MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.

OBJECTIVES: The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI. METHODS: This retrospective study included T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) images from 185 clinical scans: 135 for DNN training, 11 for DNN validation, 20 for qualitative evaluation, and 19 for quantitative evaluation. Additionally, 18 vessel wall imaging (VWI) data were included to evaluate generalization. In each scan of the DNN training set, two noise-independent images were generated from the k-space data, resulting in an input-label pair. 2.5D U-net architecture was utilized for the DNN model. Qualitative evaluation between conventional MP-RAGE and DNN-based MP-RAGE was performed by two radiologists in image quality, fine structure delineation, and lesion conspicuity. Quantitative evaluation was performed with full sampled data as a reference by measuring quantitative error metrics and volumetry at 7 different simulated noise levels. DNN application on VWI was evaluated by two radiologists in image quality. RESULTS: Our DNN-based MP-RAGE outperformed conventional MP-RAGE in all image quality parameters (average scores = 3.7 vs. 4.9, p < 0.001). In the quantitative evaluation, DNN showed better error metrics (p < 0.001) and comparable (p > 0.09) or better (p < 0.02) volumetry results than conventional MP-RAGE. DNN application to VWI also revealed improved image quality (3.5 vs. 4.6, p < 0.001). CONCLUSION: The proposed DNN model successfully denoises 3D MR image and improves its image quality by using routine clinical scans only. KEY POINTS: • Our deep learning framework successfully improved conventional 3D high-resolution MRI in all image quality parameters, fine structure delineation, and lesion conspicuity. • Compared to conventional MRI, the proposed deep neural network-based MRI revealed better quantitative error metrics and comparable or better volumetry results. • Deep neural network application to 3D MRI whose pulse sequences and parameters were different from the training data showed improvement in image quality, revealing the potential to generalize on various clinical MRI.

An Exploratory Study of Medical Journal's Twitter Use: Metadata, Networks, and Content Analyses.

BACKGROUND: An increasing number of medical journals are using social media to promote themselves and communicate with their readers. However, little is known about how medical journals use Twitter and what their social media management strategies are. OBJECTIVE: This study aimed to understand how medical journals use Twitter from a global standpoint. We conducted a broad, in-depth analysis of all the available Twitter accounts of medical journals indexed by major indexing services, with a particular focus on their social networks and content. METHODS: The Twitter profiles and metadata of medical journals were analyzed along with the social networks on their Twitter accounts. RESULTS: The results showed that overall, publishers used different strategies regarding Twitter adoption, Twitter use patterns, and their subsequent decisions. The following specific findings were noted: journals with Twitter accounts had a significantly higher number of publications and a greater impact than their counterparts; subscription journals had a slightly higher Twitter adoption rate (2%) than open access journals; journals with higher impact had more followers; and prestigious journals rarely followed other lesser-known journals on social media. In addition, an in-depth analysis of 2000 randomly selected tweets from 4 prestigious journals revealed that The Lancet had dedicated considerable effort to communicating with people about health information and fulfilling its social responsibility by organizing committees and activities to engage with a broad range of health-related issues; The New England Journal of Medicine and the Journal of the American Medical Association focused on promoting research articles and attempting to maximize the visibility of their research articles; and the British Medical Journal provided copious amounts of health information and discussed various health-related social problems to increase social awareness of the field of medicine. CONCLUSIONS: Our study used various perspectives to investigate how medical journals use Twitter and explored the Twitter management strategies of 4 of the most prestigious journals. Our study provides a detailed understanding of medical journals' use of Twitter from various perspectives and can help publishers, journals, and researchers to better use Twitter for their respective purposes.

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities.

Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.

Highly accelerated 3D MPRAGE using deep neural network-based reconstruction for brain imaging in children and young adults.

