An Algorithm for Finding Functional Modules and Protein Complexes in Protein-Protein Interaction Networks

Biological processes are often performed by a group of proteins rather than by individual proteins, and proteins in a same biological group form a densely connected subgraph in a protein-protein interaction network. Therefore, finding a densely connected subgraph provides useful information to predict the function or protein complex of uncharacterized proteins in the highly connected subgraph. We have developed an efficient algorithm and program for finding cliques and near-cliques in a protein-protein interaction network. Analysis of the interaction network of yeast proteins using the algorithm demonstrates that 59% of the near-cliques identified by our algorithm have at least one function shared by all the proteins within a near-clique, and that 56% of the near-cliques show a good agreement with the experimentally determined protein complexes catalogued in MIPS

Study on Colour Reaction of Vanadium(V) with 2-(2-Quinolylazo)-5-Diethylaminophenol and Its Application

A sensitive, selective and rapid method has been developed for the determination of vanadium based on the rapid reaction of vanadium(V) with 2-(2-quinolylazo)-5-diethylaminophenol (QADEAP). The QADEAP reacts with V(V) in the presence of citric acid-sodium hydroxide buffer solution (pH =3.5) and cetyl trimethylammonium bromide (CTMAB) medium to form a violet chelate of a molar ratio 1:2 (V(V) to QADEAP). The molar absorptivity of the chelate is 1.23 x 105 L mol-1 cm-1 at 590 nm in the measured solution. Beer's law is obeyed in the range of 0.01~0.6 mg mL-1. This method was applied to the determination of vanadium(v) with good results. South African Journal of Chemistry Vol.57 2004: 15-1

Interpretable Heterogeneous Teacher-Student Learning Framework for Hybrid-Supervised Pulmonary Nodule Detection

Existing pulmonary nodule detection methods often train models in a fully-supervised setting that requires strong labels (i.e., bounding box labels) as label information. However, manual annotation of bounding boxes in CT images is very time-consuming and labor-intensive. To alleviate the annotation burden, in this paper, we investigate pulmonary nodule detection by leveraging both strong labels and weak labels (i.e., center point labels) for training, and propose a novel hybrid-supervised pulmonary nodule detection (HND) method. The training of HND involves a heterogeneous teacher-student learning framework in two stages. In the first stage, we design a point-based consistency calibration network (PCC-Net) as a teacher, which is pre-trained to generate high-quality pseudo bounding box labels given point-augmented CT images as inputs. In the second stage, we develop an information bottleneck-guided pulmonary nodule detection network (IBD-Net) as a student to perform pulmonary nodule detection. In particular, we introduce information bottleneck to learn reliable pulmonary nodule-specific heatmaps under the guidance of PCC-Net, largely enhancing the model’s interpretability and improving the final detection performance. Based on the above designs, our method can effectively detect pulmonary nodule regions with only a limited number of bounding box labels. Experimental results on the public pulmonary nodule detection dataset LUNA16 show that our HND method achieves an excellent balance between the annotation cost and the detection performance

Revealing in situ stress-induced short- and medium-range atomic structure evolution in a multicomponent metallic glassy alloy

Deformation behaviour of multicomponent metallic glasses are determined by the evolution/reconfiguration of the short- and medium-range order (SRO and MRO) atomic structures. A precise understanding of how different atom species rearrange themselves in different stress states is still a great challenge in materials science and engineering. Here, we report a systematic and synergetic research of using electron microscopy imaging, synchrotron X-ray total scattering plus empirical potential structure refinement (EPSR) modelling to study in situ the deformation of a Zr-based multicomponent metallic glassy alloy with 5 elements. Systematic and comprehensive analyses on the characteristics of the SRO and MRO structures in 3D and the decoupled 15 partial PDFs at each stress level reveal quantitatively how the SRO and MRO structures evolve or reconfigure in 3D space in the tensile and compressive stress states. The results show that the Zr-centred atom clusters have low degree of icosahedra and are the preferred atom clusters to rearrange themselves under the tensile and compressive stresses. The Zr-Zr is the dominant atom pair in controlling the shear band's initiation and propagation. The evolution and reconfiguration of the MRO clusters under different stress states are realised by changing the connection modes between the Zr-centred atom clusters. The coordinated changes of both bond angles and bond lengths of the Zr-centred clusters are the dominant factors in accommodating the tensile or compressive strains. While other solute-centred MRO clusters only play minor roles in the atomic structure reconfiguration/evolution. The research has demonstrated a synergetic and multimodal materials operando characterization methodology that has great application potential in design and development of high performance multiple-component engineering alloys

