Hua Zhang

Bio: Hua Zhang is an academic researcher from Zhejiang University. The author has contributed to research in topics: MIMO & Orthogonal frequency-division multiplexing. The author has an hindex of 30, co-authored 156 publications receiving 2985 citations. Previous affiliations of Hua Zhang include University of Shanghai for Science and Technology & Southeast University.

Alterations of the Gut Microbiota in Patients With Coronavirus Disease 2019 or H1N1 Influenza

Abstract: BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging serious global health problem. Gastrointestinal symptoms are common in COVID-19 patients, and SARS-CoV-2 RNA has been detected in stool specimens. However, the relationship between the gut microbiome and disease remains to be established. METHODS: We conducted a cross-sectional study of 30 COVID-19 patients, 24 influenza A (H1N1) patients, and 30 matched healthy controls (HC) to identify differences in the gut microbiota by 16S ribosomal RNA (rRNA) gene V3-V4 region sequencing. RESULTS: Compared with HC, COVID-19 patients had significantly reduced bacterial diversity, a significantly higher relative abundance of opportunistic pathogens, such as Streptococcus, Rothia, Veillonella and Actinomyces, and a lower relative abundance of beneficial symbionts. Five biomarkers showed high accuracy for distinguishing COVID-19 patients from HC with an area under the curve (AUC) up to 0.89. Patients with H1N1 displayed lower diversity and different overall microbial composition compared with COVID-19 patients. Seven biomarkers were selected to distinguish the two cohorts with an AUC of 0.94. CONCLUSION: The gut microbial signature of patients with COVID-19 was different from that of H1N1 patients and HC. Our study suggests the potential value of the gut microbiota as a diagnostic biomarker and therapeutic target for COVID-19, but further validation is needed.

Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma.

Abstract: Objective To characterise gut microbiome in patients with hepatocellular carcinoma (HCC) and evaluate the potential of microbiome as non-invasive biomarkers for HCC Design We collected 486 faecal samples from East China, Central China and Northwest China prospectively and finally 419 samples completed Miseq sequencing We characterised gut microbiome, identified microbial markers and constructed HCC classifier in 75 early HCC, 40 cirrhosis and 75 healthy controls We validated the results in 56 controls, 30 early HCC and 45 advanced HCC We further verified diagnosis potential in 18 HCC from Xinjiang and 80 HCC from Zhengzhou Results Faecal microbial diversity was increased from cirrhosis to early HCC with cirrhosis Phylum Actinobacteria was increased in early HCC versus cirrhosis Correspondingly, 13 genera including Gemmiger and Parabacteroides were enriched in early HCC versus cirrhosis Butyrate-producing genera were decreased, while genera producing-lipopolysaccharide were increased in early HCC versus controls The optimal 30 microbial markers were identified through a fivefold cross-validation on a random forest model and achieved an area under the curve of 8064% between 75 early HCC and 105 non-HCC samples Notably, gut microbial markers validated strong diagnosis potential for early HCC and even advanced HCC Importantly, microbial markers successfully achieved a cross-region validation of HCC from Northwest China and Central China Conclusions This study is the first to characterise gut microbiome in patients with HCC and to report the successful diagnosis model establishment and cross-region validation of microbial markers for HCC Gut microbiota-targeted biomarkers represent potential non-invasive tools for early diagnosis of HCC

Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems

Abstract: For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a very challenging issue in channel estimation. In this paper, deep convolutional neural network (CNN) is employed to address this problem. We first propose a spatial-frequency CNN (SF-CNN) based channel estimation exploiting both the spatial and frequency correlation, where the corrupted channel matrices at adjacent subcarriers are input into the CNN simultaneously. Then, exploiting the temporal correlation in time-varying channels, a spatial-frequency-temporal CNN (SFT-CNN) based approach is developed to further improve the accuracy. Moreover, we design a spatial pilot-reduced CNN (SPR-CNN) to save spatial pilot overhead for channel estimation, where channels in several successive coherence intervals are grouped and estimated by a channel estimation unit with memory. Numerical results show that the proposed SF-CNN and SFT-CNN based approaches outperform the non-ideal minimum mean-squared error (MMSE) estimator but with reduced complexity, and achieve the performance close to the ideal MMSE estimator that is very difficult to be implemented in practical situations. They are also robust to different propagation scenarios. The SPR-CNN based approach achieves comparable performance to SF-CNN and SFT-CNN based approaches while only requires about one-third of spatial pilot overhead at the cost of complexity. The results in this paper clearly demonstrate that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems.

Gut microbial profile analysis by MiSeq sequencing of pancreatic carcinoma patients in China

Abstract: Pancreatic carcinoma (PC) is a lethal cancer. Gut microbiota is associated with some risk factors of PC, e.g. obesity and types II diabetes. However, the specific gut microbial profile in clinical PC in China has never been reported. This prospective study collected 85 PC and 57 matched healthy controls (HC) to analyze microbial characteristics by MiSeq sequencing. The results showed that gut microbial diversity was decreased in PC with an unique microbial profile, which partly attributed to its decrease of alpha diversity. Microbial alterations in PC featured by the increase of certain pathogens and lipopolysaccharides-producing bacteria, and the decrease of probiotics and butyrate-producing bacteria. Microbial community in obstruction cases was separated from the un-obstructed cases. Streptococcus was associated with the bile. Furthermore, 23 microbial functions e.g. Leucine and LPS biosynthesis were enriched, while 13 functions were reduced in PC. Importantly, based on 40 genera associated with PC, microbial markers achieves a high classification power with AUC of 0.842. In conclusion, gut microbial profile was unique in PC, providing a microbial marker for non-invasive PC diagnosis.

Convergence of Probability Measures

Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Table Of Integrals Series And Products

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Convergence of probability measures

Abstract: The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

SF-010-4 Distant metastasis occurs late during the genetic evolution of pancreatic cancer

Abstract: Metastasis, the dissemination and growth of neoplastic cells in an organ distinct from that in which they originated, is the most common cause of death in cancer patients. This is particularly true for pancreatic cancers, where most patients are diagnosed with metastatic disease and few show a sustained response to chemotherapy or radiation therapy. Whether the dismal prognosis of patients with pancreatic cancer compared to patients with other types of cancer is a result of late diagnosis or early dissemination of disease to distant organs is not known. Here we rely on data generated by sequencing the genomes of seven pancreatic cancer metastases to evaluate the clonal relationships among primary and metastatic cancers. We find that clonal populations that give rise to distant metastases are represented within the primary carcinoma, but these clones are genetically evolved from the original parental, non-metastatic clone. Thus, genetic heterogeneity of metastases reflects that within the primary carcinoma. A quantitative analysis of the timing of the genetic evolution of pancreatic cancer was performed, indicating at least a decade between the occurrence of the initiating mutation and the birth of the parental, non-metastatic founder cell. At least five more years are required for the acquisition of metastatic ability and patients die an average of two years thereafter. These data provide novel insights into the genetic features underlying pancreatic cancer progression and define a broad time window of opportunity for early detection to prevent deaths from metastatic disease.