OBJECTIVES: This study aimed to accelerate the 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence for brain imaging through the deep neural network (DNN). METHODS: This retrospective study used the k-space data of 240 scans (160 for the training set, mean ± standard deviation age, 93 ± 80 months, 94 males; 80 for the test set, 106 ± 83 months, 44 males) of conventional MPRAGE (C-MPRAGE) and 102 scans (77 ± 74 months, 52 males) of both C-MPRAGE and accelerated MPRAGE. All scans were acquired with 3T scanners. DNN was developed with simulated-acceleration data generated by under-sampling. Quantitative error metrics were compared between images reconstructed with DNN, GRAPPA, and E-SPIRIT using the paired t-test. Qualitative image quality was compared between C-MPRAGE and accelerated MPRAGE reconstructed with DNN (DNN-MPRAGE) by two readers. Lesions were segmented and the agreement between C-MPRAGE and DNN-MPRAGE was assessed using linear regression. RESULTS: Accelerated MPRAGE reduced scan times by 38% compared to C-MPRAGE (142 s vs. 320 s). For quantitative error metrics, DNN showed better performance than GRAPPA and E-SPIRIT (p < 0.001). For qualitative evaluation, overall image quality of DNN-MPRAGE was comparable (p > 0.999) or better (p = 0.025) than C-MPRAGE, depending on the reader. Pixelation was reduced in DNN-MPRAGE (p < 0.001). Other qualitative parameters were comparable (p > 0.05). Lesions in C-MPRAGE and DNN-MPRAGE showed good agreement for the dice similarity coefficient (= 0.68) and linear regression (R2 = 0.97; p < 0.001). CONCLUSIONS: DNN-MPRAGE reduced acquisition time by 38% and revealed comparable image quality to C-MPRAGE. KEY POINTS: • DNN-MPRAGE reduced acquisition times by 38%. • DNN-MPRAGE outperformed conventional reconstruction on accelerated scans (SSIM of DNN-MPRAGE = 0.96, GRAPPA = 0.43, E-SPIRIT = 0.88; p < 0.001). • Compared to C-MPRAGE scans, DNN-MPRAGE showed improved mean scores for overall image quality (2.46 vs. 2.52; p < 0.001) or comparable perceived SNR (2.56 vs. 2.58; p = 0.08).

Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging Assessment of Optic Pathway Function in Patients With Anterior Visual Pathway Compression.

BACKGROUND: In patients with sellar or parasellar tumors, it is crucial to evaluate visual field impairment in the preoperative stage and to predict visual field improvement after the surgery. The purpose of this study was to investigate the associations of diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) parameters in the optic radiations with preoperative and postoperative visual field impairment. METHODS: This prospective study included 81 participants with sellar or parasellar tumors. Multishell diffusion imaging and a visual field impairment score (VFIS) were acquired before and after the surgery. The multishell diffusion-weighted imaging was acquired to measure the neurite density and neurite orientation dispersion, as well as the diffusivity. DTI parameters were fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, and radial diffusivity (RD). NODDI provided intracellular volume fraction (Vic), the orientation dispersion index, and isotropic volume fraction (Viso). The associations of DTI and NODDI parameters in the optic radiations with VFIS were investigated, adjusting for age, tumor height, and symptom duration. RESULTS: Among 162 optic radiations, 117 were functionally impaired in the preoperative stage. FA and Vic had significant negative correlations, whereas MD and RD had significant positive correlations with the VFIS (all P < 0.001). In the preoperative stage, lower FA (P = 0.001; odds ratio = 0.750) and Vic (P = 0.003; OR = 0.827) and higher MD (P = 0.007; OR = 1.244) and RD (P < 0.001; OR = 1.361) were significantly associated with the presence of visual field impairment. For the degree of postoperative improvement, preoperative lower Vic (P = 0.034; OR = 0.910) and higher MD (P = 0.037; OR = 1.103) and RD (P = 0.047; OR = 1.090) were significantly associated with more postoperative improvement. CONCLUSIONS: DTI and NODDI parameters in the optic radiations were correlated with VFIS and associated with preoperative visual field impairments and postoperative improvement. It may help in predicting visual field improvement after the surgery in patients with sellar or parasellar tumors.

Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis.

BACKGROUND: Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. OBJECTIVE: This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. METHODS: We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. RESULTS: In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. CONCLUSIONS: This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work.

DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE.

Respiration-induced B0 fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at the expense of sequence modification. In this study, a new deep learning method, which is referred to as DeepResp, is proposed for reducing the respiration-artifacts in multi-slice gradient echo (GRE) images. DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks. Then, the network-generated phase errors are applied to the k-space data, creating an artifact-corrected image. For network training, the computer-simulated images were generated using artifact-free images and respiration data. When evaluated, both simulated images and in-vivo images of two different breathing conditions (deep breathing and natural breathing) show improvements (simulation: normalized root-mean-square error (NRMSE) from 7.8 ± 5.2% to 1.3 ± 0.6%; structural similarity (SSIM) from 0.88 ± 0.08 to 0.99 ± 0.01; ghost-to-signal-ratio (GSR) from 7.9 ± 7.2% to 0.6 ± 0.6%; deep breathing: NRMSE from 13.9 ± 4.6% to 5.8 ± 1.4%; SSIM from 0.86 ± 0.03 to 0.95 ± 0.01; GSR 20.2 ± 10.2% to 5.7 ± 2.3%; natural breathing: NRMSE from 5.2 ± 3.3% to 4.0 ± 2.5%; SSIM from 0.94 ± 0.04 to 0.97 ± 0.02; GSR 5.7 ± 5.0% to 2.8 ± 1.1%). Our approach does not require any modification of the sequence or additional hardware, and may therefore find useful applications. Furthermore, the deep neural networks extract respiration-induced phase errors, which is more interpretable and reliable than results of end-to-end trained networks.

