Epitaxial Growth of GaN Films on Chemical-Vapor-Deposited 2D MoS2 Layers by Plasma-Assisted Molecular Beam Epitaxy.

The van der Waals epitaxy of wafer-scale GaN on 2D MoS2 and the integration of GaN/MoS2 heterostructures were investigated in this report. GaN films have been successfully grown on 2D MoS2 layers using three different Ga fluxes via a plasma-assisted molecular beam epitaxy (PA-MBE) system. The substrate for the growth was a few-layer 2D MoS2 deposited on sapphire using chemical vapor deposition (CVD). Three different Ga fluxes were provided by the gallium source of the K-cell at temperatures of 825, 875, and 925 °C, respectively. After the growth, RHEED, HR-XRD, and TEM were conducted to study the crystal structure of GaN films. The surface morphology was obtained using FE-SEM and AFM. Chemical composition was confirmed by XPS and EDS. Raman and PL spectra were carried out to investigate the optical properties of GaN films. According to the characterizations of GaN films, the van der Waals epitaxial growth mechanism of GaN films changed from 3D to 2D with the increase in Ga flux, provided by higher temperatures of the K-cell. GaN films grown at 750 °C for 3 h with a K-cell temperature of 925 °C demonstrated the greatest crystal quality, chemical composition, and optical properties. The heterostructure of 3D GaN on 2D MoS2 was integrated successfully using the low-temperature PA-MBE technique, which could be applied to novel electronics and optoelectronics.

Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images.

Significance: There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease (CAD). However, existing methods either require a large pixel-wise paired training dataset or have limited capability to map pathological regions. Aim: The aim of this work is to generate virtual histological information from coronary OCT images, without a pixel-wise paired training dataset while capable of providing pathological patterns. Approach: We design a structurally constrained, pathology-aware, transformer generative adversarial network, namely structurally constrained pathology-aware convolutional transformer generative adversarial network (SCPAT-GAN), to generate virtual stained H&E histology from OCT images. We quantitatively evaluate the quality of virtual stained histology images by measuring the Fréchet inception distance (FID) and perceptual hash value (PHV). Moreover, we invite experienced pathologists to evaluate the virtual stained images. Furthermore, we visually inspect the virtual stained image generated by SCPAT-GAN. Also, we perform an ablation study to validate the design of the proposed SCPAT-GAN. Finally, we demonstrate 3D virtual stained histology images. Results: Compared to previous research, the proposed SCPAT-GAN achieves better FID and PHV scores. The visual inspection suggests that the virtual histology images generated by SCPAT-GAN resemble both normal and pathological features without artifacts. As confirmed by the pathologists, the virtual stained images have good quality compared to real histology images. The ablation study confirms the effectiveness of the combination of proposed pathological awareness and structural constraining modules. Conclusions: The proposed SCPAT-GAN is the first to demonstrate the feasibility of generating both normal and pathological patterns without pixel-wisely supervised training. We expect the SCPAT-GAN to assist in the clinical evaluation of treating the CAD by providing 2D and 3D histopathological visualizations.

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network.

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregards frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network.

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregard frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.

Automated player identification and indexing using two-stage deep learning network.

American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crowded settings, distorted objects, and imbalanced data for identifying players, especially jersey numbers. In this work, we propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. First, we utilize an object detection network, a detection transformer, to tackle the player detection problem in a crowded context. Second, we identify players using jersey number recognition with a secondary convolutional neural network, then synchronize it with a game clock subsystem. Finally, the system outputs a complete log in a database for play indexing. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos. The proposed system shows great potential for implementation in and analysis of football broadcast video.

Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster.

The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.

Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images.

