Deletion of heat shock protein 60 in adult mouse cardiomyocytes perturbs mitochondrial protein homeostasis and causes heart failure.

To maintain healthy mitochondrial enzyme content and function, mitochondria possess a complex protein quality control system, which is composed of different endogenous sets of chaperones and proteases. Heat shock protein 60 (HSP60) is one of these mitochondrial molecular chaperones and has been proposed to play a pivotal role in the regulation of protein folding and the prevention of protein aggregation. However, the physiological function of HSP60 in mammalian tissues is not fully understood. Here we generated an inducible cardiac-specific HSP60 knockout mouse model, and demonstrated that HSP60 deletion in adult mouse hearts altered mitochondrial complex activity, mitochondrial membrane potential, and ROS production, and eventually led to dilated cardiomyopathy, heart failure, and lethality. Proteomic analysis was performed in purified control and mutant mitochondria before mutant hearts developed obvious cardiac abnormalities, and revealed a list of mitochondrial-localized proteins that rely on HSP60 (HSP60-dependent) for correctly folding in mitochondria. We also utilized an in vitro system to assess the effects of HSP60 deletion on mitochondrial protein import and protein stability after import, and found that both HSP60-dependent and HSP60-independent mitochondrial proteins could be normally imported in mutant mitochondria. However, the former underwent degradation in mutant mitochondria after import, suggesting that the protein exhibited low stability in mutant mitochondria. Interestingly, the degradation could be almost fully rescued by a non-specific LONP1 and proteasome inhibitor, MG132, in mutant mitochondria. Therefore, our results demonstrated that HSP60 plays an essential role in maintaining normal cardiac morphology and function by regulating mitochondrial protein homeostasis and mitochondrial function

Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature

BACKGROUND: Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs. RESULTS: When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively. CONCLUSIONS: We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure

Humanoid Robot Cooperative Motion Control Based on Optimal Parameterization

The implementation of low-energy cooperative movements is one of the key technologies for the complex control of the movements of humanoid robots. A control method based on optimal parameters is adopted to optimize the energy consumption of the cooperative movements of two humanoid robots. A dynamic model that satisfies the cooperative movements is established, and the motion trajectory of two humanoid robots in the process of cooperative manipulation of objects is planned. By adopting the control method with optimal parameters, the parameters optimization of the energy consumption index function is performed and the stability judgment index of the robot in the movement process is satisfied. Finally, the effectiveness of the method is verified by simulations and experimentations

Titanium Nitride Film on Sapphire Substrate with Low Dielectric Loss for Superconducting Qubits

Dielectric loss is one of the major decoherence sources of superconducting qubits. Contemporary high-coherence superconducting qubits are formed by material systems mostly consisting of superconducting films on substrate with low dielectric loss, where the loss mainly originates from the surfaces and interfaces. Among the multiple candidates for material systems, a combination of titanium nitride (TiN) film and sapphire substrate has good potential because of its chemical stability against oxidization, and high quality at interfaces. In this work, we report a TiN film deposited onto sapphire substrate achieving low dielectric loss at the material interface. Through the systematic characterizations of a series of transmon qubits fabricated with identical batches of TiN base layers, but different geometries of qubit shunting capacitors with various participation ratios of the material interface, we quantitatively extract the loss tangent value at the substrate-metal interface smaller than $8.9 \times 10^{-4}$ in 1-nm disordered layer. By optimizing the interface participation ratio of the transmon qubit, we reproducibly achieve qubit lifetimes of up to 300 $\mu$s and quality factors approaching 8 million. We demonstrate that TiN film on sapphire substrate is an ideal material system for high-coherence superconducting qubits. Our analyses further suggest that the interface dielectric loss around the Josephson junction part of the circuit could be the dominant limitation of lifetimes for state-of-the-art transmon qubits

Additional file 2: Table S1. of The genetic variants in 3’ untranslated region of voltage-gated sodium channel alpha 1 subunit gene affect the mRNA-microRNA interactions and associate with epilepsy

