Certain bacteria synthesize glutathionylspermidine (Gsp), from GSH and spermidine. Escherichia coli Gsp synthetase/amidase (GspSA) catalyzes both the synthesis and hydrolysis of Gsp. Prior to the work reported herein, the physiological role(s) of Gsp or how the two opposing GspSA activities are regulated had not been elucidated. We report that Gsp-modified proteins from E. coli contain mixed disulfides of Gsp and protein thiols, representing a new type of post-translational modification formerly undocumented. The level of these proteins is increased by oxidative stress. We attribute the accumulation of such proteins to the selective inactivation of GspSA amidase activity. X-ray crystallography and a chemical modification study indicated that the catalytic cysteine thiol of the GspSA amidase domain is transiently inactivated by H 2 O 2 oxidation to sulfenic acid, which is stabilized by a very short hydrogen bond with a water molecule. We propose a set of reactions that explains how the levels of Gsp and Gsp S-thiolated proteins are modulated in response to oxidative stress. The hypersensitivities of GspSA and GspSA/ glutaredoxin null mutants to H 2 O 2 support the idea that GspSA and glutaredoxin act synergistically to regulate the redox environment of E. coli.Protein thiols are readily oxidized and reduced to form sulfenates, sulfinates, sulfonates, and intra-and intermolecular disulfides. Most organisms have complex systems that regulate the intracellular redox states of thiols (1, 2). Small thiol-containing biomolecules (e.g. GSH and cysteine, form mixed-disulfides with protein thiols (S-thiolation). These post-translational modifications protect proteins from overoxidation and regulate certain protein functions (3, 4). For example, S-glutathionylation of Escherichia coli methionine synthase, which occurs when E. coli is oxidatively stressed, suppresses the synthase activity, thereby decreasing cellular methionine concentration (5). Because GSH is abundant in most organisms (often existing at 1-10 mM), protein S-glutathionylation (GSH S-thiolation) is considered to be a reversible and universal cellular process. In E. coli, GSH and spermidine (Spd) 4 form N 1

In Escherichia coli, RcsA, a positive activator for transcription of cps (capsular polysaccharide synthesis) genes, is degraded by the Lon protease. In lon mutant, the accumulation of RcsA leads to overexpression of capsular polysaccharide. In a previous study, overproduction of ClpYQ(HslUV) protease represses the expression of cpsB∷lacZ, but there has been no direct observation demonstrating that ClpYQ degrades RcsA. By means of a MBP-RcsA fusion protein, we showed that RcsA activated cpsB∷lacZ expression and could be rapidly degraded by Lon protease in SG22622 (lon(+)). Subsequently, the comparative half-life experiments performed in the bacterial strains SG22623 (lon) and AC3112 (lon clpY clpQ) indicated that the RcsA turnover rate in AC3112 was relatively slow and RcsA was stable at 30°C or 41°C. In addition, ClpY could interact with RscA in an in vitro pull-down assay, and the more rapid degradation of RcsA was observed in the presence of ClpYQ protease at 41°C. Thus, we conclude that RcsA is indeed proteolized by ClpYQ protease.

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Image recognition and neuroimaging are increasingly being used to understand the progression of Alzheimer’s disease (AD). However, image data from single-photon emission computed tomography (SPECT) are limited. Medical image analysis requires large, labeled training datasets. Therefore, studies have focused on overcoming this problem. In this study, the detection performance of five convolutional neural network (CNN) models (MobileNet V2 and NASNetMobile (lightweight models); VGG16, Inception V3, and ResNet (heavier weight models)) on medical images was compared to establish a classification model for epidemiological research. Brain scan image data were collected from 99 subjects, and 4711 images were used. Demographic data were compared using the chi-squared test and one-way analysis of variance with Bonferroni’s post hoc test. Accuracy and loss functions were used to evaluate the performance of CNN models. The cognitive abilities screening instrument and mini mental state exam scores of subjects with a clinical dementia rating (CDR) of 2 were considerably lower than those of subjects with a CDR of 1 or 0.5. This study analyzed the classification performance of various CNN models for medical images and proved the effectiveness of transfer learning in identifying the mild cognitive impairment, mild AD, and moderate AD scoring based on SPECT images.

BACKGROUND Image-based recognition has become a long-term topic in the field of artificial intelligence, and neuroimaging has gradually become a beneficial way to understand the course of Alzheimer’s disease (AD). OBJECTIVE The goal of this study is to compare the detection performance of convolutional neural networks (CNNs) on medical images to establish a classification model for epidemiological research. However, medical image analysis lacks large labeled training datasets, and thus many transfer learning-based methods have been proposed to solve few labels in the medical field. METHODS Owing to the scarcity of image data from single-photon emission computed tomography (SPECT), this study uses transfer learning to compare the performance of diagnostic methods based on five different CNNs (two lightweight and three deeper-weight CNN models) to determine the most suitable model. Brain scan image data were collected from 36 male and 63 female subjects. This study used 4711 images as the input data for the model. RESULTS The Cognitive Abilities Screening Instrument and Mini Mental State Exam scores of subjects with Clinical Dementia Rating (CDR) of 2 were significantly lower than those of subjects with CDR of 1 and 0.5. These results indicate that the ResNet model (the deeper-weight CNN model) exhibits the highest accuracy (70.79%) and can hence be used to improve the classification of mild cognitive impairment (MCI), mild AD, and moderate AD (CDRs of 0.5, 1, and 2, respectively). CONCLUSIONS This study successfully analyzes the classification performance of different CNN architectures for medical images and also proves the effectiveness of transfer learning in identifying the MCI, mild AD, and moderate AD scoring based on SPECT images.