Significance: Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). However, unidentified calcified regions within a narrowed artery could impair the outcome of the treatment. Fast and objective identification is paramount to automatically procuring accurate readings on calcifications within the artery. Aim: We aim to rapidly identify calcification in coronary OCT images using a bounding box and reduce the prediction bias in automated prediction models. Approach: We first adopt a deep learning-based object detection model to rapidly draw the calcified region from coronary OCT images using a bounding box. We measure the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. To calibrate confidence scores of predictions, we implement dependent logistic calibration using each detection result's confidence and center coordinates. Results: We implemented an object detection module to draw the boundary of the calcified region at a rate of 140 frames per second. With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection and eliminate the estimation bias from various object detection methods. The calibrated confidence of prediction results in a confidence error of ∼ 0.13 , suggesting that the confidence calibration on calcification detection could provide a more trustworthy result. Conclusions: Given the rapid detection and effective calibration of the proposed work, we expect that it can assist in clinical evaluation of treating the CAD during the imaging-guided procedure.

RNA sequencing revealed the multi-stage transcriptome transformations during the development of gallbladder cancer associated with chronic inflammation.

Gallbladder cancer (GBC) is a highly malignant tumor with extremely poor prognosis. Previous studies have suggested that the carcinogenesis and progression of GBC is a multi-stage and multi-step process, but most of them focused on the genome changes. And a few studies just compared the transcriptome differences between tumor tissues and adjacent noncancerous tissues. The transcriptome changes, relating to every stage of GBC evolution, have rarely been studied. We selected three cases of normal gallbladder, four cases of gallbladder with chronic inflammation induced by gallstones, five cases of early GBC, and five cases of advanced GBC, using next-generation RNA sequencing to reveal the changes in mRNAs and lncRNAs expression during the evolution of GBC. In-depth analysis of the sequencing data indicated that transcriptome changes from normal gallbladder to gallbladder with chronic inflammation were distinctly related to inflammation, lipid metabolism, and sex hormone metabolism; transcriptome changes from gallbladder with chronic inflammation to early GBC were distinctly related to immune activities and connection between cells; and the transcriptome changes from early GBC to advanced GBC were distinctly related to transmembrane transport of substances and migration of cells. Expression profiles of mRNAs and lncRNAs change significantly during the evolution of GBC, in which lipid-based metabolic abnormalities play an important promotive role, inflammation and immune activities play a key role, and membrane proteins are very highlighted molecular changes.

A comparison of average daily gain, apparent digestibilities, energy balance, rumen fermentation parameters, and serum metabolites between yaks (Bos grunniens) and Qaidam cattle (Bos taurus) consuming diets differing in energy level.

Yaks (Bos grunniens), indigenous to the harsh Qinghai-Tibetan Plateau, are well adapted to the severe conditions, and graze natural pasture without supplements all year round. Qaidam cattle (Bos taurus), introduced to the Qinghai-Tibetan Plateau 1,700 years ago, are raised at a lower altitude than yaks, provided with shelter at night and offered supplements in winter. Based on their different backgrounds, we hypothesized that yaks have lower energy requirements for maintenance than cattle. To test this hypothesis, we measured average daily gain (ADG), apparent digestibilities, energy balance, rumen fermentation parameters, and serum metabolites in growing yaks and cattle offered diets differing in metabolizable energy (ME) levels (6.62, 8.02, 9.42 and 10.80 MJ/kg), but with the same crude protein concentration. Six castrated yaks (155 ± 5.8 kg) and 6 castrated Qaidam cattle (154 ± 8.0 kg), all 2.5 years old, were used in 2 concurrent 4 × 4 Latin square designs. Neutral and acid detergent fiber digestibilities were greater (P < 0.05) in yaks than in cattle, and decreased linearly (P < 0.05) with increasing dietary energy level; whereas, digestibilities of dry matter, organic matter, crude protein and ether extract increased (P < 0.05) linearly with increasing energy level. The ADG was greater (P < 0.001) in yaks than in cattle, and increased (P < 0.05) linearly with increasing energy levels. From the regressions of ADG on ME intake, the estimated ME requirement for maintenance was lower (P < 0.05) in yaks than in cattle (0.43 vs. 0.57 MJ/kg BW0.75). The ratios of digestible energy (DE):gross energy and ME:DE were higher (P < 0.05) in yaks than in cattle, and increased (P < 0.05) linearly with increasing dietary energy level. Ruminal pH decreased (P < 0.05), whereas concentrations of total volatile fatty acids (VFAs) and ammonia increased (P < 0.01) with increasing dietary energy level, and all were greater (P < 0.05) in yaks than in cattle. Concentrations of ruminal acetate and iso-VFAs were greater (P < 0.05), whereas propionate was lower (P < 0.05) in yaks than in cattle; acetate decreased (P < 0.001), whereas butyrate and propionate increased (P < 0.001) linearly with increasing dietary energy level. Serum concentrations of ß-hydroxybutyrate were lower (interaction, P < 0.001) in yaks than in cattle fed diets of 9.42 and 10.80 MJ/kg, whereas non-esterified fatty acids were greater (interaction, P < 0.01) in yaks than in cattle fed diets of 6.62 and 8.02 MJ/kg. Concentrations of serum leptin and growth hormone were greater in yaks than in cattle and serum insulin and growth hormone increased (P < 0.01) linearly with increasing dietary energy level. Our hypothesis that yaks have lower energy requirements for maintenance than cattle was supported. This lower requirement confers an advantage to yaks over Qaidam cattle in consuming low energy diets during the long winter on the Qinghai-Tibetan Plateau.

