Invited - Droplets driving and sensing pixel circuits for thin film transistor-based digital microfluidics

Thin film transistor-based active-matrix digital microfluidics (AM-DMF) is an emerging and promising technology for large-scale parallel biological sample handling. With electrowetting-on-dielectric (EWOD) method, DMF chip can realize accurately controlling discrete droplets, thus it has great application prospects in biology, chemistry, and drug discovery. With the rapid development of micro-analysis and detection requirements, the precise control of droplets in DMF chips is increasingly required, so it is necessary to conduct the real-time sensing of droplet position. Figure 1 shows the designed droplet position detection unit circuit. The circuit consists of six thin film transistors, T1-T6. The input signals mainly include the enable signal Ven, the reverse enable signal Venb, the discharge signal Vdischarge, the detection signal Vdetect, and the ground signal Vgnd. The signal Vdrive is the driving voltage applied for driving electrode. Cpixel is the equivalent capacitance between the two plates of a pixel electrode in a microfluidic chip. Vout is the output voltage signal. Please click Download on the upper right corner to see the full abstract

Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine

Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.This research was funded by Project of Natural Science Foundation of Gansu Province (20JR5RA363); Project of Gansu Provincial Education Department (2020B-003)

Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms

We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and stochastic differential equations (SDEs) on GPUs. The method is integrated with a widely used differential equation solver library in a high-level language (Julia's DifferentialEquations.jl) and enables GPU acceleration without requiring code changes by the user. Our approach achieves state-of-the-art performance compared to hand-optimized CUDA-C++ kernels, while performing $20-100\times$ faster than the vectorized-map (\texttt{vmap}) approach implemented in JAX and PyTorch. Performance evaluation on NVIDIA, AMD, Intel, and Apple GPUs demonstrates performance portability and vendor-agnosticism. We show composability with MPI to enable distributed multi-GPU workflows. The implemented solvers are fully featured, supporting event handling, automatic differentiation, and incorporating of datasets via the GPU's texture memory, allowing scientists to take advantage of GPU acceleration on all major current architectures without changing their model code and without loss of performance.Comment: 11 figure

Numerical investigation of GHz repetition rate fundamentally mode-locked all-fiber lasers

GHz repetition rate fundamentally mode-locked lasers have attracted great interest for a variety of scientific and practical applications. A passively mode-locked laser in all-fiber format has the advantages of high stability, maintenance-free operation, super compactness, and reliability. In this paper, we present numerical investigation on passive mode-locking of all-fiber lasers operating at repetition rates of 1-20 GHz. Our calculations show that the reflectivity of the output coupler, the small signal gain of the doped fiber, the total net cavity dispersion, and the modulation depth of the saturable absorber are the key parameters for producing stable fundamentally mode-locked pulses at GHz repetition rates in very short all-fiber linear cavities. The instabilities of GHz repetition rate fundamentally mode-locked all-fiber lasers with different parameters were calculated and analyzed. Compared to a regular MHz repetition rate mode-locked all-fiber laser, the pump power range for the mode-locking of a GHz repetition rate all-fiber laser is much larger due to the several orders of magnitude lower accumulated nonlinearity in the fiber cavity The presented numerical study provides valuable guidance for the design and development of highly stable mode-locked all-fiber lasers operating at GHz repetition rates.National Science Foundation Engineering Research Center for Integrated Access Networks [EEC-0812072]; Technology Research Initiative Fund (TRIF) Photonics Initiative of the University of Arizona; National Natural Science Foundation of China (NSFC) [61575075]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics