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Electricity consumption is an important economic index and plays a significant role in drawing up an energy development policy for each country. Multivariate techniques and time-series analysis have been proposed to deal with electricity consumption forecasting, but a large amount of historical data is required to obtain accurate predictions. The grey forecasting model attracted researchers by its ability to characterize an uncertain system effectively with a limited number of samples. GM(1,1) is the most frequently used grey forecasting model, but its developing coefficient and control variable were dependent on the background value that is not easy to be determined, whereas a neural-network-based GM(1,1) model called NNGM(1,1) has been presented to resolve this troublesome problem. This study has applied NNGM(1,1) to electricity consumption and has examined its forecasting ability on electricity consumption using sample data from the Turkish Ministry of Energy and Natural Resources and the Asia–Pacific Economic Cooperation energy database. Experimental results demonstrate that NNGM(1,1) performs well.

يتناول كتاب (ماوتسي يونج) وهو صاحب سيرة طويلة عبر ما يقرب من سبعين عاما، نشأ في ريف الصين لأب فلاح فقير، استهوته الماركسية فانتمى إليها، ثم صار أحد نجومها في الحزب الشيوعي الصيني، ثم صار رئيسا للحزب، ثم استقل بعرش الصين وجلس عليه حوالي ثلاثين عاما، قاد فيها الصين برؤيته الخاصة فصنع منها دولة قوية في وقت وجيز، ولا يزال كتابه الأحمر مرجعا أساسيا للفكر الصيني والسياسة الصينية، بل إن دواوين شعره هي الأكثر مبيعا في الصين، ولا تزال سيرته مقصد كثير من المطلعين.‪

In clinical settings, physicians tend to use corticosteroids in the most critically ill patients. [...]selection bias and confounders in observational studies might contribute to any observed increased mortality in patient groups treated with corticosteroids. Inconclusive clinical evidence should not be a reason for abandoning corticosteroid use in 2019-nCoV pneumonia. [...]there are studies supporting the use of corticosteroids at low-to-moderate dose in patients with coronavirus infection. According to the expert consensus statement, the following basic principles should be followed when using corticosteroids: (1) the benefits and harms should be carefully weighed before using corticosteroids; (2) corticosteroids should be used prudently in critically ill patients with 2019-nCoV pneumonia; (3) for patients with hypoxaemia due to underlying diseases or who regularly use corticosteroids for chronic diseases, further use of corticosteroids should be cautious; and (4) the dosage should be low-to-moderate (≤0·5–1 mg/kg per day methylprednisolone or equivalent) and the duration should be short (≤7 days).

Tourism demand forecasting has played an important role in supporting governments to devise development policies for travel and tourism. However, time series related to tourism often do not conform to statistical assumptions and feature significant temporal fluctuations. Because a Fourier series is often applied to oscillating sequences to remove noise, it is reasonable to develop a grey prediction model in conjunction with a Fourier series to forecast tourism demand. However, grey prediction models traditionally use one-order accumulation, treating each sample with equal weight, to identify regularities concealed in data sequences. Furthermore, when generating residuals from Fourier series, the prediction accuracy of the newly generated predicted values is not taken into account. In this study, by using fractional order accumulation to assign appropriate weights to samples, we propose a fractional grey prediction model with Fourier series that offers high prediction accuracy. Experimental results demonstrate that the proposed grey prediction model performs well compared with other considered prediction models.

Radiologic characteristics of 2019 novel coronavirus (2019-nCoV) infected pneumonia (NCIP) which had not been fully understood are especially important for diagnosing and predicting prognosis. We retrospective studied 27 consecutive patients who were confirmed NCIP, the clinical characteristics and CT image findings were collected, and the association of radiologic findings with mortality of patients was evaluated. 27 patients included 12 men and 15 women, with median age of 60 years (IQR 47-69). 17 patients discharged in recovered condition and 10 patients died in hospital. The median age of mortality group was higher compared to survival group (68 (IQR 63-73) vs 55 (IQR 35-60), P = 0.003). The comorbidity rate in mortality group was significantly higher than in survival group (80% vs 29%, P = 0.018). The predominant CT characteristics consisted of ground glass opacity (67%), bilateral sides involved (86%), both peripheral and central distribution (74%), and lower zone involvement (96%). The median CT score of mortality group was higher compared to survival group (30 (IQR 7-13) vs 12 (IQR 11-43), P = 0.021), with more frequency of consolidation (40% vs 6%, P = 0.047) and air bronchogram (60% vs 12%, P = 0.025). An optimal cutoff value of a CT score of 24.5 had a sensitivity of 85.6% and a specificity of 84.5% for the prediction of mortality. 2019-nCoV was more likely to infect elderly people with chronic comorbidities. CT findings of NCIP were featured by predominant ground glass opacities mixed with consolidations, mainly peripheral or combined peripheral and central distributions, bilateral and lower lung zones being mostly involved. A simple CT scoring method was capable to predict mortality.