Large-conductance Ca2 +-activated K+ channel ß1-subunit maintains the contractile phenotype of vascular smooth muscle cells.

Background: Vascular smooth muscle cells (VSMCs) phenotype switching is very important during the pathogenesis and progression of vascular diseases. However, it is not well understood how normal VSMCs maintain the differentiated state. The large-conductance Ca2+-activated K+ (BKCa) channels are widely expressed in VSMCs and regulate vascular tone. Nevertheless, there is limited understanding of the role of the BKCa channel in modulation of the VSMC phenotype. Methods and results: We assessed BKCa channel expression levels in normal and injured carotid arteries from rats of the balloon-injury model. A strong decrease of BKCa-ß1 was seen in the injured carotid arteries, accompanied by a parallel decrease of the VSMC contractile markers. BKCa-ß1 in primary rat aortic VSMCs was decreased with the increase of passage numbers and the stimulation of platelet-derived growth factor (PDGF)-BB. Conversely, transforming growth factor ß upregulated BKCa-ß1. Meanwhile, the BKCa-ß1 level was positively associated with the levels of VSMC contractile proteins. Intravenous injection of PDGF-BB induced downregulation of BKCa-ß1 expression in the carotid arteries. Knockdown of BKCa-ß1 favored VSMC dedifferentiation, characterized by altered morphology, abnormal actin fiber organization, decreased contractile proteins expression and reduced contractile ability. Furthermore, the resultant VSMC dedifferentiated phenotype rendered increased proliferation, migration, enhanced inflammatory factors levels, and matrix metalloproteinases activity. Studies using primary cultured aortic VSMCs from human recapitulated key findings. Finally, protein level of BKCa-ß1 was reduced in human atherosclerotic arteries. Conclusion: BKCa-ß1 is important in the maintenance of the contractile phenotype of VSMCs. As a novel endogenous defender that prevents pathological VSMC phenotype switching, BKCa-ß1 may serve as a potential therapeutic target for treating vascular diseases including post-injury restenosis and atherosclerosis.

Prediction model of crowd noise in large waiting halls.

Crowd noise is usually the primary noise in large waiting halls, and it is difficult to predict because it is influenced by several factors such as room acoustics and crowd characteristics. This study developed a crowd noise prediction model based on the superposition of direct and reverberant sound energy using the factors of the spatial layout of waiting halls, number and distribution of crowds, behavior ratio (ratio of vocal passengers to the total number of passengers), and average crowd sound power. To verify the model, on-site measurements were conducted in two large waiting halls to obtain the necessary input parameters. The crowd noise levels in one of the waiting halls were obtained from 1-s noise level data after excluding broadcast periods. A method for determining an individual's average sound power based on the model was also presented and found to be approximately 70.6 dB. Finally, the model was verified using measured data, and it showed that the model could accurately predict the average crowd noise level and changing trend of crowd noise in temporal and spatial dimensions with an average R-square of approximately 0.55 and average difference of approximately 1.1 dBA between the predicted and measured results.

