Clinical Characteristics of 26 Human Cases of Highly Pathogenic Avian Influenza A (H5N1) Virus Infection in China

BACKGROUND: While human cases of highly pathogenic avian influenza A (H5N1) virus infection continue to increase globally, available clinical data on H5N1 cases are limited. We conducted a retrospective study of 26 confirmed human H5N1 cases identified through surveillance in China from October 2005 through April 2008. METHODOLOGY/PRINCIPAL FINDINGS: Data were collected from hospital medical records of H5N1 cases and analyzed. The median age was 29 years (range 6-62) and 58% were female. Many H5N1 cases reported fever (92%) and cough (58%) at illness onset, and had lower respiratory findings of tachypnea and dyspnea at admission. All cases progressed rapidly to bilateral pneumonia. Clinical complications included acute respiratory distress syndrome (ARDS, 81%), cardiac failure (50%), elevated aminotransaminases (43%), and renal dysfunction (17%). Fatal cases had a lower median nadir platelet count (64.5 x 10(9) cells/L vs 93.0 x 10(9) cells/L, p = 0.02), higher median peak lactic dehydrogenase (LDH) level (1982.5 U/L vs 1230.0 U/L, p = 0.001), higher percentage of ARDS (94% [n = 16] vs 56% [n = 5], p = 0.034) and more frequent cardiac failure (71% [n = 12] vs 11% [n = 1], p = 0.011) than nonfatal cases. A higher proportion of patients who received antiviral drugs survived compared to untreated (67% [8/12] vs 7% [1/14], p = 0.003). CONCLUSIONS/SIGNIFICANCE: The clinical course of Chinese H5N1 cases is characterized by fever and cough initially, with rapid progression to lower respiratory disease. Decreased platelet count, elevated LDH level, ARDS and cardiac failure were associated with fatal outcomes. Clinical management of H5N1 cases should be standardized in China to include early antiviral treatment for suspected H5N1 cases

Evolution and Taxonomic Classification of Human Papillomavirus 16 (HPV16)-Related Variant Genomes: HPV31, HPV33, HPV35, HPV52, HPV58 and HPV67

Human papillomavirus 16 (HPV16) species group (alpha-9) of the Alphapapillomavirus genus contains HPV16, HPV31, HPV33, HPV35, HPV52, HPV58 and HPV67. These HPVs account for 75% of invasive cervical cancers worldwide. Viral variants of these HPVs differ in evolutionary history and pathogenicity. Moreover, a comprehensive nomenclature system for HPV variants is lacking, limiting comparisons between studies.DNA from cervical samples previously characterized for HPV type were obtained from multiple geographic regions to screen for novel variants. The complete 8 kb genomes of 120 variants representing the major and minor lineages of the HPV16-related alpha-9 HPV types were sequenced to capture maximum viral heterogeneity. Viral evolution was characterized by constructing phylogenic trees based on complete genomes using multiple algorithms. Maximal and viral region specific divergence was calculated by global and pairwise alignments. Variant lineages were classified and named using an alphanumeric system; the prototype genome was assigned to the A lineage for all types.The range of genome-genome sequence heterogeneity varied from 0.6% for HPV35 to 2.2% for HPV52 and included 1.4% for HPV31, 1.1% for HPV33, 1.7% for HPV58 and 1.1% for HPV67. Nucleotide differences of approximately 1.0% - 10.0% and 0.5%-1.0% of the complete genomes were used to define variant lineages and sublineages, respectively. Each gene/region differs in sequence diversity, from most variable to least variable: noncoding region 1 (NCR1) /noncoding region 2 (NCR2) >upstream regulatory region (URR)> E6/E7 > E2/L2 > E1/L1.These data define maximum viral genomic heterogeneity of HPV16-related alpha-9 HPV variants. The proposed nomenclature system facilitates the comparison of variants across epidemiological studies. Sequence diversity and phylogenies of this clinically important group of HPVs provides the basis for further studies of discrete viral evolution, epidemiology, pathogenesis and preventative/therapeutic interventions

