This paper contributes to the AC small signal modeling and analysis of Z source converter (ZSC) in continuous conduction mode. The AC small signal model considers the dynamics introduced by Z network uniquely contained in ZSC. AC small signal model of ZSC is derived and computer simulation results are used to validate the small signal modeling method. Various applications of the AC small signal models to ZSC design and experimental verifications are presented.
The form of the species richness-productivity relationship (SRPR) is both theoretically important and contentious. In an effort to distill general patterns, ecologists have undertaken meta-analyses, within which each SRPR data set is first classified into one of five alternative forms: positive, humped (unimodal), negative, U-shaped (unimodal), and no relationship. Herein, I first provide a critique of this approach, based on 68 plant data sets/studies used in three meta-analyses published in Ecology. The meta-analyses are shown to have resulted in highly divergent outcomes, inconsistent and often highly inappropriate classification of data sets, and the introduction and multiplication of errors from one meta-analysis to the next. I therefore call on the ecological community at large to adopt a far more rigorous and critical attitude to the use of meta-analysis. Second, I develop the argument that the literature on the SRPR continues to be bedeviled by a common failing to appreciate the fundamental importance of the scale of analysis, beginning with the confusion evident between concepts of grain, focus, and extent. I postulate that variation in the form of the SRPR at fine scales of analysis owes much to artifacts of the sampling regime adopted. An improved understanding may emerge from combining sampling theory with an understanding of the factors controlling the form of species abundance distributions and species accumulation curves.
Background: Numerous studies have investigated the relationship between COX-2 8473 T > C polymorphism and cancer susceptibility, however, the results remain controversial. Therefore, we carried out the present meta-analysis to obtain a more accurate assessment of this potential association. Methods: In this meta-analysis, 79 case-control studies were included with a total of 38,634 cases and 55,206 controls. We searched all relevant articles published in PubMed, EMBASE, OVID, Web of Science, CNKI and Wanfang Data, till September 29, 2017. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the strength of the association. We performed subgroup analysis according to ethnicity, source of controls, genotyping method and cancer type. Moreover, Trial sequential analysis (TSA) was implemented to decrease the risk of type I error and estimate whether the current evidence of the results was sufficient and conclusive. Results: Overall, our results indicated that 8473 T > C polymorphism was not associated with cancer susceptibility. However, stratified analysis showed that the polymorphism was associated with a statistically significant decreased risk for nasopharyngeal cancer and bladder cancer, but an increased risk for esophageal cancer and skin cancer. Interestingly, TSA demonstrated that the evidence of the result was sufficient in this study. Conclusion: No significant association between COX-2 8473 T > C polymorphism and cancer risk was detected.
BackgroundPeriodontitis is a major oral health problem and it is considered as one of the reasons for tooth loss in developing and developed nations. The objective of the current review was to investigate the association between IL10 polymorphisms -1082 A>G (rs1800896), -819C>T (rs1800871), -592 A>C (rs1800872) and the risk of either chronic periodontitis or aggressive periodontitis.MethodsThis is a meta- analysis study, following the preferred reporting items for systematic reviews and meta- analyses (PRISMA). Relevant studies were searched in the health related electronic databases. Methodological quality of the included studies were assessed using the Newcastle-Ottawa Scale. For individual studies, odds ratio (OR) and its 95%confidence interval (CI) were calculated to assess the strength of association between IL10 polymorphisms (-1082 A>G, -819C>T, -592 A>C) and the risk of periodontitis. For pooling of the estimates across studies included, the summary OR and its 95% CIs were calculated with random-effects model. The pooled estimates were done under four genetic models such as the allelic contrast model, the recessive model, the dominant model and the additive model. Trial sequential analysis (TSA) was done for estimation of the required information size for this meta-analysis study.ResultsSixteen studies were identified for this review. The included studies were assessed to be of moderate to good methodological quality. A significant association between polymorphism of IL10-1082 A>G polymorphism and the risk of chronic periodontitis in the non-Asian populations was observed only in the recessive model (OR,1.42; 95% CI:1.11, 1.8,I-2: 43%). The significant associations between -592 A>C polymorphism and the risk of aggressive periodontitis in the non-Asian populations were observed in particular genetic models such as allele contrast (OR, 4.34; 95%CI:1.87,10.07,I-2: 65%) and recessive models (OR, 2.1; 95% CI:1.16, 3.82,I-2: 0%). The TSA plot revealed that the required information size for evidence of effect was sufficient to draw a conclusion.ConclusionsThis meta-analysis suggested that the IL10-1082 A>G polymorphism was associated with chronic periodontitis CP risk in non-Asians. Thus, in order to further establish the associations between IL10 (-819 C>T, -592 A>C) in Asian populations, future studies should include larger sample sizes with multi-ethnic groups.