Multi-Scale Reconstruction of Undersampled Spectral-Spatial OCT Data for Coronary Imaging Using Deep Learning.

Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain while maintaining high quality in image reconstruction. The down-scaling schedule boosts data acquisition speed without any hardware modifications. Additionally, we propose a unified multi-scale reconstruction framework, namely Multiscale-Spectral-Spatial-Magnification Network (MSSMN), to resolve highly down-scaled (compressed) OCT images with flexible magnification factors. We incorporate the proposed methods into Spectral Domain OCT (SD-OCT) imaging of human coronary samples with clinical features such as stent and calcified lesions. Our experimental results demonstrate that spectral-spatial down-scaled data can be better reconstructed than data that are down-scaled solely in either spectral or spatial domain. Moreover, we observe better reconstruction performance using MSSMN than using existing reconstruction methods. Our acquisition method and multi-scale reconstruction framework, in combination, may allow faster SD-OCT inspection with high resolution during coronary intervention.

Soil moisture determines nitrous oxide emission and uptake.

Soils are major sources and sinks of nitrous oxide (N2O). The main pathway of N2O emission is performed through soil denitrification; however, the uptake phenomenon in denitrification is overlooked, leading to an underestimation of N2O production. Soil moisture strongly influences denitrification rates, but exact quantifications coupled with nosZ, nirK, and nirS gene analysis remain inadequately unaccounted for. In this study, a 15N-N2O pool dilution (15N2OPD) method was used to measure N2O production rates under different soil moisture levels. Therefore, 20%, 40%, 60%, 80% and 100% soil water holding capacity (WHC) were used. The results revealed that N2O uptake rates increased proportionally with soil moisture content and peaked at 80% WHC with 4.17 ± 2.74 µg N kg-1 soil h-1. The N2O production and net emission rates similarly peaked at 80% WHC, reading at 32.50 ± 4.86 and 27.63 ± 3.09 µg N kg-1 soil h-1 during the incubation period (18 days). Soil moisture content increased the gene copy number of the nosZ, NH4+ content, and denitrification potential in soil. N2O uptake at WHC 80-100% was significantly greater than that at WHC 20-60%. It was attributed to a decrease in O2 and the high NO3- concentration inhibition (> 50 mg N kg-1 of soil NO3--N content). Principal components analysis (PCA) indicated that the number of nosZ genes was the major driver of N2O uptake, especially nosZ clade II. Thus, the results of this study deepen our understanding of the mechanisms underpinning N2O sources and sinks in soils and provide a useful gene-based indicator to estimate N2O uptake.

Pathways of soil N2O uptake, consumption, and its driving factors: a review.

Nitrous oxide (N2O) is an important greenhouse gas that plays a significant role in atmospheric photochemical reactions and contributes to stratospheric ozone depletion. Soils are the main sources of N2O emissions. In recent years, it has been demonstrated that soil is not only a source but also a sink of N2O uptake and consumption. N2O emissions at the soil surface are the result of gross N2O production, uptake, and consumption, which are co-occurring processes. Soil N2O uptake and consumption are complex biological processes, and their mechanisms are still worth an in-depth systematic study. This paper aimed to systematically address the current research progress on soil N2O uptake and consumption. Based on a bibliometric perspective, this study has highlighted the pathways of soil N2O uptake and consumption and their driving factors and measurement techniques. This systematic review of N2O uptake and consumption will help to further understand N transformations and soil N2O emissions.

Comparison of Daily Routines Between Middle-aged and Older Participants With and Those Without Diabetes in the Electronic Framingham Heart Study: Cohort Study.

