16 research outputs found
UNCERTAINTY IN DIRECTIONAL REPRESENTATIONS, PREIMAGES OF KERNEL TRANSFORMATIONS AND APPLICATIONS
No full textThis dissertation develops theoretical and computational advances in discrete directional time–frequency analysis, phase retrieval, kernel‑based inverse problems, and applications to electron microscopy denoising. Chapter 3 presents sharp uncertainty inequalities for the Directional Gabor Ridge Transform (DGRT) and its weighted variant (DWGRT) within a discrete‐frame framework, yielding explicit bounds on spatial support and directional frequency localization. These results extend classical continuous uncertainty principles to fully discrete directional frames and provide explicit guidance on window lengths, orientation sampling, and weight functions.
Chapter 4 formulates undersampled short‑time Fourier magnitude inversion as a supervised learning problem. I design a compact neural network trained with adversarial and reconstruction losses that reconstructs eight‑thousand‑sample audio segments from four‑thousand magnitude measurements. Extensive experiments demonstrate rapid convergence and superior numerical and perceptual quality compared to Griffin–Lim, including downstream classification accuracy improvements.
In Chapter 5, I implement and compare three deterministic kPCA pre‑image algorithms: fixed‑point iteration, kernel ridge regression, and Schölkopf’s method, applying them across MNIST, CIFAR‑10, and SVHN under noise‑free and noisy scenarios. Metrics of PSNR, SSIM, and PCC identify each solver’s strengths and limitations. Motivated by these findings, I introduce DCGAN‑KPCAnet and WGAN‑KPCAnet, two generative adversarial inverse solvers that learn the kPCA mapping directly. WGAN‑KPCAnet consistently exceeds the best deterministic solver in reconstruction fidelity, structural preservation, and noise robustness.
Chapter 6 integrates cosine‑similarity kPCA denoising into graphene‑liquid‑cell and single particle cryo‑EM pipelines. By replacing masking and averaging with kPCA inversion, I achieve substantial improvements in two‑dimensional projection quality and enable high‑resolution three‑dimensional reconstructions that reveal dynamic structural states. Kernel parameter selection, computational scalability, and software integration are discussed.
Together, these contributions establish new discrete uncertainty bounds, demonstrate the efficacy of learning‑based phase retrieval, advance kernel pre‑image algorithms through generative modeling, and apply kernel PCA denoising to challenging electron microscopy data, bridging fundamental mathematics with practical signal processing applications
Catalytic Effect of Solvent Vapors on the Spontaneous Formation of Caffeine–Malonic Acid Cocrystal
No full textCaffeine (a model
pharmaceutical mimic) and malonic acid (a common
excipient partner) are known to form a molecular cocrystal spontaneously
over about 1 week when their powders are mixed at ambient conditions.
We report the dramatic catalytic acceleration of this reaction when
the mixture of powders is exposed to vapors of common laboratory solvents.
Acetone and methanol vapors show rate enhancements over 1000-fold,
effecting quantitative conversion in less than 5 min. The reaction
progress was tracked ex situ by powder X-ray diffraction, and products
were verified by 13C solid-state NMR. Our data show no
evidence of an intermediate phase. Gravimetric experiments show that
solvent vapor uptake is not stoichiometric and is reversible. This
rare example of gas phase catalysis of supramolecular transformations
has important implications for a deeper mechanistic understanding
of diffusion-controlled solid–solid reactions
Author Correction: Boosting hydrogel conductivity via water-dispersible conducting polymers for injectable bioelectronics
No full textData_Sheet_4_Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic.PDF
No full textObjectivesTo evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.MethodsOne hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.ResultsOn environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.ConclusionWe described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.</p
Data_Sheet_6_Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic.PDF
No full textObjectivesTo evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.MethodsOne hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.ResultsOn environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.ConclusionWe described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.</p
Data_Sheet_2_Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic.PDF
No full textObjectivesTo evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.MethodsOne hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.ResultsOn environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.ConclusionWe described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.</p
Image_2_Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic.JPEG
No full textObjectivesTo evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.MethodsOne hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.ResultsOn environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.ConclusionWe described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.</p
Image_1_Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic.JPEG
No full textObjectivesTo evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.MethodsOne hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.ResultsOn environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.ConclusionWe described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.</p
Data_Sheet_5_Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic.PDF
No full textObjectivesTo evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.MethodsOne hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.ResultsOn environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.ConclusionWe described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.</p
Image_3_Investigation of bacterial and fungal population structure on environmental surfaces of three medical institutions during the COVID-19 pandemic.JPEG
No full textObjectivesTo evaluate the population structure of environmental bacteria and fungi in three different types of medical institutions and the potential risks due to antibiotic resistance during the coronavirus disease 2019 (COVID-19) pandemic.MethodsOne hundred twenty-six environmental surface samples were collected from three medical institutions during the COVID-19 pandemic. A total of 6,093 and 13,514 representative sequences of 16S and ITS ribosomal RNA (rRNA) were obtained by amplicon sequencing analysis. The functional prediction was performed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool based on the Greengenes database and the FAPROTAX database.ResultsOn environmental surfaces in three medical institutions during the COVID-19 pandemic, Firmicutes (51.6%) and Bacteroidetes (25%) were the dominant bacteria, while Ascomycota (39.4%) and Basidiomycota (14.2%) were the dominant fungi. A number of potential bacterial and fungal pathogens were successfully identified by the metagenomic approach. Furthermore, compared with the bacterial results, the fungi showed a generally closer Bray Curtis distance between samples. The overall ratio of Gram-negative bacteria to Gram-positive bacteria was about 3:7. The proportion of stress-tolerant bacteria in medical institutions A, B and C reached 88.9, 93.0 and 93.8%, respectively. Anaerobic bacteria accounted for 39.6% in outdoor environments, 77.7% in public areas, 87.9% in inpatient areas and 79.6% in restricted areas. Finally, the β-Lactam resistance pathway and polymyxin resistance pathway were revealed through functional prediction.ConclusionWe described the microbial population structure changes in three different types of medical institutions using the metagenomic approach during the COVID-19 pandemic. We found that the disinfection measures performed by three healthcare facilities may be effective on the “ESKAPE” pathogens, but less effective on fungal pathogens. Moreover, emphasis should be given to the prevention and control of β-lactam and polymyxin antibiotics resistance bacteria during the COVID-19 pandemic.</p
