Structural elucidation of phosphoglycolipids from strains of the bacterial thermophiles Thermus and Meiothermus.

The structures of two major phosphoglycolipids from the thermophilic bacteria Thermus oshimai NTU-063, Thermus thermophilus NTU-077, Meiothermus ruber NTU-124, and Meiothermus taiwanensis NTU-220 were determined using spectroscopic and chemical analyses to be 2'-O-(1,2-diacyl-sn-glycero-3-phospho) -3'-O-(alpha-N-acetyl-glucosaminyl)-N-glyceroyl alkylamine [PGL1 (1)] and the novel structure 2'-O-(2-acylalkyldio-1-O-phospho)-3'-O-(alpha-N-acetylglucosaminyl)-N-glyceroyl alkylamine [PGL2 (2)]. PGL2 (2) is the first phosphoglycolipid identified with a 2-acylalkyldio-1-O-phosphate moiety. The fatty acids of the phosphoglycolipids are mainly iso-C(15:0), -C(16:0), and -C(17:0) and anteiso-C(15:0) and -C(17:0). The ratios of PGL2 (2) to PGL1 (1) are significantly altered when grown at different temperatures for three strains, T. thermophilus NTU-077, M. ruber NTU-124, and M. taiwanensis NTU-220, but not for T. oshimai NTU-063. Accordingly, the ratios of iso- to anteiso-branched fatty acids increase when grown at the higher temperature

Hyperbaric oxygen suppressed tumor progression through the improvement of tumor hypoxia and induction of tumor apoptosis in A549-cell-transferred lung cancer

Tumor cells have long been recognized as a relative contraindication to hyperbaric oxygen treatment (HBOT) since HBOT might enhance progressive cancer growth. However, in an oxygen deficit condition, tumor cells are more progressive and can be metastatic. HBOT increasing in oxygen partial pressure may benefit tumor suppression. In this study, we investigated the effects of HBOT on solid tumors, such as lung cancer. Non-small cell human lung carcinoma A549-cell-transferred severe combined immunodeficiency mice (SCID) mice were selected as an in vivo model to detect the potential mechanism of HBOT in lung tumors. HBOT not only improved tumor hypoxia but also suppressed tumor growth in murine xenograft tumor models. Platelet endothelial cell adhesion molecule (PECAM-1/CD31) was significantly increased after HBOT. Immunostaining of cleaved caspase-3 was demonstrated and apoptotic tumor cells with nuclear debris were aggregated starting on the 14th-day after HBOT. In vitro, HBOT suppressed the growth of A549 cells in a time-dependent manner and immediately downregulated the expression of p53 protein after HBOT in A549 cells. Furthermore, HBOT-reduced p53 protein could be rescued by a proteasome degradation inhibitor, but not an autophagy inhibitor in A549 cells. Our results demonstrated that HBOT improved tissue angiogenesis, tumor hypoxia and increased tumor apoptosis to lung cancer cells in murine xenograft tumor models, through modifying the tumor hypoxic microenvironment. HBOT will merit further cancer therapy as an adjuvant treatment for solid tumors, such as lung cancer

A Study on Tunneling Current of ONO Films and Data Retention Effects in Flash Memories

Abstract In this research, 5 different thicknesses of oxide-nitride-oxide (ONO) inter-poly-gate dielectrics in flash memories are studied. Besides the experiments of analyzing program/erase speeds, various I-V tests have also being conducted to understand the tunneling characteristics of these ONO films. Data retention effects are also investigated by measuring the threshold voltage shifts consecutively up to 200 h of 250EC baking. All the findings are analyzed and concluded to propose a set of ONO film scaling rules

Fabrication of Compact Microstrip Line-Based Balun-Bandpass Filter with High Common-Mode Suppression

A new type of balun-bandpass filter was proposed based on the traditional coupled-line theory and folded open-loop ring resonators (OLRRs) configuration. For that, a new device with both filter-type and balun-type characteristics was investigated and fabricated. Both magnetic and electric coupling structures were implemented to provide high performance balun-bandpass responses. The fabricated balun-bandpass filters had a wide bandwidth more than 200 MHz and a low insertion loss less than 2.51 dB at a center frequency of 2.6 GHz. The differences between the two outputs were below 0.4 dB in magnitude and within 180 ± 7° in phase. Also, the balun-bandpass filter presented an excellent common-mode rejection ratio over 25 dB in the passband. An advanced design methodology had been adopted based on EM simulation for making these designed parameters of OLRRs, microstrip lines, and open stubs. The measured frequency responses agreed well with simulated ones

Metrology Camera System of Prime Focus Spectrograph for Subaru Telescope

The Prime Focus Spectrograph (PFS) is a new optical/near-infrared multi-fiber spectrograph designed for the prime focus of the 8.2m Subaru telescope. The metrology camera system of PFS serves as the optical encoder of the COBRA fiber motors for the configuring of fibers. The 380mm diameter aperture metrology camera will locate at the Cassegrain focus of Subaru telescope to cover the whole focal plane with one 50M pixel Canon CMOS sensor. The metrology camera is designed to provide the fiber position information within 5{\mu}m error over the 45cm focal plane. The positions of all fibers can be obtained within 1s after the exposure is finished. This enables the overall fiber configuration to be less than 2 minutes.Comment: 10 pages, 12 figures, SPIE Astronomical Telescopes and Instrumentation 201

Committed Private Information Retrieval

A private information retrieval (PIR) scheme allows a client to retrieve a data item $x_i$ among $n$ items $x_1,x_2,\ldots,x_n$ from $k$ servers, without revealing what $i$ is even when $t < k$ servers collude and try to learn $i$. Such a PIR scheme is said to be $t$-private. A PIR scheme is $v$-verifiable if the client can verify the correctness of the retrieved $x_i$ even when $v \leq k$ servers collude and try to fool the client by sending manipulated data. Most of the previous works in the literature on PIR assumed that $v < k$, leaving the case of all-colluding servers open. We propose a generic construction that combines a linear map commitment (LMC) and an arbitrary linear PIR scheme to produce a $k$-verifiable PIR scheme, termed a committed PIR scheme. Such a scheme guarantees that even in the worst scenario, when all servers are under the control of an attacker, although the privacy is unavoidably lost, the client won't be fooled into accepting an incorrect $x_i$. We demonstrate the practicality of our proposal by implementing the committed PIR schemes based on the Lai-Malavolta LMC and three well-known PIR schemes using the GMP library and blst, the current fastest C library for elliptic curve pairings.Comment: Accepted at ESORICS 202

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion