SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions.

BACKGROUND: Detecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge. RESULTS: Here, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs. CONCLUSIONS: Here, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP-SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer. AVAILABILITY AND IMPLEMENTATION: https://github.com/scutdy/SSO/blob/master/SEEI.zip .

Poly-l-lysine modified MOF nanoparticles with pH/ROS sensitive CIP release and CUR triggered photodynamic therapy against drug-resistant bacterial infection.

The challenge of drug resistance in bacteria caused by the over use of biotics is increasing during the therapy process, which has attracted great attentions of the clinicians and scientists around the world. Recently, photodynamic therapy (PDT) triggered by photosensitizer (PS) has become a promising treatment method because of its high efficacy, easy operation, and low side effect. Herein, the poly-l-lysine (PLL) modified metal-organic framework (MOF) nanoparticles, ZIF/PLL-CIP/CUR, were synthesized to allow both reactive oxygen species (ROS) responsive drug release and photodynamic effect for synergistic therapy against drug resistant bacterial infections. The PLL was modified on the shell of the zeolite imidazole framework (ZIF) by the ROS-responsive thioketal linker for controllable CIP release. CUR were encapsulated in ZIF as the photosensitizer for blue light mediated photodynamic effect to produce singlet oxygen (1O2) and superoxide anion radical (O2-) for efficient inhibition towards methicillin-resistant Staphylococcus aureus (MRSA). The charge conversion from negative charge (-4.6 mV) to positive charge (2.6 mV) was observed at pH 7.4 and pH 5.5, and 70.9 % CIP was found released at pH 5.5 in the presence of H2O2, which suggests the good biosafety at physiological pH and ROS-responsive drug release of the as-prepared nanoparticle in the bacterial microenvironment. The as-prepared nanoparticles could effectively kill MRSA and disrupt bacterial biofilm by combination of chemo- and photodynamic therapy. In mice model, the as-prepared nanoparticles exhibited excellent biosafety and synergistic effect with 98.81 % healing rate in treatment of MRSA infection, which is considered as a promising candidate in combating drug resistant bacterial infection.

Magnetic ion-imprinted polyacrylonitrile-chitosan electro-spun nanofibrous membrane as recyclable adsorbent with selective heavy metal removal and antibacterial fouling in water treatment.

Water pollution has become one of the most concerned environmental issues on the worldwide scale. Due to the harmfulness of the heavy metal ions and microorganisms in wastewater, novel filtration membranes for water treatment are expected to simultaneously clear these pollutants. Herein, the electro-spun polyacrylonitrile (PAN) based magnetic ion-imprinted membrane (MIIM) were fabricated to achieve both selective removal of Pb(II) ions and excellent antibacterial efficiency. The competitive removal experiments showed that the MIIM displayed efficiently selective removal of Pb(II) (45.4 mg·g-1). Pseudo-second-order mode and Langmuir isotherm equation is well matched with the equilibrium adsorption. The MIIM showed sustained removal performance (~79.0 %) against Pb(II) ions after 7 adsorption-desorption cycles with negligible Fe ions loss of 7.3 %. Moreover, the MIIM exhibited excellent antibacterial properties that >90 % of E. coli and S. aureus were killed by the MIIM. In conclusion, the MIIM provides a novel technological platform for integration of multi-function with selective metal ions removal, excellent cycling reusability, and enhanced antibacterial fouling property, which can be potentially utilized as a promising adsorbent in actual treatment of polluted water.

SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.

BACKGROUND: The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS: In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS: The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION: The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions.

Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm's evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately.

An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications.

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.