Background: The prevalence of class III obesity (body mass index [BMI]>= 40 kg/m(2)) has increased dramatically in several countries and currently affects 6% of adults in the US, with uncertain impact on the risks of illness and death. Using data from a large pooled study, we evaluated the risk of death, overall and due to a wide range of causes, and years of life expectancy lost associated with class III obesity. Methods and Findings: In a pooled analysis of 20 prospective studies from the United States, Sweden, and Australia, we estimated sex-and age-adjusted total and cause-specific mortality rates (deaths per 100,000 persons per year) and multivariable-adjusted hazard ratios for adults, aged 19-83 y at baseline, classified as obese class III (BMI 40.0-59.9 kg/m(2)) compared with those classified as normal weight (BMI 18.5-24.9 kg/m(2)). Participants reporting ever smoking cigarettes or a history of chronic disease (heart disease, cancer, stroke, or emphysema) on baseline questionnaires were excluded. Among 9,564 class III obesity participants, mortality rates were 856.0 in men and 663.0 in women during the study period (19762009). Among 304,011 normal-weight participants, rates were 346.7 and 280.5 in men and women, respectively. Deaths from heart disease contributed largely to the excess rates in the class III obesity group (rate differences = 238.9 and 132.8 in men and women, respectively), followed by deaths from cancer (rate differences = 36.7 and 62.3 in men and women, respectively) and diabetes (rate differences = 51.2 and 29.2 in men and women, respectively). Within the class III obesity range, multivariable-adjusted hazard ratios for total deaths and deaths due to heart disease, cancer, diabetes, nephritis/nephrotic syndrome/nephrosis, chronic lower respiratory disease, and influenza/pneumonia increased with increasing BMI. Compared with normal-weight BMI, a BMI of 40-44.9, 45-49.9, 50-54.9, and 55-59.9 kg/m(2) was associated with an estimated 6.5 (95% CI: 5.7-7.3), 8.9 (95% CI: 7.4-10.4), 9.8 (95% CI: 7.4-12.2), and 13.7 (95% CI: 10.5-16.9) y of life lost. A limitation was that BMI was mainly ascertained by self-report. Conclusions: Class III obesity is associated with substantially elevated rates of total mortality, with most of the excess deaths due to heart disease, cancer, and diabetes, and major reductions in life expectancy compared with normal weight.
The state-of-art in the lipidomic analysis is summarized here to provide the overview of available sample preparation strategies, mass spectrometry (MS)-based methods for the qualitative analysis of lipids, and the quantitative MS approaches for high-throughput clinical workflows. Major challenges in terms of widely accepted best practices for lipidomic analysis, nomenclature, and standards for data reporting are discussed as well.
Summary Background Patients with peritoneal metastatic colorectal cancer have reduced overall survival compared with patients with metastatic colorectal cancer without peritoneal involvement. Here we further investigated the effect of the number and location of metastases in patients receiving first-line systemic chemotherapy. Methods We analysed individual patient data for previously untreated patients enrolled in 14 phase 3 randomised trials done between 1997 and 2008. Trials were included if protocols explicitly pre-specified and solicited for patients with peritoneal involvement in the trial data collection process or had done a formal peritoneum-focused review of individual pre-treatment scans. We used stratified multivariable Cox models to assess the prognostic associations of peritoneal metastatic colorectal cancer with overall survival and progression-free survival, adjusting for other key clinical-pathological factors (age, sex, Eastern Cooperative Oncology Group (ECOG) performance score, primary tumour location [colon vs rectum], previous treatment, and baseline BMI). The primary endpoint was difference in overall survival between populations with and without peritoneal metastases. Findings Individual patient data were available for 10 553 patients. 9178 (87%) of 10 553 patients had non-peritoneal metastatic colorectal cancer (4385 with one site of metastasis, 4793 with two or more sites of metastasis), 194 (2%) patients had isolated peritoneal metastatic colorectal cancer, and 1181 (11%) had peritoneal metastatic colorectal cancer and other organ involvement. These groups were similar in age, ethnic origin, and use of targeted treatment. Patients with peritoneal metastatic colorectal cancer were more likely than those with non-peritoneal metastatic colorectal cancer to be women (565 [41%] of 1371 vs 3312 [36%] of 9169 patients; p=0·0003), have colon primary tumours (1116 [84%] of 1334 patients vs 5603 [66%]; p<0·0001), and have performance status of 2 (136 [10%] vs 521 [6%]; p<0·0001). We recorded a higher proportion of patients with mutated BRAF in patients with peritoneal-only (eight [18%] of 44 patients with available data) and peritoneal metastatic colorectal cancer with other sites of