics of ubiquitination. FRET is established between binding of fluorophore-labeled Ub to eGFP-tagged ZnUBP, a domain that exclusively binds unconjugated Ub. We name this assay the Free Ub Sensor System (FUSS). Using Uba1, UbcH5 and CHIP as model E1, E2 and E3 enzymes, respectively, we demonstrate that ubiquitination results in decreasing FRET efficiency, from which reaction rates can be determined. Further treatment with USP21, a deubiquitinase, leads to increased FRET efficiency, confirming the reversibility of the assay. We subsequently use this assay to show that increasing the concentration of CHIP or UbcH5 but not Uba1 enhances ubiquitination rates, and develop a novel machine learning approach to model ubiquitination. The overall ubiquitination activity is also increased upon incubation with tau, a substrate of CHIP. Our data together demonstrate the versatile applications of a novel ubiquitination assay that does not require labeling of E1, E2, E3 or substrates, and is thus likely compatible with any E1-E2-E3 combinations.

Representation learning of knowledge graphs encodes both entities and relations into a continuous low-dimensional space. Most existing methods focus on learning representations with structured fact triples indicating relations between entities, ignoring rich additional information of entities including entity attributes and associated multimodal content descriptions. In this paper, we propose a new model to learn knowledge representations with entity attributes and multimedia descriptions (KR-AMD). Specifically, we construct three triple encoders to obtain structure-based entity representation, attribute-based entity representation and multimedia content-based entity representation, and finally generate the knowledge representations for knowledge graphs in KR-AMD. The experimental results show that, by special modeling of entity attributes and text-image descriptions, KR-AMD can significantly outperform state-of-the-art KR models in prediction of entities, attributes and relations, which validates the effectiveness of KR-AMD.

The aberrant misfolding and subsequent conversion of monomeric protein into amyloid aggregates characterises many neurodegenerative disorders, including Parkinson's and Alzheimer's diseases. These aggregates are highly heterogeneous in structure, generally of low abundance and typically smaller than the diffraction limit of light (≈250 nm). To overcome the challenges these characteristics pose to the study of endogenous aggregates formed in cells, we have developed a method to characterise them at the nanometre scale without the need for a conjugated fluorophore. Using a combination of DNA PAINT and an amyloid‐specific aptamer, we demonstrate that this technique is able to detect and super‐resolve a range of aggregated species, including those formed by α‐synuclein and amyloid‐β. Additionally, this method enables endogenous protein aggregates within cells to be characterised. We found that neuronal cells derived from patients with Parkinson's disease contain a larger number of protein aggregates than those from healthy controls.