BackgroundMulticolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks.MethodsFrom 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set.FindingsPromising accuracies (84·6% to 92·4%) and AUCs (0·921–0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p < 0·0001) and overall survival (13·6 vs 6·5 months, p < 0·0001) for AML.InterpretationThrough large-scaled clinical validation, we showed that AI algorithms can produce efficient and clinically-relevant MFC analysis. This approach also possesses a great advantage of the ability to integrate other clinical tests.FundThis work was supported by the Ministry of Science and Technology (107-2634-F-007-006 and 103–2314-B-002-185-MY2) of Taiwan.

Parkinson’s disease (PD) is known as a mitochondrial disease. Some even regarded it specifically as a disorder of the complex I of the electron transport chain (ETC). The ETC is fundamental for mitochondrial energy production which is essential for neuronal health. In the past two decades, more than 20 PD-associated genes have been identified. Some are directly involved in mitochondrial functions, such as PRKN, PINK1, and DJ-1. While other PD-associate genes, such as LRRK2, SNCA, and GBA1, regulate lysosomal functions, lipid metabolism, or protein aggregation, some have been shown to indirectly affect the electron transport chain. The recent identification of CHCHD2 and UQCRC1 that are critical for functions of complex IV and complex III, respectively, provide direct evidence that PD is more than just a complex I disorder. Like UQCRC1 in preventing cytochrome c from release, functions of ETC proteins beyond oxidative phosphorylation might also contribute to the pathogenesis of PD.

A growing number of human-centered applications benefit from continuous advancements in the emotion recognition technology. Many emotion recognition algorithms have been designed to model multimodal behavior cues to achieve high performances. However, most of them do not consider the modulating factors of an individual's personal attributes in his/her expressive behaviors. In this work, we propose a Personalized Attributes-Aware Attention Network (PAaAN) with a novel personalized attention mechanism to perform emotion recognition using speech and language cues. The attention profile is learned from embeddings of an individual's profile, acoustic, and lexical behavior data. The profile embedding is derived using linguistics inquiry word count computed between the target speaker and a large set of movie scripts. Our method achieves the stateof-the-art 70.3% unweighted accuracy in a four class emotion recognition task on the IEMOCAP. Further analysis reveals that affect-related semantic categories are emphasized differently for each speaker in the corpus showing the effectiveness of our attention mechanism for personalization.

Highlights d Neuronal reduction of uqcrc1, the ETC complex III subunit, causes PD-like symptoms d uqcrc1 regulates DA neuronal maintenance and locomotor activity in flies d The disease-associated uqcrc1 variant fails to bind cytochrome c, triggering apoptosis d Targeting cytochrome c, but not ROS, ameliorates uqcrc1mediated neurodegeneration