Visual-Textual Attribute Learning for Class-Incremental Facial Expression Recognition

In this paper, we study facial expression recognition (FER) in the class-incremental learning (CIL) setting, which defines the classification of well-studied and easily-accessible basic expressions as an initial task while learning new compound expressions gradually. Motivated by the fact that compound expressions are meaningful combinations of basic expressions, we treat basic expressions as attributes (i.e., semantic descriptors), and thus compound expressions are represented in terms of attributes. To this end, we propose a novel visual-textual attribute learning network (VTA-Net), mainly consisting of a textual-guided visual module (TVM) and a textual compositional module (TCM), for class-incremental FER. Specifically, TVM extracts textual-aware visual features and classifies expressions by incorporating the textual information into visual attribute learning. Meanwhile, TCM generates visual-aware textual features and predicts expressions by exploiting the dependency between textual attributes and category names of old and new expressions based on a textual compositional graph. In particular, a visual-textual distillation loss is introduced to calibrate TVM and TCM during incremental learning. Finally, the outputs from TVM and TCM are fused to make a final prediction. On the one hand, at each incremental task, the representations of visual attributes are enhanced since visual attributes are shared across old and new expressions. This increases the stability of our method. On the other hand, the textual modality, which involves rich prior knowledge of the relevance between expressions, facilitates our model to identify subtle visual distinctions between compound expressions, improving the plasticity of our method. Experimental results on both in-the-lab and in-the-wild facial expression databases show the superiority of our method against several state-of-the-art methods for class-incremental FER

Guided Time-optimal Model Predictive Control of a Multi-rotor

Time-optimal control of a multi-rotor remains an open problem due to the under-actuation and nonlinearity of its dynamics, which make it difficult to solve this problem directly. In this paper, the time-optimal control problem of the multi-rotor is studied. Firstly, a thrust limit optimal decomposition method is proposed, which can reasonably decompose the limited thrust into three directions according to the current state and the target state. As a result, the thrust limit constraint is decomposed as a linear constraint. With the linear constraint and decoupled dynamics, a time-optimal guidance trajectory can be obtained. Then, a cost function is defined based on the time-optimal guidance trajectory, which has a quadratic form and can be used to evaluate the time-optimal performance of the system outputs. Finally, based on the cost function, the time-optimal control problem is reformulated as an MPC (Model Predictive Control) problem. The experimental results demonstrate the feasibility and validity of the proposed methods.Comment: 6 pages, 5 figure

Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple client devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training and reduces the dimension of local gradients for back propagation (BP) via last-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at client devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization.Comment: 15 pages, 13 figure

Do statins improve outcomes for patients with non-small cell lung cancer? A systematic review and meta-analysis protocol

Introduction Lung cancer is the most common neoplasm and the leading cause of cancer-related death worldwide. Non-small cell lung cancer (NSCLC), accounting for 85% of all lung cancer cases, is frequently diagnosed at an advanced and metastatic stage. In addition, survival of patients with NSCLC has not improved significantly over the recent decades. Statins are used as a cholesterol-lowering agent, but recently preclinical and clinical studies have revealed their anticancer effects. Thus, this systematic review and meta-analysis aims to clarify whether statins improve the prognosis of patients with NSCLC. Methods and analysis We will search MEDLINE (PubMed), EMBASE, Web of Science, the Cochrane Central Register of Controlled Trials and ClinicalTrials.gov with no restriction on language. Both randomised controlled trials (RCTs) and observational cohort studies evaluating the prognostic role of statins in patients with NSCLC will be included. The primary outcome will be overall survival, and the secondary outcomes will include cancer-specific survival, disease-free survival and cancer recurrence. Two assessors will assess the RCTs using the Cochrane Collaboration's risk of bias tool and the observational cohort studies according to ROBINS-I. Publication bias will be assessed by funnel plot using the STATA software v.13.1. Ethics and dissemination No ethical issues are predicted. This systematic review and meta-analysis aims to describe the prognostic effects of statins in patients with NSCLC, which would help clinicians to optimise treatment for patients with NSCLC. These findings will be published in a peer-reviewed journal and presented at national and international conferences. PROSPERO registration number CRD42016047524