Active PSF shaping and adaptive optics enable volumetric localization microscopy through brain sections

Application of single-molecule switching nanoscopy (SMSN) beyond the coverslip surface poses substantial challenges due to sample-induced aberrations that distort and blur single-molecule emission patterns. We combined active shaping of point spread functions and efficient adaptive optics to enable robust 3D-SMSN imaging within tissues. This development allowed us to image through 30-μm-thick brain sections to visualize and reconstruct the morphology and the nanoscale details of amyloid-β filaments in a mouse model of Alzheimer's disease

Artificial intelligence in steam cracking modeling : a deep learning algorithm for detailed effluent prediction

Chemical processes can benefit tremendously from fast and accurate effluent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these fields, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning artificial neural networks (DL ANNs) has been developed for the largest chemicals production process-steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker effluent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed characterization of a naphtha is predicted from three points on the boiling curve and paraffins, iso-paraffins, olefins, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the effluent prediction is 0.1 wt%. When combining all networks-using the output of the previous as input to the next-the effluent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major benefit is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented framework highly suitable for the continuous monitoring of difficult-to-access process parameters and for the envisioned, high-frequency real-time optimization (RTO) strategy or process control. Nevertheless, the lack of a fundamental basis implies that fundamental understanding is almost completely lost, which is not always well-accepted by the engineering community. In addition, the performance of the developed networks drops significantly for naphthas that are highly dissimilar to those in the training set. (C) 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company

Combining expansion microscopy with other super-resolution techniques

Expansion microscopy (ExM) was introduced since 2015 and has been fast developed since then. This technique, via physical enlargement of fluorescence carried biological samples, can resolve structures of tens of nanometers with conventional microscopes. Here I discussed the current methods in ExM and influences of different fixation, protease digestion and labelling methods used in ExM. Validation of ExM was also carried out in the work using image registration of microtubule cytoskeletons and the 190 nm periodic structures of β-spectrin ring structures in neurons. Next, the combination of ExM with other super-resolution techniques, e.g. stimulated emission depletion (STED) microscopy was proposed. The centrosome protein CEP152, the primary cilium and microtubule were resolved using expansion STED (ExSTED) microscopy. With the optimized ExSTED microscopy, a sub-10 nm 2D and a sub-50 nm 3D resolution was achieved. Structured illumination microscopy (SIM) was also attempted to image the expanded hydrogels, but severe artifacts were observed. Finally, a tri-functional fluorescent probe was proposed, where a fluorescent dye was linked with a benzyl-guanine and an acrylic acid group. The probe was used to stain a SNAP-tagged nuclear pore protein in cells and used to crosslink proteins to acrylamide-based hydrogel in ExM

Langley aerospace test highlights, 1985

The role of the Langley Research Center is to perform basic and applied research necessary for the advancement of aeronautics and space flight, to generate new and advanced concepts for the accomplishment of related national goals, and to provide research advice, technological support, and assistance to other NASA installations, other government agencies, and industry. Significant tests which were performed during calendar year 1985 in Langley test facilities, are highlighted. Both the broad range of the research and technology activities at the Langley Research Center and the contributions of this work toward maintaining United States leadership in aeronautics and space research, are illustrated. Other highlights of Langley research and technology for 1985 are described in Research and Technology-1985 Annual Report of the Langley Research Center

Best Prompts for Text-to-Image Models and How to Find Them

Recent progress in generative models, especially in text-guided diffusion models, has enabled the production of aesthetically-pleasing imagery resembling the works of professional human artists. However, one has to carefully compose the textual description, called the prompt, and augment it with a set of clarifying keywords. Since aesthetics are challenging to evaluate computationally, human feedback is needed to determine the optimal prompt formulation and keyword combination. In this paper, we present a human-in-the-loop approach to learning the most useful combination of prompt keywords using a genetic algorithm. We also show how such an approach can improve the aesthetic appeal of images depicting the same descriptions.Comment: 13 pages (6 main pages), 7 figures, 4 tables, accepted at SIGIR '23 Short Paper Trac

Ultrafast Microfluidic Immunoassays Towards Real-time Intervention of Cytokine Storms