Routing and Power Allocation in Asynchronous Gaussian Multiple-Relay Channels

We investigate the cooperation efficiency of the multiple-relay channel when carrier-level synchronization is not available and all nodes use a decode-forward scheme. We show that by using decode-forward relay signaling, the transmission is effectively interference-free even when all communications share one common physical medium. Furthermore, for any channel realization, we show that there always exist a sequential path and a corresponding simple power allocation policy, which are optimal. Although this does not naturally lead to a polynomial algorithm for the optimization problem, it greatly reduces the search space and makes finding heuristic algorithms easier. To illustrate the efficiency of cooperation and provide prototypes for practical implementation of relay-channel signaling, we propose two heuristic algorithms. The numerical results show that in the low-rate regime, the gain from cooperation is limited, while the gain is considerable in the high-rate regime.</p

A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data

Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm is used to extract the typical electricity consumption behaviors and perform fuzzy consumer categorization, followed by a proposed novel algorithm to identify distinct consumer categories and their consumption characteristics. Supervised classification algorithm is used to classify new consumers and evaluate the validity of the identified categories. The proposed model is applied to a real dataset of U.S. non-residential consumers collected by smart meters over one year. The results indicate that large or special institutions usually have their distinct consumption characteristics while others such as some medium and small institutions or similar building types may have the same characteristics. Moreover, the comparison results with other methods show the improved performance of the proposed model in terms of category identification and classifying accuracy

High-performance Fe–Si soft magnetic composites with controllable silicate/nano-Fe composite coating

The design of magnetic insulation coating structure has always been a challenge for high-performance soft magnetic composites (SMCs). In this work, we prepared Fe–Si SMCs with silicate/nano-Fe composite coating successfully by in-situ oxidation method combined with spark plasma sintering (SPS). The formation mechanism of the composite coating and its effect on the electro-magnetic properties of Fe–Si SMCs were investigated. The results showed that a uniform Fe2O3 coating can be obtained by reactions between Fe and H2O/O2 during in-situ oxidation process, and became thicker with the increased oxidation time. After sintering, the oxide coating was transformed into a composite coating composed of Fe2SiO4 with excellent insulation and nano-Fe with high ferromagnetism, which resulted from the interfacial reaction between Fe2O3 coating and Fe–Si core. The increased oxidation time led to the gradually thicker composite coating, and resulted in a linear decrease in saturation magnetization, indicating good controllability of the coating. However, excessive oxidation time led to the increased eddy current loss as well as the core loss due to the weakened resistivity. Thus, the Fe–Si SMCs exhibited high saturation magnetic induction (1.66T) and very low core loss (643.9 kW/m3 at 0.1 T/50 kHz) especially when the oxidation time was 1 h

MicroRNA-93 promotes the malignant phenotypes of human glioma cells and induces their chemoresistance to temozolomide

MicroRNAs (miRNAs), a class of small non-coding RNAs, can induce mRNA degradation or repress translation by binding to the 3′-untranslated region (UTR) of its target mRNA. Recently, some specific miRNAs, e.g. miR-93, have been found to be involved in pathological processes by targeting some oncogenes or tumor suppressors in glioma. However, the regulatory mechanism of miR-93 in the biological behaviors and chemoresistance of glioma cells remains unclear. In the present study, in situ hybridization and real-time RT-PCR data indicated that miR-93 was significantly upregulated in glioma patients (n=43) compared with normal brain tissues (n=8). Moreover, the upregulated miR-93 level was significantly associated with the advanced malignancy. We also found that upregulation of miR-93 promoted the proliferation, migration and invasion of glioma cells, and that miR-93 was involved in the regulation of cell cycle progression by mediating the protein levels of P21, P27, P53 and Cyclin D1. P21 was further identified as a direct target of miR-93. Knockdown of P21 attenuated the suppressive effects of miR-93 inhibition on cell cycle progression and colony formation. In addition, inhibition of miR-93 enhanced the chemosensitization of glioma cells to temozolomide (TMZ). Based on these above data, our study demonstrates that miR-93, upregulated in glioma, promotes the proliferation, cell cycle progression, migration and invasion of human glioma cells and suppresses their chemosensitivity to TMZ. Therefore, miR-93 may become a promising diagnostic marker and therapeutic target for glioma