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Transition metal single-atom catalysts with nitrogen-doped carbon substrates were designed and analyzed as the catalysts to oxidize Hg0 with O2, using density functional theory calculations, scaling relation analysis, and microkinetic modeling.

Catalytic oxidation of NO and Hg

MotivationSequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.ResultsWe present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.Availability and implementationThe implementation is available at https://github.com/muhaochen/seq_ppi.git.Supplementary informationSupplementary data are available at Bioinformatics online.

We explored the interplay between energy metabolism and the impact of rapamycin (Rapa) on regulatory T cell (Treg) differentiation. Naive CD4+ T cells were stimulated under Treg-polarizing conditions with or without Rapa. Rapa promoted Treg induction, as the expression of Foxp3 and Treg phenotypic markers were enhanced. Rapa disrupts glycolysis while favoring mitochondrial metabolism in induced Tregs (iTregs). Metabolic profiling showed reduced glycolytic metabolites in Rapa-treated iTregs, in line with the downregulation of glucose uptake and the expression of glycolytic enzymes. Conversely, Rapa increased the ratios of ATP/ADP and ATP/AMP, the production of mitochondrial ATP, and the expression of ATP5A. Treatment with oxidative phosphorylation inhibitors suppressed Foxp3 expression in Rapa-treated cells. Moreover, Rapa decreased oleic acid and palmitoleic acid levels and increased l-carnitine and acetylcarnitine levels and CPT1A expression in iTregs, indicative of augmented fatty acid oxidation. In conclusion, Rapa induces metabolic reprogramming in Tregs, affecting their differentiation.

Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and KG entities in the same space and conduct query answering via dense similarity search. However, most logical operators designed in previous studies do not satisfy the axiomatic system of classical logic, limiting their performance. Moreover, these logical operators are parameterized and thus require many complex FOL queries as training data, which are often arduous to collect or even inaccessible in most real-world KGs. We thus present FuzzQE, a fuzzy logic based logical query embedding framework for answering FOL queries over KGs. FuzzQE follows fuzzy logic to define logical operators in a principled and learning-free manner, where only entity and relation embeddings require learning. FuzzQE can further benefit from labeled complex logical queries for training. Extensive experiments on two benchmark datasets demonstrate that FuzzQE provides significantly better performance in answering FOL queries compared to state-of-the-art methods. In addition, FuzzQE trained with only KG link prediction can achieve comparable performance to those trained with extra complex query data., Comment: AAAI'2022

Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple. Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however, existing embedding methods only model triple-level uncertainty, and reasoning results lack global consistency. To address these shortcomings, we propose BEUrRE, a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics. BEUrRE models each entity as a box (i.e. axis-aligned hyperrectangle) and relations between two entities as affine transforms on the head and tail entity boxes. The geometry of the boxes allows for efficient calculation of intersections and volumes, endowing the model with calibrated probabilistic semantics and facilitating the incorporation of relational constraints. Extensive experiments on two benchmark datasets show that BEUrRE consistently outperforms baselines on confidence prediction and fact ranking due to its probabilistic calibration and ability to capture high-order dependencies among facts., Comment: NAACL-HLT 2021

Natural regulatory T cells (nTregs) play a dominant role in maintaining immunological homeostasis and they are known to undergo metabolic reprogramming during immune responses. Transforming growth factor-β1 (TGF-β1), an anti-inflammatory cytokine, can promote the induction of regulatory T cells. Here, we investigated the effects of TGF-β1 on the stability and metabolism of nTregs stimulated in vitro. CD4+CD25+ nTregs were isolated from mouse spleens and stimulated with anti-CD3 and anti-CD28 antibodies plus IL-2 in the presence or absence of TGF-β1. Exposure to TGF-β1 induced the activation of STAT5 and sustained the expression of the nTregs transcription factor Foxp3. In addition, TGF-β1 inhibited glycolysis, as shown by reduced lactate production and diminished expression of Glut1, Hk2, Enolase1, and Hif-1α. nTregs treated with TGF-β1 exhibited downregulated mTORC1 signaling but enhanced activation of the serine-threonine phosphatase PP2A. Moreover, treat with the PP2A inhibitor okadaic acid disrupted the maintenance of Foxp3 expression by TGF-β1. Thus, TGF-β1 serves to maintain Foxp3 expression in cultured nTregs, possibly via PP2A activation and suppression of mTORC1-regulated glycolysis.

Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works using KG embeddings. While existing KG embedding approaches mainly learn and predict facts within a single KG, a more plausible solution would benefit from the knowledge in multiple language-specific KGs, considering that different KGs have their own strengths and limitations on data quality and coverage. This is quite challenging, since the transfer of knowledge among multiple independently maintained KGs is often hindered by the insufficiency of alignment information and the inconsistency of described facts. In this paper, we propose KEnS, a novel framework for embedding learning and ensemble knowledge transfer across a number of language-specific KGs. KEnS embeds all KGs in a shared embedding space, where the association of entities is captured based on self-learning. Then, KEnS performs ensemble inference to combine prediction results from embeddings of multiple language-specific KGs, for which multiple ensemble techniques are investigated. Experiments on five real-world language-specific KGs show that KEnS consistently improves state-of-the-art methods on KG completion, via effectively identifying and leveraging complementary knowledge., Comment: Findings of EMNLP 2020

Objectives This study aimed to identify factors associated with initiation and adherence of osteoporosis medication from a patient perspective. Methods A web-based survey was developed based on health behavior theories. Descriptive analyses were conducted for all survey items. Analyses in a structural equation modeling framework were conducted to identify factors associated with treatment initiation and adherence. Results Five hundred forty-five women completed the questionnaire. A majority were currently receiving medications for osteoporosis (n = 376, 69.0%) and 25.0% of these patients (n = 94) were considered adherent to their treatment. Knowledge was strongly associated with osteoporosis treatment initiation (standard error [SE], 0.58). Greater knowledge of disease was associated with increased likelihood of initiating medication. Medication complexity (SE, 0.49) and perceived susceptibility to fracture and loss of independence (SE, −0.37) were also associated with initiation. Perceived barriers (SE, −0.85) such as inconvenience, lack of efficacy and financial burden were observed to be the greatest obstacle to adherence. The greater the perceived barriers, the less likely patients were to adhere to medication. Patients' perception of self-efficacy (SE, 0.37) also affected adherence. The greater the patient perception of ability to independently manage their medication, the more likely they were to adhere to the medication. Conclusions Different factors were found to be associated with initiation and adherence of osteoporosis medication. Patient knowledge of their disease and the perception of barriers were found to be the most influential. Empowering patients with the knowledge to better understand their disease and decreasing the perception of barriers through education initiatives may be effective in improving patient outcomes.

The transcription factor hypoxia inducible factor-1α (HIF-1α) mediates adaptive responses to oxidative stress by nuclear translocation and regulation of gene expression. Mitochondrial changes are critical for the adaptive response to oxidative stress. However, the transcriptional and non-transcriptional mechanisms by which HIF-1α regulates mitochondria in response to oxidative stress are poorly understood. Here, we examined the subcellular localization of HIF-1α in human cells and identified a small fraction of HIF-1α that translocated to the mitochondria after exposure to hypoxia or H2O2 treatment. Moreover, the livers of mice with CCl4-induced fibrosis showed a progressive increase in HIF-1α association with the mitochondria, indicating the clinical relevance of this finding. To probe the function of this HIF-1α population, we ectopically expressed a mitochondrial-targeted form of HIF-1α (mito-HIF-1α). Expression of mito-HIF-1α was sufficient to attenuate apoptosis induced by exposure to hypoxia or H2O2-induced oxidative stress. Moreover, mito-HIF-1α expression reduced the production of reactive oxygen species, the collapse of mitochondrial membrane potential, and the expression of mitochondrial DNA-encoded mRNA in response to hypoxia or H2O2 treatment independently of nuclear pathways. These data suggested that mitochondrial HIF-1α protects against oxidative stress induced-apoptosis independently of its well-known role as a transcription factor., Graphical abstract fx1, Highlights • HIF-1α is recruited to mitochondria in response to oxidative stress. • Mitochondrial HIF-1α protects against oxidative stress induced apoptosis. • HIF-1α in mitochondria reduces ROS levels and reverses mitochondrial damage. • Mitochondrial HIF-1α reduces mtDNA encoded mRNA levels. • Mitochondrial HIF-1α may involve in liver fibrosis.

Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information. Hence, we present an end-to-end framework, Lasagna, for PPI predictions using only the primary sequences of a protein pair. Lasagna incorporates a deep residual recurrent convolutional neural network in the Siamese learning architecture, which leverages both robust local features and contextualized information that are significant for capturing the mutual influence of protein sequences. Our framework relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that Lasagna outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.

Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different confidence score modeling strategies. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results, and it consistently outperforms baselines on these tasks.

Populating ontology graphs represents a long-standing problem for the Semantic Web community. Recent advances in translation-based graph embedding methods for populating instance-level knowledge graphs lead to promising new approaching for the ontology population problem. However, unlike instance-level graphs, the majority of relation facts in ontology graphs come with comprehensive semantic relations, which often include the properties of transitivity and symmetry, as well as hierarchical relations. These comprehensive relations are often too complex for existing graph embedding methods, and direct application of such methods is not feasible. Hence, we propose On2Vec, a novel translation-based graph embedding method for ontology population. On2Vec integrates two model components that effectively characterize comprehensive relation facts in ontology graphs. The first is the Component-specific Model that encodes concepts and relations into low-dimensional embedding spaces without a loss of relational properties; the second is the Hierarchy Model that performs focused learning of hierarchical relation facts. Experiments on several well-known ontology graphs demonstrate the promising capabilities of On2Vec in predicting and verifying new relation facts. These promising results also make possible significant improvements in related methods.

Adaptive inverse control, proposed by Bernard Widrow, is mainly based on the well-known least mean square (LMS) algorithm. The LMS is a stochastic gradient algorithm under the minimum mean square error (MSE) criterion, which performs well for linear and Gaussian systems. However, its performance will become poor when signals are non-Gaussian, especially when systems are disturbed by impulsive noises. In this work, in order to improve the robustness of the adaptive inverse control against impulsive noises, we propose a new adaptive inverse control method, which is based on the recently developed maximum correntropy criterion (MCC) algorithm. The MCC algorithm aims at maximizing the correntropy between the model output and the desired response. Since correntropy is a nonlinear similarity measure that contains higher-order statistics of the signals and is insensitive to large outliers, the proposed method can achieve desirable performance in impulsive noise environments. Theoretical results on optimal solution and convergence are derived. Simulation results are also presented to demonstrate the superior performance of the new method.

The immunosuppressive drug rapamycin (RAPA) has been used clinically to prevent graft rejection since 1999 because of its suppressive effects on T cell activation and proliferation. Recently, many studies have suggested that RAPA also has the potential to promote tolerance by driving the expansion of naturally occurring regulatory T (Treg) cells, and facilitating the de novo generation of induced Treg cells, which has aroused great interest in its potential ability to promote tolerance after transplantation. However, its effect on Treg cells remains controversial both in vitro and in vivo. Here, we systematically analyzed data on the effects of RAPA from both clinical and basic studies: (1) To compare its clinical effect with calcineurin inhibitors in transplant recipients, and discuss whether its effects on graft survival correlates with its effects on Treg cells. (2) To analyze the effects of RAPA on Treg cells from animal and in vitro studies, and to investigate whether the effects of RAPA on Treg cells was dependent on dosage and timing. (3) To discuss the mechanisms involved and how they might be applied to induce transplant tolerance.