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Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning
Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores' spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.
Journal Article
Digital Twin of Coal Mine Rescue Robot—Research on Intelligence and Visualization
Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak environmental perception capability, which have become critical bottlenecks for field application. As an emerging technology in the mining field, digital twin enables high-precision virtual-real mapping and on-site operation guidance, providing a novel solution to the above problems. To realize autonomous navigation and digital twin visualization of the CMRR, this paper first carries out targeted hardware retrofits on the CMRR platform, upgrades environmental perception, communication transmission and motion control modules, and lays a solid hardware foundation for subsequent algorithm design and system implementation. Aiming at the complex post-disaster underground environment, a digital twin-integrated CMRR system is constructed. For intelligent autonomous navigation, this study investigates a 3D point cloud–based autonomous navigation framework and proposes a slope-fitting method as well as a maximum arrival probability obstacle avoidance method based on Bézier curve trajectories. For environmental visualization, a digital twin interactive interface is built to monitor gas and other environmental parameters in real time, and accurately reconstruct underground roadway structures based on point cloud data. This design not only ensures the robot’s autonomous obstacle avoidance but also helps rescuers grasp underground conditions in advance. Field tests in a simulated post-disaster mine with complex terrain show that the system can stably complete autonomous navigation tasks, maintain stable motion control under dynamic interference, and provide accurate and reliable environmental data for rescue decisions, verifying its feasibility and effectiveness in harsh mine rescue scenarios.
Journal Article
Plant Information Systems, Manufacturing Capabilities, and Plant Performance
Firms have been investing over $5 billion a year in recent years on new information technology and software in their manufacturing plants. In this study, we develop a conceptual model based on the theory of dynamic capabilities to study how manufacturing plants realize improvements in plant performance by leveraging plant information systems to enable implementation of advanced manufacturing capabilities. We develop hypotheses about relationships between information systems, their impact on manufacturing practices, and the overall impact on plant performance. Analysis of survey data from 1,077 U.S. manufacturing plants provides empirical support for the dynamic capabilities model and suggests that manufacturing capabilities mediate the impact of information systems on plant performance. Our results underscore the importance of manufacturing and organizational capabilities in studying the impact of IT on manufacturing plant productivity, and provide a sharper theoretical lens to evaluate their impact.
Journal Article
Accelerator design for 1.3-MW beam power operation of the J-PARC Main Ring
Abstract
The J-PARC Main Ring (MR) has supplied the high-intensity proton beam for the T2K long-baseline neutrino experiment since 2010. The present beam power is 510 kW and the total number of protons on the target reaches $3.64\\times10^{21}$. To observe charge-conjugation and parity-transformation (CP) violation in the lepton sector with high accuracy, more protons need to be delivered to the T2K target. The project to upgrade the beam power to 1.3 MW started as a mid-term plan of the MR. In parallel to preparing a full technical design report, the technical designs of hardware upgrades using new technologies and all accelerator components that are necessary to deliver the 1.3-MW beam power are summarized and consolidated in this short paper. Further, this paper includes beam dynamics studies and simulation results for handling $3.3\\times 10^{14}$ protons per pulse (ppp) without significant beam loss in the ring and transport lines. The Hyper-Kamiokande (HK) project has recently been approved, and construction has started; the MR upgrade and HK project will work together efficiently to study the CP violation.
Journal Article
FILO: Automated FIx-LOcus Identification for Android Framework Compatibility Issues
Keeping up with the fast evolution of mobile operating systems is challenging for developers, who have to frequently adapt their apps to the upgrades and behavioral changes of the underlying API framework. Those changes often break backward compatibility. The consequence is that apps, if not updated, may misbehave and suffer unexpected crashes if executed within an evolved environment. Being able to quickly identify the portion of the app that should be modified to provide compatibility with new API versions can be challenging. To facilitate the debugging activities of problems caused by backward incompatible upgrades of the operating system, this paper presents FILO, a technique that is able to recommend the method that should be modified to implement the fix by analyzing a single failing execution. FILO can also provide additional information and key symptomatic anomalous events that can help developers understand the reason for the failure, therefore facilitating the implementation of the fix. We evaluated FILO against 18 real compatibility problems related to Android upgrades and compared it with Spectrum-Based Localization approaches. Results show that FILO is able to efficiently and effectively identify the fix-locus in the apps.