metastasis (34 [12%] of 289), compared with patients with non-peritoneal metastatic colorectal cancer (194 [9%] of 2230; p=0·028 comparing the three groups). Overall survival (adjusted HR 0·75, 95% CI 0·63–0·91; p=0·003) was better in patients with isolated non-peritoneal sites than in those with isolated peritoneal metastatic colorectal cancer. Overall survival of patients with two of more non-peritoneal sites of metastasis (adjusted HR 1·04, 95% CI 0·86–1·25, p=0.69) and those with peritoneal metastatic colorectal cancer plus one other site of metastasis (adjusted HR 1·10, 95% CI 0·89–1·37, p=0·37) was similar to those with isolated peritoneal metastases. Compared with patients with isolated peritoneal metastases, those with peritoneal metastases and two or more additional sites of metastasis had the shortest survival (adjusted HR 1·40; CI 1·14–1·71; p=0·0011). Interpretation Patients with peritoneal metastatic colorectal cancer have significantly shorter overall survival than those with other isolated sites of metastases. In patients with several sites of metastasis, poor survival is a function of both increased number of metastatic sites and peritoneal involvement. The pattern of metastasis and in particular, peritoneal involvement, results in prognostic heterogeneity of metastatic colorectal cancer. Funding None.
This paper focuses on direct torque control (DTC) for three-phase AC electric drives. A novel model predictive control scheme is proposed that keeps the motor torque, the stator flux, and (if present) the inverter's neutral point potential within given hysteresis bounds while minimizing the switching frequency of the inverter. Based on an internal model of the drive, the controller predicts several future switch transitions, extrapolates the output trajectories, and chooses the sequence of inverter switch positions (voltage vectors) that minimizes the switching frequency. The advantages of the proposed controller are twofold. First, as underlined by the experimental results in the second part of this paper, it yields a superior performance with respect to the industrial state of the art. Specifically, the switching frequency is reduced by up to 50% while the torque and flux are kept more accurately within their bounds. Moreover, the fast dynamic torque response is inherited from standard DTC. Second, the scheme is applicable to a large class of (three-phase) AC electric machines driven by inverters.
Summary Background Malaria is one of the greatest causes of mortality worldwide. Use of the most effective treatments for malaria remains inadequate for those in need, and there is concern over the emergence of resistance to these treatments. In 2010, the Global Fund launched the Affordable Medicines Facility—malaria (AMFm), a series of national-scale pilot programmes designed to increase the access and use of quality-assured artemisinin based combination therapies (QAACTs) and reduce that of artemisinin monotherapies for treatment of malaria. AMFm involves manufacturer price negotiations, subsidies on the manufacturer price of each treatment purchased, and supporting interventions such as communications campaigns. We present findings on the effect of AMFm on QAACT price, availability, and market share, 6–15 months after the delivery of subsidised ACTs in Ghana, Kenya, Madagascar, Niger, Nigeria, Uganda, and Tanzania (including Zanzibar). Methods We did nationally representative baseline and endpoint surveys of public and private sector outlets that stock antimalarial treatments. QAACTs were identified on the basis of the Global Fund's quality assurance policy. Changes in availability, price, and market share were assessed against specified success benchmarks for 1 year of AMFm implementation. Key informant interviews and document reviews recorded contextual factors and the implementation process. Findings In all pilots except Niger and Madagascar, there were large increases in QAACT availability (25·8–51·9 percentage points), and market share (15·9–40·3 percentage points), driven mainly by changes in the private for-profit sector. Large falls in median price for QAACTs per adult equivalent dose were seen in the private for-profit sector in six pilots, ranging from US$1·28 to $4·82. The market share of oral artemisinin monotherapies decreased in Nigeria and Zanzibar, the two pilots where it was more than 5% at baseline. Interpretation Subsidies combined with supporting interventions can be effective in rapidly improving availability, price, and market share of QAACTs, particularly in the private for-profit sector. Decisions about the future of AMFm should also consider the effect on use in vulnerable populations, access to malaria diagnostics, and cost-effectiveness. Funding The Global Fund to Fight AIDS, Tuberculosis and Malaria, and the Bill & Melinda Gates Foundation.
Background: The dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations. Results: We introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza. Conclusions: Analysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.