Antiviral activity by lecithin-induced fengycin lipopeptides as a potent key substrate against Cucumber mosaic virus.

In this study, the effect of different growth substrates on the production of biosurfactants in the PPL strain of Bacillus amyloliquefaciens-a biocontrol agent for diseases affecting pepper and tomato plants-and on the antiviral effect of the PPL strain on Cucumber mosaic virus (CMV)-infected pepper plants was investigated. The multifunctional PPL strain exhibited enhanced growth and increased production of biosurfactants upon lecithin supplementation and consequently exhibited potent anti-CMV activity. The enhanced anti-CMV activity of the lecithin-supplemented PPL culture could be attributed to the antiviral effect as well as to the upregulation of plant defense-related genes. Treatment with pure commercial fengycins elicited a defense response against CMV in pepper plants; this effect was similar to that observed upon treatment with the lecithin-supplemented PPL culture. To the best of our knowledge, this is the first study to report the antiviral activity of lecithin-induced fengycin lipopeptides. These results suggest that the growth substrate affects antimicrobial production by B. amyloliquefaciens PPL, and consequently its antiviral activity.

Exploring linearity of deep neural network trained QSM: QSMnet.

Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill-conditioned dipole inversion in QSM and generated high-quality susceptibility maps. In this paper, the network, which was trained by healthy volunteer data, is evaluated for hemorrhagic lesions that have substantially higher susceptibility than healthy tissues in order to test "linearity" of QSMnet for susceptibility. The results show that QSMnet underestimates susceptibility in hemorrhagic lesions, revealing degraded linearity of the network for the untrained susceptibility range. To overcome this limitation, a data augmentation method is proposed to generalize the network for a wider range of susceptibility. The newly trained network, which is referred to as QSMnet+, is assessed in computer-simulated lesions with an extended susceptibility range (-1.4 â€‹ppm to +1.4 â€‹ppm) and also in twelve hemorrhagic patients. The simulation results demonstrate improved linearity of QSMnet+ over QSMnet (root mean square error of QSMnet+: 0.04 â€‹ppm vs. QSMnet: 0.36 â€‹ppm). When applied to patient data, QSMnet+ maps show less noticeable artifacts to those of conventional QSM maps. Moreover, the susceptibility values of QSMnet+ in hemorrhagic lesions are better matched to those of the conventional QSM method than those of QSMnet when analyzed using linear regression (QSMnet+: slope â€‹= â€‹1.05, intercept â€‹= â€‹-0.03, R2 â€‹= â€‹0.93; QSMnet: slope â€‹= â€‹0.68, intercept â€‹= â€‹0.06, R2 â€‹= â€‹0.86), consolidating improved linearity in QSMnet+. This study demonstrates the importance of the trained data range in deep neural network-powered parametric mapping and suggests the data augmentation approach for generalization of network. The new network can be applicable for a wide range of susceptibility quantification.

Antifungal evaluation of fengycin isoforms isolated from Bacillus amyloliquefaciens PPL against Fusarium oxysporum f. sp. lycopersici.

Bacillus amyloliquefaciens PPL is known to have a broad spectrum antifungal activity against plant pathogenic fungi. We focused on the cyclic lipopeptides (CLPs) extracted from the culture broth that are known to promote the ability and the efficiency of B. amyloliquefaciens PPL to control fungal diseases in pepper and tomato. In this study, the PPL strain exhibited enhanced culture yield and increased production of fengycin lipopeptides upon lecithin supplementation. The purified iturin A fraction from strain PPL exhibited higher antifungal activity (73 - 80%) against pepper anthracnose than fengycin fraction in vitro and in vivo. However, the control of tomato Fusarium wilt by the PPL strain was mainly attributed to fengycin lipopeptides. A comparison of liquid chromatography-mass spectrometry (LC-MS) and LC-tandem MS analysis of the filtrate, we found that the antifungal compounds against Fusarium wilt present in the strain PPL culture filtrate were a series of isoforms of fengycin (type F1, F2, and F3). The purified fengycin F1 type showed better antifungal activity against Fusarium wilt compared the other isoforms. To the best of our knowledge, this is the first study to report the antifungal activity of fengycin isoform types in the context of Fusarium wilt. The CLPs produced by the PPL strain are potential candidates for controlling fungal disease in tomato and pepper plants.