BACKGROUND: Daily routines (eg, physical activity and sleep patterns) are important for diabetes self-management. Traditional research methods are not optimal for documenting long-term daily routine patterns in participants with glycemic conditions. Mobile health offers an effective approach for collecting users' long-term daily activities and analyzing their daily routine patterns in relation to diabetes status. OBJECTIVE: This study aims to understand how routines function in diabetes self-management. We evaluate the associations of daily routine variables derived from a smartwatch with diabetes status in the electronic Framingham Heart Study (eFHS). METHODS: The eFHS enrolled the Framingham Heart Study participants at health examination 3 between 2016 and 2019. At baseline, diabetes was defined as fasting blood glucose level ≥126 mg/dL or as a self-report of taking a glucose-lowering medication; prediabetes was defined as fasting blood glucose level of 100-125 mg/dL. Using smartwatch data, we calculated the average daily step counts and estimated the wake-up times and bedtimes for the eFHS participants on a given day. We compared the average daily step counts and the intraindividual variability of the wake-up times and bedtimes of the participants with diabetes and prediabetes with those of the referents who were neither diabetic nor prediabetic, adjusting for age, sex, and race or ethnicity. RESULTS: We included 796 participants (494/796, 62.1% women; mean age 52.8, SD 8.7 years) who wore a smartwatch for at least 10 hours/day and remained in the study for at least 30 days after enrollment. On average, participants with diabetes (41/796, 5.2%) took 1611 fewer daily steps (95% CI 863-2360; P<.001) and had 12 more minutes (95% CI 6-18; P<.001) in the variation of their estimated wake-up times, 6 more minutes (95% CI 2-9; P=.005) in the variation of their estimated bedtimes compared with the referents (546/796, 68.6%) without diabetes or prediabetes. Participants with prediabetes (209/796, 26.2%) also walked fewer daily steps (P=.04) and had a larger variation in their estimated wake-up times (P=.04) compared with the referents. CONCLUSIONS: On average, participants with diabetes at baseline walked significantly fewer daily steps and had larger variations in their wake-up times and bedtimes than the referent group. These findings suggest that modifying the routines of participants with poor glycemic health may be an important approach to the self-management of diabetes. Future studies should be designed to improve the remote monitoring and self-management of diabetes.

Complete sequence and comparative analysis of the mitochondrial genome of the rare and endangered Clematis acerifolia, the first clematis mitogenome to provide new insights into the phylogenetic evolutionary status of the genus.

Clematis is one of the large worldwide genera of the Ranunculaceae Juss. Family, with high ornamental and medicinal value. China is the modern distribution centre of Clematis with abundant natural populations. Due to the complexity and high morphological diversity of Clematis, the genus is difficult to classify systematically, and in particular, the phylogenetic position of the endangered Clematis acerifolia is highly controversial. The use of the mitochondrial complete genome is a powerful molecular method that is frequently used for inferring plants phylogenies. However, studies on Clematis mitogenome are rare, thus limiting our full understanding of its phylogeny and genome evolution. Here, we sequenced and annotated the C. acerifolia mt genome using Illumina short- and Nanopore long-reads, characterized the species first complete mitogenome, and performed a comparative phylogenetic analysis with its close relatives. The total length of the C. acerifolia mitogenome is 698,247 bp and the main structure is multi-branched (linear molecule 1 and circular molecule 2). We annotated 55 genes, including 35 protein-coding, 17 tRNA, and 3 rRNA genes. The C. acerifolia mitogenome has extremely unconserved structurally, with extensive sequence transfer between the chloroplast and mitochondrial organelles, sequence repeats, and RNA editing. The phylogenetic position of C. acerifolia was determined by constructing the species mitogenome with 24 angiosperms. Further, our C. acerifolia mitogenome characteristics investigation included GC contents, codon usage, repeats and synteny analysis. Overall, our results are expected to provide fundamental information for C. acerifolia mitogenome evolution and confirm the validity of mitochondrial analysis in determining the phylogenetic positioning of Clematis plants.

