Land degradation is always with us but its causes, extent and severity are contested. We define land degradation as a long-term decline in ecosystem function and productivity, which may be assessed ...using long-term, remotely sensed normalized difference vegetation index (NDVI) data. Deviation from the norm may serve as a proxy assessment of land degradation and improvement - if other factors that may be responsible are taken into account. These other factors include rainfall effects which may be assessed by rain-use efficiency, calculated from NDVI and rainfall. Results from the analysis of the 23-year Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data indicate declining rain-use efficiency-adjusted NDVI on ca. 24% of the global land area with degrading areas mainly in Africa south of the equator, South-East Asia and south China, north-central Australia, the Pampas and swaths of the Siberian and north American taiga; 1.5 billion people live in these areas. The results are very different from previous assessments which compounded what is happening now with historical land degradation. Economic appraisal can be undertaken when land degradation is expressed in terms of net primary productivity and the resultant data allow statistical comparison with other variables to reveal possible drivers.
Crop segmentation from the images taken in the outdoor fields is a complex task. In this paper, a new morphology modeling method is utilized to establish the crop color model in the CIE L*a*b* (or ...Lab for simplification) color space and to realize the crop image segmentation. In the supervised learning stage, morphology modeling is applied to deal with the color characteristics of the crop with respect to the pixel lightness component and establish the crop color model. To verify the performance of the proposed method, 56 test images which in size of 601×601 and taken from April 27, 2011 to May 21, 2011 are utilized to compare the proposed method with eight other famous approaches. Experiment shows that the segmentation quality of the proposed method is approximately 87.2% for the Automatic Target Recognition Working Group (ATRWG) evaluation method and 96.0% for another evaluation method. Moreover, the segmentation performance for images taken on cloudy, overcast and sunny days is analyzed. Experiment demonstrates that our method is robust to the variation of illumination in the field and performed better than eight other approaches. Furthermore, the impact of different structuring element types to the proposed method is compared. Overall, the proposed crop segmentation method can be used to crop segmentation in the field effectively.
To identify the association between the expression of lncRNA NEAT1 and clinicopathological characteristics of patients with HCC, and to explore the prognostic significance of lncRNA NEAT1 in ...predicting prognosis of HCC.
We retrospectively reviewed 86 patients with HCC (35 female, 51 male) managed in our institution between 2009 and 2014. The expression level of lncRNA NEAT1 was detected by real-time PCR. Prognostic factors were evaluated using Kaplan–Meier curves and Cox proportional hazards models.
For the entire cohort of 86 patients, we showed that the expression level of NEAT1 was significantly higher in HCC tissues compared with non-tumorous tissues and NEAT1 was increased obviously in the HCC cell lines including SMMC-7721, Huh-7 and Hep3B (P < 0.001). MTT assay showed that si-NEAT1 remarkably inhibited the cell proliferation in three HCC cell lines. Moreover, over-expression of lncRNA NEAT1 was closely related to liver cirrhosis (P = 0.026), microvascular invasion (MVI) (P = 0.023), and TNM stage (P = 0.017). After adjusting for competing risk factors, we identified that expression level of lncRNA NEAT1 was an independently risk factor associated with the prognosis of patients with HCC (P = 0.031).
In this study, we found NEAT1 expressed significantly higher in HCC tissues compared with non-tumorous tissues. Overexpression of lncRNA NEAT1 was an independently risk factor associated with the prognosis of patients with HCC.
To analyze the risk factors for progression of urolith associated with obstructive urosepsis to severe sepsis or septic shock, we had done the retrospective cross-sectional study, which would ...facilitate the early identification of high-risk patients.
Datas were retrospectively reviewed from 160 patients, suffering from obstructive urosepsis associated with urolith between December 2013 and December 2019. There were 49 patients complicating by severe sepsis (severe sepsis group), 12 patients complicating by septic shock (septic shock group), and 99 patients without progressing to severe sepsis or septic shock (sepsis group). The data covered age, gender, BMI (body mass index), time interval from ED (emergency department) to admission, WBC count (white blood cell count), NLR (neutrophil/lymphocyte ratio), HGB (hemoglobin), etc. Datas were analyzed by univariate analyses and multivariate logistic regression analysis. The corresponding nomogram prediction model was drawn according to the regression coefficients.
Univariate analysis showed that the differences of age, the time interval from ED to admission, history of diabetes mellitus, history of CKI (chronic kidney disease), NLR, HGB, platelet count, TBil (total bilirubin), SCr (serum creatinine), ALB (albumin), PT (prothrombin time), APTT (activated partial thromboplastin time), INR (international normalized ratio), PCT (procalcitonin), and positive rate of pathogens in blood culture were statistically significant (P < 0.05). Multivariatelogistic regression analysis showed that age, SCr, and history of CKI were independent risk factors for progression to severe sepsis, or septic shock (P < 0.05).
Aged ≥ 65 years, SCr ≥ 248 mol/L, and history of CKI were independent risk factors for progression of urolith associated with obstructive urosepsis to severe sepsis or septic shock. We need to pay more attention to these aspects, when coming across the patients with urolithic sepsis.
This study reports the most comprehensive data set thus far of surface seawaterpCO2 (partial pressure of CO2) and the associated air–sea CO2 fluxes in a major ocean margin, the East China Sea (ECS), ...based on 24 surveys conducted in 2006 to 2011. We showed highly dynamic spatial variability in sea surface pCO2 in the ECS except in winter, when it ranged across a narrow band of 330 to 360 µatm. We categorized the ECS into five different domains featuring with different physics and biogeochemistry to better characterize the seasonality of the pCO2 dynamics and to better constrain the CO2 flux. The five domains are (I) the outer Changjiang estuary and Changjiang plume, (II) the Zhejiang–Fujian coast, (III) the northern ECS shelf, (IV) the middle ECS shelf, and (V) the southern ECS shelf. In spring and summer, pCO2 off the Changjiang estuary was as low as < 100 µatm, while it was up to > 400 µatm in autumn. pCO2 along the Zhejiang–Fujian coast was low in spring, summer and winter (300 to 350 µatm) but was relatively high in autumn (> 350 µatm). On the northern ECS shelf, pCO2 in summer and autumn was > 340 µatm in most areas, higher than in winter and spring. On the middle and southern ECS shelf, pCO2 in summer ranged from 380 to 400 µatm, which was higher than in other seasons (< 350 µatm). The area-weighted CO2 flux on the entire ECS shelf was-10.0 ± 2.0 in winter, -11.7 ± 3.6 in spring, -3.5 ± 4.6 in summer and -2.3 ± 3.1 mmol m-2 d-1 in autumn. It is important to note that the standard deviations in these flux ranges mostly reflect the spatial variation in pCO2 rather than the bulk uncertainty. Nevertheless, on an annual basis, the average CO2 influx into the entire ECS shelf was 6.9 ± 4.0 mmol m-2 d-1, about twice the global average in ocean margins.
Today most research related to manufacturing strategy development concentrates on descriptive processes and conceptual models, and therefore lacks the capability of assessing the supportive degree of ...manufacturing strategy to competitive priorities, and is also difficult to assess the congruence among various decisions of the manufacturing strategy. This paper proposes an approach for manufacturing strategy development based on quality function deployment (QFD). The study starts by analyzing the process of manufacturing strategy development and the features of QFD. Thereafter, a methodology related to manufacturing strategy development based on QFD is developed, which comprises two stages and eleven steps. This approach uses QFD as a transforming device to link competitive factors with manufacturing decision categories such as structural decision categories and infrastructural categories, and uses QFD as a main tool in different stages of manufacturing strategy development process. This paper also integrates fuzzy set theory and house of quality (HOQ) in order to provide a structural tool to capture the inherent imprecision and vagueness of decision-relevant inputs and to facilitate the analysis of decision-relevant QFD information. A case is given to illustrate the utilization of the proposed approach at the end of this paper.
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