A Structural-Environmental Model of Alcohol and Substance-Related Sexual HIV Risk in Latino Migrant Day Laborers.

A structural-environmental model of alcohol and substance-related sexual HIV risk in 344 Latino migrant day laborers, participants in a cross-sectional survey, is tested using structural equation modeling. Hypothesized pathways include: (1) direct paths between environmental conditions and both distress related risk factors, and cultural and community protective factors; (2) indirect paths between environmental conditions and distress through cultural and community protective factors; and (3) indirect paths between environmental conditions and sexual risk through both distress risk factors and cultural and community protective factors. As hypothesized, the environmental factors, discrimination and working conditions, were indirectly related to sexual risk through the distress related factor, problem drinking, and through the protective factor, contact with family in country of origin. More specifically, as discrimination and working conditions worsen, contact with family decreases, problem drinking increases, and sexual risk increases. Implications for multi-level interventions are discussed.

RESUMEN: Un modelo estructural-ambiental de riesgo de VIH sexual relacionado con el alcohol y las sustancias en 344 jornaleros migrantes latinos, participantes en una encuesta transversal, se prueba usando modelos de ecuaciones estructurales. Las rutas hipotéticas incluyen: (1) rutas directas entre las condiciones ambientales y los factores de riesgo relacionados con la angustia y los factores de protección culturales y comunitarios; (2) rutas indirectas entre las condiciones ambientales y la angustia a través de factores protectores culturales y comunitarios; y (3) rutas indirectas entre las condiciones ambientales y el riesgo sexual a través de factores de riesgo de angustia y factores de protección culturales y comunitarios. Según la hipótesis, los factores ambientales, la discriminación y las condiciones de trabajo, estaban indirectamente relacionados con el riesgo sexual a través del factor relacionado con la angustia, el problema con el alcohol y, a través del factor de protección, el contacto con la familia en el país de origen. Más específicamente, a medida que empeoran la discriminación y las condiciones de trabajo, disminuye el contacto con la familia, aumenta el problema con la bebida y aumenta el riesgo sexual. Se discuten las implicaciones para intervenciones de múltiples niveles.

Advances in gradient echo myelin water imaging at 3T and 7T.

Gradient echo myelin water imaging (GRE-MWI) is an MRI technique to measure myelin concentration and involves the analysis of signal decay characteristics of the multi-echo gradient echo data. The method provides a myelin water fraction as a quantitative biomarker for myelin. In this work, a new sequence and post-processing methods were proposed to generate high quality GRE-MWI images at 3T and 7T. In order to capture the rapidly decaying myelin water signals, a bipolar readout GRE sequence was designed with "gradient pairing," compensating for the eddy current effects. The flip angle dependency from the multi-compartmental T1 effects was explored and avoided using a 2D multi-slice acquisition with a long TR. Additionally, the sequence was tested for the effects of inflow and magnetization transfer and demonstrated robustness to these error sources. Lastly, the temporal and spatial B0 inhomogeneity effects were mitigated by using the B0 navigator and field inhomogeneity corrections. Using the method, high-quality myelin water images were successfully generated for the in-vivo human brain at both field strengths. When the myelin water fraction at 3T and 7T were compared, they showed a good correlation (R2≥ 0.88; p < 0.001) with a larger myelin water fraction at 7T. The proposed method also opens the possibility of high resolution (isotropic 1.5 mm resolution) myelin water mapping at 7T.

Characterization and antifungal activity of chitosanase produced by Pedobacter sp. PR-M6.