Mannose and phosphomannose isomerase regulate energy metabolism under glucose starvation in leukemia.

Diverse metabolic changes are induced by various driver oncogenes during the onset and progression of leukemia. By upregulating glycolysis, cancer cells acquire a proliferative advantage over normal hematopoietic cells; in addition, these changes in energy metabolism contribute to anticancer drug resistance. Because leukemia cells proliferate by consuming glucose as an energy source, an alternative nutrient source is essential when glucose levels in bone marrow are insufficient. We profiled sugar metabolism in leukemia cells and found that mannose is an energy source for glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway. Leukemia cells express high levels of phosphomannose isomerase (PMI), which mobilizes mannose to glycolysis; consequently, even mannose in the blood can be used as an energy source for glycolysis. Conversely, suppression of PMI expression or a mannose load exceeding the processing capacity of PMI inhibited transcription of genes related to mitochondrial metabolism and the TCA cycle, therefore suppressing the growth of leukemia cells. High PMI expression was also a poor prognostic factor for acute myeloid leukemia. Our findings reveal a new mechanism for glucose starvation resistance in leukemia. Furthermore, the combination of PMI suppression and mannose loading has potential as a novel treatment for driver oncogene-independent leukemia.

Alteration in Oral Microbiome Among Men Who Have Sex With Men With Acute and Chronic HIV Infection on Antiretroviral Therapy.

Despite the antiretroviral therapy (ART), human immunodeficiency virus (HIV)-related oral disease remains a common problem for people living with HIV (PLWH). Evidence suggests that impairment of immune function in HIV infection might lead to the conversion of commensal bacteria to microorganisms with increased pathogenicity. However, limited information is available about alteration in oral microbiome in PLWH on ART. We performed a longitudinal comparative study on men who have sex with men (MSM) with acute HIV infection (n=15), MSM with chronic HIV infection (n=15), and HIV-uninfected MSM controls (n=15). Throat swabs were collected when these subjects were recruited (W0) and 12 weeks after ART treatment (W12) from the patients. Genomic DNAs were extracted and 16S rRNA gene sequencing was performed. Microbiome diversity was significantly decreased in patients with acute and chronic HIV infections compared with those in controls at the sampling time of W0 and the significant difference remained at W12. An increased abundance of unidentified Prevotellaceae was found in patients with acute and chronic HIV infections. Moreover, increased abundances of Prevotella in subjects with acute HIV infection and Streptococcus in subjects with chronic HIV infection were observed. In contrast, greater abundance in Lactobacillus, Rothia, Lautropia, and Bacteroides was found in controls. After effective ART, Bradyrhizobium was enriched in both acute and chronic HIV infections, whereas in controls, Lactobacillus, Rothia, Clostridia, Actinobacteria, and Ruminococcaceae were enriched. In addition, we found that lower CD4+ T-cell counts (<200 cells/mm3) were associated with lower relative abundances of Haemophilus, Actinomyces, unidentified Ruminococcaceae, and Rothia. This study has shown alteration in oral microbiome resulting from HIV infection and ART. The results obtained warrant further studies in a large number of subjects with different ethnics. It might contribute to improved oral health in HIV-infected individuals.

Genetic variations of toll-like receptors: Impact on susceptibility, severity and prognosis of bacterial meningitis.

Bacterial meningitis (BM) is a serious infectious disease of the central nervous system，which is mainly caused by Streptococcus pneumoniae, Neisseria meningitidis, Haemophilus influenzae, Group B Streptococcus and Listeria monocytogenes. Throughout the world, BM has become one of the most lethal diseases that commonly occurs in children. Toll like receptors (TLRs) are one of the most important immune defense lines in infectious diseases, and play an essential role in host defense. Accumulating evidence shows that genetic variations in TLRs are associated with host responses in BM. This review aims to summarize the role of different TLRs and their genetic variations in the susceptibility, severity and prognosis of BM and discuss the identified risk factors for better treatment and improvement of the course and outcome of BM.

Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation.

Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler's elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.