MSDRP: A Deep Learning Model Based on Multisource Data for Predicting Drug Response

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines. Results: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model

Topological optimization of a variable cross-section cantilever-based piezoelectric wind energy harvester

Wind energy is a typical foreseeable renewable energy source. This study constructs and optimizes a variable cross-section cantilever-based piezoelectric energy harvester for low-speed wind energy harvesting. The Galerkin approach is usually used to discretize the continuum model and then get the ordinary differential equations. However, this method is more suitable for calculating uniformity than the variable cross-sectional beam model. To solve this problem, we proposed an improved piecewise Galerkin approach for discretizing the continuum model with a variable cross section. By modifying the boundary expressions and modal functions between segments, it can improve both computation speed and accuracy. COMSOL simulations demonstrate that natural frequencies calculated via the improved method are more accurate than those of the traditional Galerkin method. The method of multiple scales is applied to determine the output power and critical wind velocity. A distinctive numerical approach is presented for shape optimization by combining the analytical calculation method with the particle swarm optimization (PSO) technique for low-speed wind energy harvesting. Additionally, the logic function is chosen to produce the optimal shape’s fitting expression for engineering applications. With all the improvements, the output power of a variable cross-section beam-based harvester reaches as much as 3.668 times that of a uniform beam model, demonstrating the importance of structural optimization for this type of energy harvesters. Finally, experiments are set up to verify the optimization procedure. Actually, it builds an analytical framework for the adaptive selection of variable-section piezoelectric cantilever wind-induced vibration energy harvesters

Electron-positron pair creation induced by quantum-mechanical tunneling

We study the creation of electron-positron pairs from the vacuum induced by two spatially displaced static electric fields. The strength and spatial width of each localized field is less than required for pair creation. If, however, the separation between the fields is less than the quantum-mechanical tunneling length associated with the corresponding quantum scattering system, the system produces a steady flux of electron-positron pairs. We compute the time dependence of the pair-creation probability by solving the Dirac equation numerically for various external field sequences. For the special case of two very narrow fields we provide an analytical expression for the pair-creation rate in the long-time limit

Chaos Suppression of an Electrically Actuated Microresonator Based on Fractional-Order Nonsingular Fast Terminal Sliding Mode Control

This paper focuses on chaos suppression strategy of a microresonator actuated by two symmetrical electrodes. Dynamic behavior of this system under the case where the origin is the only stable equilibrium is investigated first. Numerical simulations reveal that system may exhibit chaotic motion under certain excitation conditions. Then, bifurcation diagrams versus amplitude or frequency of AC excitation are drawn to grasp system dynamics nearby its natural frequency. Results show that the vibration is complex and may exhibit period-doubling bifurcation, chaotic motion, or dynamic pull-in instability. For the suppression of chaos, a novel control algorithm, based on an integer-order nonsingular fast terminal sliding mode and a fractional-order switching law, is proposed. Fractional Lyapunov Stability Theorem is used to guarantee the asymptotic stability of the system. Finally, numerical results with both fractional-order and integer-order control laws show that our proposed control law is effective in controlling chaos with system uncertainties and external disturbances

Detection of intergenic non-coding RNAs expressed in the main developmental stages in Drosophila melanogaster

How many intergenically encoded non-coding RNAs (ncRNAs) are expressed during various developmental stages in Drosophila? Previous analyses in one or a few developmental stages indicated abundant expression of intergenic ncRNAs. However, some reported that ncRNAs have been recently falsified, and, in general, the false positive rate for ncRNA detection is unknown. In this report, we used reverse transcription-PCR (RT-PCR), a more robust method, to detect ncRNAs from the intergenic regions that are expressed in four major developmental stages (6–8 h embryo, 20–22 h embryo, larvae and adult). We tested 1027 regions, ∼10% of all intergenic regions, and detected transcription by RT–PCR. The results from 18 342 RT–PCR experiments revealed evidence for transcription in 72.7% of intergenic regions in the developmental process. The early developmental stage appears to be associated with more abundant ncRNAs than later developmental stages. In the early stage, we detected 43.6% of intergenic regions that encode transcripts in the triplicate RT–PCR experiments, yielding an estimate of 5006 intergenic regions in the entire genome likely encoding ncRNAs. We compared the RT–PCR-related approach with previous tiling array-based approach and observed that the latter method is insensitive to short ncRNAs, especially the molecules less than 120 bp. We measured false positive rates for the analyzed genomic approaches including the RT–PCR and tiling array method

Determinants of never-treated status in rural versus urban contexts for individuals with schizophrenia in a population-based study in China

Background A goal of China’s 2012 National Mental Health Law is to improve access to services and decrease urban versus rural disparities in services. However, pre-reform data is needed for objective evaluation of these reforms’ effectiveness. Accordingly, this study compares the pre-reform utilization of medical services for the treatment of schizophrenia in rural and urban communities in China. Methods In a large community-based study in four provinces representing 12% of China’s population conducted from 2001 to 2005, we identified 326 individuals with schizophrenia (78 never treated). Comparing those living in urban (n = 86) versus rural (n = 240) contexts, we used adjusted Poisson regression models to assess the relationship of ‘never treated’ status with family-level factors (marital status, family income, and number of co-resident family members) and illness severity factors (age of onset, symptom severity and functional impairment). Results Despite similar impairments due to symptoms, rural patients were less likely to have received intensive mental health services (i.e., use psychiatric inpatient services), and appeared more likely to be ‘never treated’ or to only have received outpatient care. Among rural patients, only having more than four co-resident family members was independently associated with ‘never-treated’ status (RR = 0.34; 95% CI, 0.12–0.94; p = 0.039). Among urban patients, only older age of onset was independently associated with ‘never-treated’ status (RR = 1.06; 95% CI 1.02–1.10, p = 0.003). Conclusions Identifying differential drivers of service utilization in urban and rural communities is needed before implementing policies to improve the utilization and equity of services and to define metrics of program success

Dynamic modeling and structural optimization of a bistable electromagnetic vibration energy harvester

A novel bistable electromagnetic vibration energy harvester (BEMH) is constructed and optimized in this study, based on a nonlinear system consisting mainly of a flexible membrane and a magnetic spring. A large-amplitude transverse vibration equation of the system is established with the general nonlinear geometry and magnetic force. Firstly, the mathematical model, considering the higher-order nonlinearities given by nonlinear Galerkin method, is applied to a membrane with a co-axial magnet mass and magnetic spring. Secondly, the steady vibration response of the membrane subjected to a harmonic base motion is obtained, and then the output power considering electromagnetic effect is analytically derived. On this basis, a parametric study in a broad frequency domain has been achieved for the BEMH with different radius ratios and membrane thicknesses. It is demonstrated that model predictions are both in close agreement with results from the finite element simulation and experiment data. Finally, the proposed efficient solution method is used to obtain an optimizing strategy for the design of multi-stable energy harvesters with the similar flexible structure