Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches. Code will be available at \url{https://github.com/kkkls/FFTformer}.Comment: Code will be available at \url{https://github.com/kkkls/FFTformer

Functional TCR T cell screening using single-cell droplet microfluidics

Adoptive T cell transfer, in particular TCR T cell therapy, holds great promise for cancer immunotherapy with encouraging clinical results. However, finding the right TCR T cell clone is a tedious, time-consuming, and costly process. Thus, there is a critical need for single cell technologies to conduct fast and multiplexed functional analyses followed by recovery of the clone of interest. Here, we use droplet microfluidics for functional screening and real-time monitoring of single TCR T cell activation upon recognition of target tumor cells. Notably, our platform includes a tracking system for each clone as well as a sorting procedure with 100% specificity validated by downstream single cell reverse-transcription PCR and sequencing of TCR chains. Our TCR screening prototype will facilitate immunotherapeutic screening and development of T cell therapies

Functional TCR T cell screening using single-cell droplet microfluidics

Adoptive T cell transfer, in particular TCR T cell therapy, holds great promise for cancer immunotherapy with encouraging clinical results. However, finding the right TCR T cell clone is a tedious, time-consuming, and costly process. Thus, there is a critical need for single cell technologies to conduct fast and multiplexed functional analyses followed by recovery of the clone of interest. Here, we use droplet microfluidics for functional screening and real-time monitoring of single TCR T cell activation upon recognition of target tumor cells. Notably, our platform includes a tracking system for each clone as well as a sorting procedure with 100% specificity validated by downstream single cell reverse-transcription PCR and sequencing of TCR chains. Our TCR screening prototype will facilitate immunotherapeutic screening and development of T cell therapies

Rapid bacterial detection and antibiotic susceptibility testing in whole blood using one-step, high throughput blood digital PCR.

Sepsis due to antimicrobial resistant pathogens is a major health problem worldwide. The inability to rapidly detect and thus treat bacteria with appropriate agents in the early stages of infections leads to excess morbidity, mortality, and healthcare costs. Here we report a rapid diagnostic platform that integrates a novel one-step blood droplet digital PCR assay and a high throughput 3D particle counter system with potential to perform bacterial identification and antibiotic susceptibility profiling directly from whole blood specimens, without requiring culture and sample processing steps. Using CTX-M-9 family ESBLs as a model system, we demonstrated that our technology can simultaneously achieve unprecedented high sensitivity (10 CFU per ml) and rapid sample-to-answer assay time (one hour). In head-to-head studies, by contrast, real time PCR and BioRad ddPCR only exhibited a limit of detection of 1000 CFU per ml and 50-100 CFU per ml, respectively. In a blinded test inoculating clinical isolates into whole blood, we demonstrated 100% sensitivity and specificity in identifying pathogens carrying a particular resistance gene. We further demonstrated that our technology can be broadly applicable for targeted detection of a wide range of antibiotic resistant genes found in both Gram-positive (vanA, nuc, and mecA) and Gram-negative bacteria, including ESBLs (blaCTX-M-1 and blaCTX-M-2 families) and CREs (blaOXA-48 and blaKPC), as well as bacterial speciation (E. coli and Klebsiella spp.) and pan-bacterial detection, without requiring blood culture or sample processing. Our rapid diagnostic technology holds great potential in directing early, appropriate therapy and improved antibiotic stewardship in combating bloodstream infections and antibiotic resistance

Rapid bacterial detection and antibiotic susceptibility testing in whole blood using one-step, high throughput blood digital PCR.

Sepsis due to antimicrobial resistant pathogens is a major health problem worldwide. The inability to rapidly detect and thus treat bacteria with appropriate agents in the early stages of infections leads to excess morbidity, mortality, and healthcare costs. Here we report a rapid diagnostic platform that integrates a novel one-step blood droplet digital PCR assay and a high throughput 3D particle counter system with potential to perform bacterial identification and antibiotic susceptibility profiling directly from whole blood specimens, without requiring culture and sample processing steps. Using CTX-M-9 family ESBLs as a model system, we demonstrated that our technology can simultaneously achieve unprecedented high sensitivity (10 CFU per ml) and rapid sample-to-answer assay time (one hour). In head-to-head studies, by contrast, real time PCR and BioRad ddPCR only exhibited a limit of detection of 1000 CFU per ml and 50-100 CFU per ml, respectively. In a blinded test inoculating clinical isolates into whole blood, we demonstrated 100% sensitivity and specificity in identifying pathogens carrying a particular resistance gene. We further demonstrated that our technology can be broadly applicable for targeted detection of a wide range of antibiotic resistant genes found in both Gram-positive (vanA, nuc, and mecA) and Gram-negative bacteria, including ESBLs (blaCTX-M-1 and blaCTX-M-2 families) and CREs (blaOXA-48 and blaKPC), as well as bacterial speciation (E. coli and Klebsiella spp.) and pan-bacterial detection, without requiring blood culture or sample processing. Our rapid diagnostic technology holds great potential in directing early, appropriate therapy and improved antibiotic stewardship in combating bloodstream infections and antibiotic resistance

Rapid bacterial detection and antibiotic susceptibility testing in whole blood using one-step, high throughput blood digital PCR.

Sepsis due to antimicrobial resistant pathogens is a major health problem worldwide. The inability to rapidly detect and thus treat bacteria with appropriate agents in the early stages of infections leads to excess morbidity, mortality, and healthcare costs. Here we report a rapid diagnostic platform that integrates a novel one-step blood droplet digital PCR assay and a high throughput 3D particle counter system with potential to perform bacterial identification and antibiotic susceptibility profiling directly from whole blood specimens, without requiring culture and sample processing steps. Using CTX-M-9 family ESBLs as a model system, we demonstrated that our technology can simultaneously achieve unprecedented high sensitivity (10 CFU per ml) and rapid sample-to-answer assay time (one hour). In head-to-head studies, by contrast, real time PCR and BioRad ddPCR only exhibited a limit of detection of 1000 CFU per ml and 50-100 CFU per ml, respectively. In a blinded test inoculating clinical isolates into whole blood, we demonstrated 100% sensitivity and specificity in identifying pathogens carrying a particular resistance gene. We further demonstrated that our technology can be broadly applicable for targeted detection of a wide range of antibiotic resistant genes found in both Gram-positive (vanA, nuc, and mecA) and Gram-negative bacteria, including ESBLs (blaCTX-M-1 and blaCTX-M-2 families) and CREs (blaOXA-48 and blaKPC), as well as bacterial speciation (E. coli and Klebsiella spp.) and pan-bacterial detection, without requiring blood culture or sample processing. Our rapid diagnostic technology holds great potential in directing early, appropriate therapy and improved antibiotic stewardship in combating bloodstream infections and antibiotic resistance

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.Comment: Validation code of the baseline model is available at https://github.com/ofsoundof/IMDN. Validation of all submitted models is available at https://github.com/ofsoundof/NTIRE2022_ES