To investigate the temperature requirements of chitosanase activity, as well as the degradation patterns generated by enzyme-induced chitosan oligomer hydrolysis, Pedobacter sp. PR-M6 was inoculated onto 0.5% colloidal chitosan medium agar plates. Cell growth was higher at 30 °C than at 20 °C during the initial 2 days of incubation. The protein content rapidly increased on day 1 at both temperatures and then it slowly increased at 20 °C and slowly decreased at 30 °C during the following 5 days of incubation. In order to characterize the electrophoretic pattern, Pedobacter sp. PR-M6 was cultured in 1% powder chitosan medium at 20 °C and 30 °C for 5 days after incubation and analyzed by SDS-PAGE. Four bands were visible, corresponding to ct1 (25 kDa), ct2 (17 kDa), ct3 (15 kDa), and ct4 (14 kDa), at both 20 °C and 30 °C. The optimal conditions for the activity of chitosanase produced from Pedobacter sp. PR-M6 were 60 °C and 1.81 enzyme units/mg protein. Two major isozyme bands (ct3 and ct4) exhibited their strongest chitosanase activity at 50 °C in SDS-PAGE gel. The reaction products generated from (GlcN)2-(GlcN)5 substrates at 60 °C after a 1 h incubation were investigated by thin-layer chromatography. Low-molecular weight chitosan and oligochitosan (LCOC) and soluble chitosan showed antifungal activity against A. brassicicola, B. cinerea, F. solani, and R. solani. LCOC exhibited higher antifungal activity than soluble chitosan. Moreover, LCOC treatments (500 ppm and 1000 ppm) inhibited conidia germination in A. brassicicola.

Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI).

MR g-ratio, which measures the ratio of the aggregate volume of axons to that of fibers in a voxel, is a potential biomarker for white matter microstructures. In this study, a new approach for acquiring an in-vivo whole human brain g-ratio map is proposed. To estimate the g-ratio, myelin volume fraction and axonal volume fraction are acquired using multi-echo gradient echo myelin water imaging (GRE-MWI) and neurite orientation dispersion and density imaging (NODDI), respectively. In order to translate myelin water fraction measured in GRE-MWI into myelin volume fraction, a new scaling procedure is proposed and validated. This scaling approach utilizes geometric measures of myelin structure and, therefore, provides robustness over previous methods. The resulting g-ratio map reveals an expected range of g-ratios (0.71-0.85 in major fiber bundles) with a small inter-subject coefficient of variance (less than 2%). Additionally, a few fiber bundles (e.g. cortico-spinal tract and optic radiation) show different constituents of myelin volume fraction and axonal volume fraction, indicating potentials to utilize the measures for deciphering fiber tracking.

Quantitative susceptibility mapping using deep neural network: QSMnet.

Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.

An R2* model of white matter for fiber orientation and myelin concentration.

Myelin, which consists of lipid bilayers, is one of the major constituents of white matter in the brain and has been suggested as a primary source of magnetic susceptibility contrasts. In this study, a new R2* model that simultaneously explains the effects of fiber orientation and myelin concentration is proposed and tested. In addition, a new approach that produces R2* maps without the effects of myelin is suggested. Experimental results demonstrate that the model reveals a high goodness of fit for the R2* distribution of white matter compared to a model that explains either fiber orientation or myelin concentration. The proposed R2* map shows a relatively uniform spatial distribution of R2* compared to the uncorrected R2* map and the fiber orientation or myelin concentration corrected R2* map.

Identification, purification, and expression patterns of chitinase from psychrotolerant Pedobacter sp. PR-M6 and antifungal activity in vitro.

In this study, a novel psychrotolerant chitinolytic bacterium Pedobacter sp. PR-M6 that displayed strong chitinolytic activity on 0.5% colloidal chitin was isolated from the soil of a decayed mushroom. Chitinase activity of PR-M6 at 25 °C (C25) after 6 days of incubation with colloidal chitin increased rapidly to a maximum level (31.3 U/mg proteins). Three chitinase isozymes (chiII, chiIII, and chiIV) from the crude enzyme at 25 °C (C25) incubation were expressed on SDS-PAGE gels at 25 °C. After purification by chitin-affinity chromatography, six chitinase isozymes (chiI, chiII, chiIII, chiIV, chiV, and chiVI) from C25-fractions were expressed on SDS-PAGE gels at 25 °C. Major bands of chitinase isozymes (chiI, chiII, and chiIII) from C4-fractions were strongly expressed on SDS-PAGE gels at 25 °C. Pedobacter sp. PR-M6 showed high inhibition rate of 60.9% and 57.5% against Rhizoctonia solani and Botrytis cinerea, respectively. These results indicated that psychrotolerant Pedobacter sp. PR-M6 could be applied widely as a microorganism agent for the biocontrol of agricultural phytopathogens at low temperatures.

Antifungal activity and patterns of N-acetyl-chitooligosaccharide degradation via chitinase produced from Serratia marcescens PRNK-1.