Genetic variants and alleles in 3'UTR of SCN1A_v001 of male patients and controls (in *.ped file); Table S2. genetic variants and alleles in 3'UTR of SCN1A_v001 of female patients and controls (in *.ped file); Table S3. genetic variants in 3'UTR-SCN1A found in study subjects and their locations (in *.info file); Table S4. Fisher’s exact test on rare genetic variants for case/control association study; Table S5. 50 most expressed miRNA in four parts of CNS; Table S6. STarMir parameters of predicted miRNA-binding sites of 3’UTR of SCN1A gene in genotype groups; Table S7. the frequently lost and compensatory sites for the alteration in conserved sites of miRNAs binding of 3’UTR-SCN1A in genotype groups. Table S8. the conserved sites of miRNA binding in wild type 3’UTR-SCN1A. Table S9. the comparison of STarMir parameters between males and females. “S0”-“S14.txt” were the working input files for STarMir analysis. S0. wild type (reference) 3’UTR sequence; S1. male CTTTA haplotype 3’UTR sequence; S2. male CTCTA haplotype 3’UTR sequence; S3. male CCTTA haplotype 3’UTR sequence; S4. male TTTTA haplotype 3’UTR sequence; S5. female CTTAACA haplotype 3’UTR sequence; S6. female TTCAACA haplotype 3’UTR sequence; S7. female TTTAACA 3’UTR sequence; S8. female 6568_6571del 3’UTR sequence; S9. female 7338_7344del 3’UTR sequence; S10. female 7065_7066insG 3’UTR sequence; S11. 50 microRNAs expressed in human hippocampus; S12. 50 microRNAs expressed in human frontal cortex; S13. 50 microRNAs expressed in human cerebellum; S14. 50 microRNAs expressed in human midbrain. (ZIP 403 kb

Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text

Abstract Background Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction; but most of them consider sentence sequence only, without modeling syntactic structures. The aim of this study was to utilize a deep neural network to capture the syntactic features and further improve the performances of relation extraction in clinical notes. Methods We propose a novel neural approach to model shortest dependency path (SDP) between target entities together with the sentence sequence for clinical relation extraction. Our neural network architecture consists of three modules: (1) sentence sequence representation module using bidirectional long short-term memory network (Bi-LSTM) to capture the features in the sentence sequence; (2) SDP representation module implementing the convolutional neural network (CNN) and Bi-LSTM network to capture the syntactic context for target entities using SDP information; and (3) classification module utilizing a fully-connected layer with Softmax function to classify the relation type between target entities. Results Using the 2010 i2b2/VA relation extraction dataset, we compared our approach with other baseline methods. Our experimental results show that the proposed approach achieved significant improvements over comparable existing methods, demonstrating the effectiveness of utilizing syntactic structures in deep learning-based relation extraction. The F-measure of our method reaches 74.34% which is 2.5% higher than the method without using syntactic features. Conclusions We propose a new neural network architecture by modeling SDP along with sentence sequence to extract multi-relations from clinical text. Our experimental results show that the proposed approach significantly improve the performances on clinical notes, demonstrating the effectiveness of syntactic structures in deep learning-based relation extraction

Functions of COP1/SPA E3 Ubiquitin Ligase Mediated by MpCRY in the Liverwort <i>Marchantia polymorpha</i> under Blue Light

COP1/SPA1 complex in Arabidopsis inhibits photomorphogenesis through the ubiquitination of multiple photo-responsive transcription factors in darkness, but such inhibiting function of COP1/SPA1 complex would be suppressed by cryptochromes in blue light. Extensive studies have been conducted on these mechanisms in Arabidopsis whereas little attention has been focused on whether another branch of land plants bryophyte utilizes this blue-light regulatory pathway. To study this problem, we conducted a study in the liverwort Marchantia polymorpha and obtained a MpSPA knock-out mutant, in which Mpspa exhibits the phenotype of an increased percentage of individuals with asymmetrical thallus growth, similar to MpCRY knock-out mutant. We also verified interactions of MpSPA with MpCRY (in a blue light-independent way) and with MpCOP1. Concomitantly, both MpSPA and MpCOP1 could interact with MpHY5, and MpSPA can promote MpCOP1 to ubiquitinate MpHY5 but MpCRY does not regulate the ubiquitination of MpHY5 by MpCOP1/MpSPA complex. These data suggest that COP1/SPA ubiquitinating HY5 is conserved in Marchantia polymorpha, but dissimilar to CRY in Arabidopsis, MpCRY is not an inhibitor of this process under blue light

Time-sensitive clinical concept embeddings learned from large electronic health records

Abstract Background Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependencies along the longitudinal sequence of a patient’s records, which may lead to incorrect selection of contexts. Methods To address this issue, we extended three popular concept embedding learning methods: word2vec, positive pointwise mutual information (PPMI) and FastText, to consider time-sensitive information. We then trained them on a large electronic health records (EHR) database containing about 50 million patients to generate concept embeddings and evaluated them for both intrinsic evaluations focusing on concept similarity measure and an extrinsic evaluation to assess the use of generated concept embeddings in the task of predicting disease onset. Results Our experiments show that embeddings learned from information within one visit (time window zero) improve performance on the concept similarity measure and the FastText algorithm usually had better performance than the other two algorithms. For the predictive modeling task, the optimal result was achieved by word2vec embeddings with a 30-day sliding window. Conclusions Considering time constraints are important in training clinical concept embeddings. We expect they can benefit a series of downstream applications