Journal Article
Role of salicylic acid in regulating ethylene and physiological characteristics for alleviating salinity stress on germination, growth and yield of sweet pepper
During a preliminary study, effects of 0, 20, 40, and 60 mM NaCl salinity were assessed on germination rate in relation to electrolyte leakage (EL) in sweet pepper. Results explored significant rises in ethylene evolution from seeds having more EL. It was, therefore, hypothesized that excessive ethylene biosynthesis in plants due to salinity stress might be a root cause of low crop productivity. As salicylic acid is one of the potent ethylene inhibitors, thus SA was used to combat effects of ethylene produced under salinity stress of 60 mM NaCl on different physiological and morphological characteristics of sweet pepper.
The effect of 0.05, 0.1, 0.2, 0.3, 0.4, 0.5 and 0.6 mM SA was evaluated on seed germination, growth and yield of sweet pepper cv. Yolo wonder at salinity stress on 60 mM NaCl. Seeds were primed with SA concentrations and incubated till 312 h in an incubator to study germination. Same SA concentrations were sprayed on foliage of plants grown in saline soil (60 mM NaCl).
Seeds primed by 0.2 to 0.3 mM SA improved germination rate by 33% due to suppression of ethylene from 3.19 (control) to 2.23-2.70 mg plate
. Electrolyte leakage reduced to 20.8-21.3% in seeds treated by 0.2-0.3 mM SA compared to 39.9% in untreated seeds. Results also explored that seed priming by 0.3 mM improved TSS, SOD and chlorophyll contents from 13.7 to 15.0 mg g
FW, 4.64 to 5.38 activity h
100 mg
and 89 to 102 ug g
compared to untreated seeds, respectively. Results also explore that SA up to 0.2 mM SA applied on plant foliage improved LAI (5-13%), photosynthesis (4-27%), WUE (11-57%), dry weight (5-20%), SOD activity (4-20%) and finally fruit yield (4-20%) compared to untreated plants by ameliorating effect of 60 mM NaCl. Foliar application of SA also caused significant increase in nutrient use efficiency due to significant variations in POD and SOD activities.
Salicylic acid suppressed ethylene evolution from germinating seeds up to 30% under stress of 60 mM NaCl due to elevated levels of TSS and SOD activity. Foliar application of SA upgraded SOD by lowering POD activity to improve NUE particularly K use efficiency at salinity stress of 60 mM NaCl. Application of 0.2 and 0.3 mM SA emerged as the most effective concentrations of SA for mitigating 60 mM NaCl stress on different physiological and morphological characteristics of sweet pepper.
Journal Article
A distributed in-memory key-value store system on heterogeneous CPU–GPU cluster
In-memory key-value stores play a critical role in many data-intensive applications to provide high-throughput and low latency data accesses. In-memory key-value stores have several unique properties that include (1) data-intensive operations demanding high memory bandwidth for fast data accesses, (2) high data parallelism and simple computing operations demanding many slim parallel computing units, and (3) a large working set. However, our experiments show that homogeneous multicore CPU systems are increasingly mismatched to the special properties of key-value stores because they do not provide massive data parallelism and high memory bandwidth; the powerful but the limited number of computing cores does not satisfy the demand of the unique data processing task; and the cache hierarchy may not well benefit to the large working set. In this paper, we present the design and implementation of Mega-KV, a distributed in-memory key-value store system on a heterogeneous CPU–GPU cluster. Effectively utilizing the high memory bandwidth and latency hiding capability of GPUs, Mega-KV provides fast data accesses and significantly boosts overall performance and energy efficiency over the homogeneous CPU architectures. Mega-KV shows excellent scalability and processes up to 623-million key-value operations per second on a cluster installed with eight CPUs and eight GPUs, while delivering an efficiency of up to 299-thousand operations per Watt (KOPS/W).
Journal Article
A Dynamic Model of Consumer Replacement Cycles in the PC Processor Industry
As high-tech markets mature, replacement purchases inevitably become the dominant proportion of sales. Despite the clear importance of product replacement, little empirical work examines the separate roles of adoption and replacement. A consumer's replacement decision is dynamic and driven by product obsolescence because these markets frequently undergo rapid improvements in quality and falling prices. The goal of this paper is to construct a model of consumer product replacement and to investigate the implications of replacement cycles for firms.
To this end, I develop and estimate a dynamic model of consumer demand that explicitly accounts for the replacement decision when consumers are uncertain about future price and quality. Using a unique data set from the PC processor industry, I show how to combine aggregate data on sales and product ownership to infer replacement behavior. The results reveal substantial variation in replacement behavior over time, and this heterogeneity provides an opportunity for managers to tailor their product introduction and pricing strategies to target the consumers of a particular segment that are most likely to replace in the near future.
Journal Article