Link prediction in drug-target interactions network using similarity indices.

BACKGROUND: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem. RESULTS: We compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available. CONCLUSION: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions

Adaptive Intelligent Systems for Extreme Environments

As embedded processors become powerful, a growing number of embedded systems equipped with artificial intelligence (AI) algorithms have been used in radiation environments to perform routine tasks to reduce radiation risk for human workers. On the one hand, because of the low price, commercial-off-the-shelf devices and components are becoming increasingly popular to make such tasks more affordable. Meanwhile, it also presents new challenges to improve radiation tolerance, the capability to conduct multiple AI tasks and deliver the power efficiency of the embedded systems in harsh environments. There are three aspects of research work that have been completed in this thesis: 1) a fast simulation method for analysis of single event effect (SEE) in integrated circuits, 2) a self-refresh scheme to detect and correct bit-flips in random access memory (RAM), and 3) a hardware AI system with dynamic hardware accelerators and AI models for increasing flexibility and efficiency. The variances of the physical parameters in practical implementation, such as the nature of the particle, linear energy transfer and circuit characteristics, may have a large impact on the final simulation accuracy, which will significantly increase the complexity and cost in the workflow of the transistor level simulation for large-scale circuits. It makes it difficult to conduct SEE simulations for large-scale circuits. Therefore, in the first research work, a new SEE simulation scheme is proposed, to offer a fast and cost-efficient method to evaluate and compare the performance of large-scale circuits which subject to the effects of radiation particles. The advantages of transistor and hardware description language (HDL) simulations are combined here to produce accurate SEE digital error models for rapid error analysis in large-scale circuits. Under the proposed scheme, time-consuming back-end steps are skipped. The SEE analysis for large-scale circuits can be completed in just few hours. In high-radiation environments, bit-flips in RAMs can not only occur but may also be accumulated. However, the typical error mitigation methods can not handle high error rates with low hardware costs. In the second work, an adaptive scheme combined with correcting codes and refreshing techniques is proposed, to correct errors and mitigate error accumulation in extreme radiation environments. This scheme is proposed to continuously refresh the data in RAMs so that errors can not be accumulated. Furthermore, because the proposed design can share the same ports with the user module without changing the timing sequence, it thus can be easily applied to the system where the hardware modules are designed with fixed reading and writing latency. It is a challenge to implement intelligent systems with constrained hardware resources. In the third work, an adaptive hardware resource management system for multiple AI tasks in harsh environments was designed. Inspired by the “refreshing” concept in the second work, we utilise a key feature of FPGAs, partial reconfiguration, to improve the reliability and efficiency of the AI system. More importantly, this feature provides the capability to manage the hardware resources for deep learning acceleration. In the proposed design, the on-chip hardware resources are dynamically managed to improve the flexibility, performance and power efficiency of deep learning inference systems. The deep learning units provided by Xilinx are used to perform multiple AI tasks simultaneously, and the experiments show significant improvements in power efficiency for a wide range of scenarios with different workloads. To further improve the performance of the system, the concept of reconfiguration was further extended. As a result, an adaptive DL software framework was designed. This framework can provide a significant level of adaptability support for various deep learning algorithms on an FPGA-based edge computing platform. To meet the specific accuracy and latency requirements derived from the running applications and operating environments, the platform may dynamically update hardware and software (e.g., processing pipelines) to achieve better cost, power, and processing efficiency compared to the static system

Probing Ground-state Single-electron Self-exchange Across A Molecule-metal Interface

We have probed single-molecule redox reaction dynamics of hemin (chloride) adsorbed on Ag nanoparticle surfaces by single-molecule surface-enhanced Raman spectroscopy (SMSERS) combined with spectroelectrochemistry. Redox reaction at the molecule/Ag interface is identified and probed by the prominent fluctuations of the Raman frequency of a specific vibrational mode, nu(4), which is a typical marker of the redox state of the iron center in a hemin molecule. On the basis of the autocorrelation and cross-correlation analysis of the single-molecule Raman spectral trajectories and the control measurements of single-molecule spectroelectochemistry and electrochemical STM, we suggest that the single-molecule redox reaction dynamics at the hemin Ag interface is primarily driven by thermal fluctuations. The spontaneous fluctuation dynamics of the single-molecule redox reaction is measured under no external electric potential across the molecule metal interfaces, which provides a novel and unique approach to characterize the interfacial electron transfer at the molecule metal interfaces. Our demonstrated approaches are powerful for obtaining molecular coupling and dynamics involved in interfacial electron transfer processes. The new information obtained is critical for a further understanding, design, and manipulation of the charge transfer processes at the molecule metal interface or metal-molecule-metal junctions, which are fundamental elements in single-molecule electronics, catalysis, and solar energy conversion

Revealing Linear Aggregates Of Light Harvesting Antenna Proteins In Photosynthetic Membranes

How light energy is harvested in a natural photosynthetic membrane through energy transfer is closely related to the stoichiometry and arrangement of light harvesting antenna proteins in the membrane. The specific photosynthetic architecture facilitates a rapid and efficient energy transfer among the light harvesting proteins (LH2 and LH1) and to the reaction center. Here we report the identification of linear aggregates of light harvesting proteins, LH2, in the photosynthetic membranes under ambient conditions by using atomic force microscopy (AFM) imaging and spectroscopic analysis. Our results suggest that the light harvesting protein, LH2, can exist as linear aggregates of 4 2 proteins in the photosynthetic membranes and that the protein distributions are highly heterogeneous. In the photosynthetic membranes examined in our measurements, the ratio of the aggregated to the nonaggregated LH2 proteins is about 3:1 to 5:1 depending on the intensity of the illumination used during sample incubation and oil the bacterial species. A FM images further identify that the LH2 proteins in the linear aggregates are monotonically tilted at an angle 4 +/- 2 degrees from the plane of the photosynthetic membranes. The aggregates result in red-shifted absorption and emission spectra that are measured using various mutant membranes, including an LH2 knockout, LH1 knockout, and LH2 at different population densities. Measuring the fluorescence lifetimes of purified LH2 and LH2 in membranes, we have observed that the LH2 proteins in membranes exhibit biexponential lifetime decays whereas the purified LH2 proteins gave single exponential lifetime decays. We attribute that the two lifetime components originate from the existence of both aggregated and nonaggregated LH2 proteins in the photosynthetic membranes