Biomarker-guided precision medicine holds great promise to provide personalized therapy with a good understanding of the molecular or cellular data of an individual patient. However, implementing this approach in critical care uniquely faces enormous challenges as it requires obtaining “real-time” data with high sensitivity, reliability, and multiplex capacity near the patient’s bedside in the quickly evolving illness. Current immunodiagnostic platforms generally compromise assay sensitivity and specificity for speed or face significantly increased complexity and cost for highly multiplexed detection with low sample volume. This thesis introduces two novel ultrafast immunoassay platforms: one is a machine learning-based digital molecular counting assay, and the other is a label-free nano-plasmonic sensor integrated with an electrokinetic mixer. Both of them incorporate microfluidic approaches to pave the way for near-real-time interventions of cytokine storms. In the first part of the thesis, we present an innovative concept and the theoretical study that enables ultrafast measurement of multiple protein biomarkers (<1 min assay incubation) with comparable sensitivity to the gold standard ELISA method. The approach, which we term “pre-equilibrium digital enzyme-linked immunosorbent assay” (PEdELISA) incorporates the single-molecular counting of proteins at the early, pre-equilibrium state to achieve the combination of high speed and sensitivity. We experimentally demonstrated the assay’s application in near-real-time monitoring of patients receiving chimeric antigen receptor (CAR) T-cell therapy and for longitudinal serum cytokine measurements in a mouse sepsis model. In the second part, we report the further development of a machine learning-based PEdELISA microarray data analysis approach with a significantly extended multiplex capacity using the spatial-spectral microfluidic encoding technique. This unique approach, together with a convolutional neural network-based image analysis algorithm, remarkably reduced errors faced by the highly multiplexed digital immunoassay at low analyte concentrations. As a result, we demonstrated the longitudinal data collection of 14 serum cytokines in human patients receiving CAR-T cell therapy at concentrations < 10pg/mL with a sample volume < 10 µL and 5-min assay incubation. In the third part, we demonstrate the clinical application of a machine learning-based digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing module that can be operated inside a biosafety cabinet to minimize the exposure of technician to the virus infection and (ii) a compact fluorescence optical scanner for the potential near-bedside readout. The automated system has achieved high interassay precision (~10% CV) with high sensitivity (<0.4pg/mL). Our data revealed large subject-to-subject variability in patient responses to anti-inflammatory treatment for COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays. Lastly, an AC electroosmosis-enhanced localized surface plasmon resonance (ACE-LSPR) biosensing device was presented for rapid analysis of cytokine IL-1β among sepsis patients. The ACE-LSPR device is constructed using both bottom-up and top-down sensor fabrication methods, allowing the seamless integration of antibody-conjugated gold nanorod (AuNR) biosensor arrays with microelectrodes on the same microfluidic platform. Applying an AC voltage to microelectrodes while scanning the scattering light intensity variation of the AuNR biosensors results in significantly enhanced biosensing performance. The technologies developed have enabled new capabilities with broad application to advance precision medicine of life-threatening acute illnesses in critical care, which potentially will allow the clinical team to make individualized treatment decisions based on a set of time-resolved biomarker signatures.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163129/1/yujing_1.pd

2013 Annual Research Symposium Abstract Book

2013 annual volume of abstracts for science research projects conducted by students at Trinity College

Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization

Now a day, health information management and utilization is the demanding task to health informaticians for delivering the eminence healthcare services. Extracting the similar cases from the case database can aid the doctors to recognize the same kind of patients and their treatment details. Accordingly, this paper introduces the method called H-BCF for retrieving the similar cases from the case database. Initially, the patient’s case database is constructed with details of different patients and their treatment details. If the new patient comes for treatment, then the doctor collects the information about that patient and sends the query to the H-BCF. The H-BCF system matches the input query with the patient’s case database and retrieves the similar cases. Here, the PSO algorithm is used with the BCF for retrieving the most similar cases from the patient’s case database. Finally, the Doctor gives treatment to the new patient based on the retrieved cases. The performance of the proposed method is analyzed with the existing methods, such as PESM, FBSO-neural network, and Hybrid model for the performance measures accuracy and F-Measure. The experimental results show that the proposed method attains the higher accuracy of 99.5% and the maximum F-Measure of 99% when compared to the existing methods