Serratia marcescens PRNK-1, which has strong chitinolytic activity, was isolated from cockroaches (Periplaneta americana L.). The chitinase from S. marcescens PRNK-1 was characterized after incubation in a 0.5% colloidal chitin medium at 30 °C for 3 days. The molecular weights of three bands after staining for chitinase activity were approximately 34, 41, and 48 kDa on an SDS-PAGE gel. S. marcescens PRNK-1 strain strongly inhibited hyphal growth of Rhizoctonia solani and Fusarium oxysporum. Thin-layer chromatography (TLC) and high performance liquid chromatograph (HPLC) analyses were conducted to investigate the degradation patterns of N-acetyl-chitooligosaccharides by PRNK-1 chitinase. The N-acetyl-chitooligosaccharides: N-acetyl-chitin dimer (GlcNAc)2, N-acetyl-chitin trimer (GlcNAc)3, and N-acetyl-chitin tetramer (GlcNAc)4 were degraded to (GlcNAc)1-3 on a TLC plate. In an additional experiment, (GlcNAc)6 was degraded to (GlcNAc)1-4 on a TLC plate. The optimal temperature for chitinase activity of the PRNK-1 was 50 °C, producing 32.8 units/mL. As seen via TLC, the highest degradation of (GlcNAc)4 by PRNK-1 chitinase occurred with 50 °C incubation. The optimal pH for chitinase activity of PRNK-1 was pH 5.5, producing 24.6 units/mL. As seen via TLC, the highest degradation of (GlcNAc)4 by PRNK-1 chitinase occurred at pH 5.0-6.0. These results indicate that chitinase produced from S. marcescens PRNK-1 strain showed strong antifungal activity and potential of production of N-acetyl-chitooligosaccharides.

Antifungal activity and expression patterns of extracellular chitinase and ß-1,3-glucanase in Wickerhamomyces anomalus EG2 treated with chitin and glucan.

In this study, the expression patterns of extracellular chitinase and ß-1,3-glucanase from cultured Wickerhamomyces anomalus EG2 treated with chitin, glucan, and chemical chitinase inhibitors (kinetin, caffeine, and acetazolamide) were investigated using SDS-PAGE. Relationship between enzyme expression and antifungal activity from yeast plays a very important role for biocontrol of phytopathoges. To determine antifungal activity against phytopathogens, W. anomalus EG2 was shown to strongly inhibit hyphal growth of Fusarium oxysporum KACC 40032 and Rhizoctonia solani KACC 40111. Slight chitinase activity was observed 12 h after incubation in both PDB and YPD medium without colloidal chitin. The molecular weight of chitinase was approximately 124 kDa ß-1,3-Glucanase isoenzyme (GN1 and GN2) was observed distinctly on SDS-PAGE gels when laminarin was used as a substrate. ß-1,3-Glucanase isoenzyme was not observed when using glucan-containing high polymer complex (GHPC) as a substrate. Production of chitinase from W. anomalus EG2 was inhibited slightly by acetazolamide. Abnormal and cluster-shaped cells of W. anomalus EG2 were observed in both PDB and YPD medium treated with colloidal chitin. These results indicated that W. anomalus EG2 could be applied commercially as a biological control agent of phytopathogens and as a bioinhibitor of yeast cell growth.

Antifungal activity of chitinase obtained from Paenibacillus ehimensis MA2012 against conidial of Collectotrichum gloeosporioides in vitro.

To investigate the expression patterns of chitinase on SDS-PAGE gel, Paenibacillus ehimensis MA2012 was incubated in gelatin-chitin medium (GCM) at 30 °C for 7 days. Six major bands (Ch3, Ch4, Ch5, Ch6, Ch7, and Ch8) of chitinase isozymes in GC medium appeared on SDS-PAGE gel during the incubation period. Chitinase activity staining of P. ehimensis MA2012 was detected on 2-DE with different pI values (4-11). After DEAE-Sephadex chromatography, eight bands (Ch1 to Ch8) of chitinase isozymes were stained strongly with Calcofluor white M2R at fraction 45. After Sephadex G-75 gel filtration, six bands (Ch3 to Ch8) of chitinase isozymes were stained with Calcofluor white M2R at fractions of 11-12. The specific activity of the purified chitinase was 3.8 units mg(-1) protein with a purification factor of 0.27. Inhibition rate of the conidial germination of Colletotrichum gloeosporioides was 87% in partial purified chitinase treatment compared with control.