Probing Single-molecule Enzyme Active-site Conformational State Intermittent Coherence

The relationship between protein conformational dynamics and enzymatic reactions has been a fundamental focus in modern enzymology. Using single-molecule fluorescence resonance energy transfer (FRET) with a combined statistical data analysis approach, we have identified the intermittently appearing coherence of the enzymatic conformational state from the recorded single-molecule intensity-time trajectories of enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) in catalytic reaction. The coherent conformational state dynamics suggests that the enzymatic catalysis involves a multistep conformational motion along the coordinates of substrate-enzyme complex formation and product releasing, presenting as an extreme dynamic behavior intrinsically related to the time bunching effect that we have reported previously. The coherence frequency, identified by statistical results of the correlation function analysis from single-molecule FRET trajectories, increases with the increasing substrate concentrations. The intermittent coherence in conformational state changes at the enzymatic reaction active site is likely to be common and exist in other conformation regulated enzymatic reactions. Our results of HPPK interaction with substrate support a multiple-conformational state model, being consistent with a complementary conformation selection and induced-fit enzymatic loop-gated conformational change mechanism in substrate-enzyme active complex formation

Post-stroke experiences and health information needs among Chinese elderly ischemic stroke survivors in the internet environment: a qualitative study

BackgroundElderly stroke survivors are encouraged to receive appropriate health information to prevent recurrences. After discharge, older patients seek health information in everyday contexts, examining aspects that facilitate or impair healthy behavior.ObjectivesTo explore the experiences of older stroke patients when searching for health information, focusing on search methods, identification of health information, and difficulties faced during the search process.MethodsUsing the qualitative descriptive methodology, semi-structured interviews were conducted with fifteen participants.ResultsParticipants associated the health information they sought with concerns about future life prospects triggered by perceived intrusive changes in their living conditions. Based on the participants’ descriptions, four themes were refined: participants’ motivation to engage in health information acquisition behavior, basic patterns of health information search, source preferences for health information, and difficulties and obstacles in health information search, and two search motivation subthemes, two search pattern subthemes, four search pathway subthemes, and four search difficulty subthemes were further refined.ConclusionOlder stroke patients face significant challenges in searching for health information online. Healthcare professionals should assess survivors’ health information-seeking skills, develop training programs, provide multichannel online access to health resources, and promote secondary prevention for patients by improving survivors’ health behaviors and self-efficacy

A simple risk stratification model that predicts 1-year postoperative mortality rate in patients with solid-organ cancer

This study aimed to construct a scoring system developed exclusively from the preoperative data that predicts 1-year postoperative mortality in patients with solid cancers. A total of 20,632 patients who had a curative resection for solid-organ cancers between 2007 and 2012 at Chang Gung Memorial Hospital Linkou Medical Center were included in the derivation cohort. Multivariate logistic regression analysis was performed to develop a risk model that predicts 1-year postoperative mortality. Patients were then stratified into four risk groups (low-, intermediate-, high-, and very high-risk) according to the total score (0–43) form mortality risk analysis. An independent cohort of 16,656 patients who underwent curative cancer surgeries at three other hospitals during the same study period (validation cohort) was enrolled to verify the risk model. Age, gender, cancer site, history of previous cancer, tumor stage, Charlson comorbidity index, American Society of Anesthesiologist score, admission type, and Eastern Cooperative Oncology Group performance status were independently predictive of 1-year postoperative mortality. The 1-year postoperative mortality rates were 0.5%, 3.8%, 14.6%, and 33.8%, respectively, among the four risk groups in the derivation cohort (c-statistic, 0.80), compared with 0.9%, 4.2%, 14.6%, and 32.6%, respectively, in the validation cohort (c-statistic, 0.78). The risk stratification model also demonstrated good discrimination of long-term survival outcome of the four-tier risk groups (P < 0.01 for both cohorts). The risk stratification model not only predicts 1-year postoperative mortality but also differentiates long-term survival outcome between the risk groups

Hybrid acoustic metamaterial as super absorber for broadband low-frequency sound

A hybrid acoustic metamaterial is proposed as a new class of sound absorber, which exhibits superior broadband low-frequency sound absorption as well as excellent mechanical stiffness/strength. Based on the honeycomb-corrugation hybrid core (H-C hybrid core), we introduce perforations on both top facesheet and corrugation, forming perforated honeycomb-corrugation hybrid (PHCH) to gain super broadband low-frequency sound absorption. Applying the theory of micro-perforated panel (MPP), we establish a theoretical method to calculate the sound absorption coefficient of this new kind of metamaterial. Perfect sound absorption is found at just a few hundreds hertz with two-octave 0.5 absorption bandwidth. To verify this model, a finite element model is developed to calculate the absorption coefficient and analyze the viscous-thermal energy dissipation. It is found that viscous energy dissipation at perforation regions dominates the total energy consumed. This new kind of acoustic metamaterials show promising engineering applications, which can serve as multiple functional materials with extraordinary low-frequency sound absorption, excellent stiffness/strength and impact energy absorption