Study on the Mechanism of Liuwei Dihuang Pills in Treating Parkinson's Disease Based on Network Pharmacology.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease in middle-aged and elderly people. Liuwei Dihuang (LWDH) pills have a good effect on PD, but its mechanism remains unclear. Network pharmacology is the result of integrating basic theories and research methods of medicine, biology, computer science, bioinformatics, and other disciplines, which can systematically and comprehensively reflect the mechanism of drug intervention in disease networks. METHODS: The main components and targets of herbs in LWDH pills were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Its active components were screened based on absorption, distribution, metabolism, and excretion (ADME); the PD-related targets were obtained from the Genecards, OMIM, TTD, and DRUGBANK databases. We used R to take the intersection of LWDH- and PD-related targets and Cytoscape software to construct the drug-component-target network. Moreover, STRING and Cytoscape software was used to analyze protein-protein interactions (PPI), construct a PPI network, and explore potential protein functional modules in the network. The Metascape platform was used to perform KEGG pathway and GO function enrichment analyses. Finally, molecular docking was performed to verify whether the compound and target have good binding activity. RESULTS: After screening and deduplication, 210 effective active ingredients, 204 drug targets, 4333 disease targets, and 162 drug-disease targets were obtained. We consequently constructed a drug-component-targets network and a PPI-drug-disease-targets network. The results showed that the hub components of LWDH pills were quercetin, stigmasterol, kaempferol, and beta-sitosterol; the hub targets were AKT1, VEGFA, and IL6. GO and KEGG enrichment analyses showed that these targets are involved in neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic processes, membrane rafts, MAPK signaling pathways, cellular senescence, and other biological processes. Molecular docking showed that the hub components were in good agreement with the hub targets. CONCLUSION: LWDH pills have implications for the treatment of PD since they contain several active components, target multiple ligands, and activate various pathways. The hub components possibly include quercetin, stigmasterol, kaempferol, and beta-sitosterol and act through pairing with hub targets, such as AKT1, VEGFA, and IL6, to regulate neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic process, membrane raft, MAPK signaling pathway, and cellular senescence for the treatment of PD.

A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight.

Node localization, which is formulated as an unconstrained NP-hard optimization problem, is considered as one of the most significant issues of wireless sensor networks (WSNs). Recently, many swarm intelligent algorithms (SIAs) were applied to solve this problem. This study aimed to determine node location with high precision by SIA and presented a new localization algorithm named LMQPDV-hop. In LMQPDV-hop, an improved DV-Hop was employed as an underground mechanism to gather the estimation distance, in which the average hop distance was modified by a defined weight to reduce the distance errors among nodes. Furthermore, an efficient quantum-behaved particle swarm optimization algorithm (QPSO), named LMQPSO, was developed to find the best coordinates of unknown nodes. In LMQPSO, the memetic algorithm (MA) and Lévy flight were introduced into QPSO to enhance the global searching ability and a new fast local search rule was designed to speed up the convergence. Extensive simulations were conducted on different WSN deployment scenarios to evaluate the performance of the new algorithm and the results show that the new algorithm can effectively improve position precision.

External cavity lasing pumped stimulated Brillouin scattering in a high Q microcavity.

Stimulated Brillouin scattering (SBS) in a microcavity is usually realized by employing a wavelength tunable external cavity diode laser (TECDL) as the pump source. In this Letter, we report the observation of SBS in a high Q microcavity based on a TECDL-free scheme. The microcavity is employed as a mode-reflecting mirror for constructing a fiber-ring laser and, simultaneously, pumped by the fiber-ring lasing with intrinsic resonance latching. Several regimes are observed in a microcavity with a diameter of ∼215 µm, such as single lasing pumped SBS and multiple regular lasing pumped SBSs (single or cascaded). The microwave signals from the beat notes of the composite output lasing are measured with full-width at half-maximum on the scale of kilohertz at ∼11 and ∼22 GHz, indicating the high coherence between the pump and the Brillouin lasing.

Generation of optical frequency combs in a fiber-ring/microresonator laser system.

We propose and experimentally demonstrate a simple scheme for generating optical frequency combs (OFCs) in a fiber-ring/microresonator laser system. The ultrahigh Q whispering gallery mode microresonator is employed both as a mode reflection mirror to generate erbium lasing and as a Kerr-nonlinearity initiator that introduces optical parametric oscillation signals to form OFCs. By controlling the coupling position between the fiber taper and microresonator, optimizing the fiber polarization, as well as the pump power from a 974 nm laser diode (LD), versatile OFCs can be tuned out from single-wavelength states. The OFCs have single, multiple, or combined free spectral ranges. In addition, a Raman-gain-assisted OFC is also observed with a bandwidth of ∼230 nm. This LD-pumped and multifunctional laser system could find applications in precision spectroscopy, biochemical sensing, and optical fiber communication systems.