Our study aimed to determine how lncRNA DANCR, miR-320a, and CTNNB1 interact with each other and regulate osteogenic differentiation in osteoporosis. qRT-PCR and western blotting were performed to determine the expression of DANCR, miR-320a, CTNNB1, and the osteoporosis- or Wnt/β-catenin pathway-related markers T-cell factor 1 (TCF-1), runt-related transcription factor 2 (RUNX2), alkaline phosphatase (ALP), osteocalcin (OCN), and osteopontin (OPN). Interactions between CTNNB1, DANCR, and miR-320a were predicted by bioinformatics approaches and validated using a luciferase assay. Osteoblastic phenotypes were evaluated by ALP staining, ALP activity assay and Alizarin Red staining. The bilateral ovariectomy method was used to establish an in vivo osteoporosis model. Bone morphological changes were examined using hematoxylin and eosin (H&E) and Alcian Blue staining. The expression levels of DANCR and miR-320a in BMSCs derived from osteoporosis patients were upregulated, whereas CTNNB1 expression was downregulated compared with that in healthy controls. Importantly, we demonstrated that miR-320a and DANCR acted independently from each other and both inhibited CTNNB1 expression, whereas the inhibitory effect was additive when miR-320a and DANCR were cooverexpressed. Moreover, we found that DANCR overexpression largely abrogated the effect of the miR-320a inhibitor on CTNNB1 expression and the Wnt/β-catenin signaling pathway in BMSCs during osteogenic differentiation. We further confirmed the results above in BMSCs derived from an osteoporosis animal model. Taken together, our findings revealed that DANCR and miR-320a regulated the Wnt/β-catenin signaling pathway during osteogenic differentiation in osteoporosis through CTNNB1 inhibition. Our results highlight the potential value of DANCR and miR-320a as promising therapeutic targets for osteoporosis treatment.Osteoporosis: Tiny targets to keep bones strongTwo non-coding RNAs are potential targets for reducing bone loss in post-menopausal osteoporosis. Bones are constantly being remodeled; when resorption outpaces generation of new bone, bones are weakened, causing osteoporosis and leading to decreased quality of life and injuries. Although treatments exist, they often have undesirable side effects, and new treatments are needed. The molecular basis of the changes that accompany osteoporosis are poorly understood. Da Zhong at the Xiangya Hospital of Central South University in Changsha, China, and co-workers investigated how two non-coding RNAs, small molecules that regulate gene expression, are involved in the progression of post-menopausal osteoporosis. They found that levels of both molecules are increased in osteoporosis, and that silencing them increases building of new bone, key to maintaining bone strength. These results illuminate a potential new direction in treatments for osteoporosis.

Grey prediction models play a significant role in forecasting energy demand, particularly the GM(1,1) model. To increase the prediction accuracy of the original GM(1,1) model, the corresponding residual GM(1,1) model is often recommended. However, the original and residual models that form the basis of the remnant grey prediction model are usually set up independently. In this work, we use a neural network to determine the degree to which a predicted value obtained from the original GM(1,1) model can be modified. A distinctive feature of our proposed prediction model is that the residual model is leveraged by providing a new adjustment mechanism for predicted values to maximize the prediction accuracy. The independent creation of a residual model is no longer required for the proposed model. The prediction accuracy of the proposed prediction models is verified using real energy demand cases. Experimental results showed that the proposed remnant GM(1,1) models perform well in comparison with other remnant GM(1,1) variants.

Background: There is controversy over whether use of new oral anticoagulants (NOACs) associates with increased hemorrhage risk compared with non-NOAC. Meanwhile, determining which NOAC to use remains unclear. We aimed to summarize the evidence about NOACs in venous thromboembolism (VTE) prevention for patients with total hip and knee arthroplasty (THA and TKA). Methods: We searched RCTs assessing NOACs for VTE prophylaxis in adults undergoing THA and TKA in Medline, Embase, and Cochrane up to May 2021. Primary outcomes were VTE [included deep vein thrombosis (DVT) and pulmonary embolism (PE)], major VTE, and major bleeding. The rank probabilities of each treatment were summarized by the surface under the cumulative ranking curve area (SUCRA). Results: 25 RCTs with 42,994 patients were included. Compared with non-NOAC, NOACs were associated with a decreased risk of VTE (RR 0.68; 95% CI 0.55–0.84) and major VTE (RR = 0.52; 95% CI 0.35–0.76). Additionally, rivaroxaban, apixaban, and edoxaban but not dabigatran and betrixaban, did confer a higher efficacy compared with non-NOAC. None of the individual NOACs increased the risk of bleeding, while apixaban and betrixaban were even associated with a decreased risk of bleeding. In the comparison of different NOACs, rivaroxaban was associated with the greatest benefits in VTE (SUCRA = 79.6), DVT (SUCRA = 88.8), and major VTE (SUCRA = 89.9) prevention. Furthermore, subgroup analysis confirmed that NOACs associated with a higher efficacy tendency in patients with follow-up duration <60 days than follow-up duration ≥60 days. Conclusion: Evidence suggests that NOACs exert more benefits on VTE prophylaxis, and none of the individual NOACs increased hemorrhage compared with non-NOAC. Among various NOACs, rivaroxaban is recommended in patients with lower bleeding risk, and apixaban is recommended in patients with higher bleeding risk. Systematic Review Registration : [ https://www.crd.york.ac.uk/prospero/ ], identifier [CRD42021266890].

Regarding bankruptcy prediction as a kind of grey system problem, this study aims to develop multivariate grey prediction models based on the most representative GM(1, N ) for bankruptcy prediction. There are several distinctive features of the proposed grey prediction model. First, to improve the prediction performance of the GM(1, N ), grey relational analysis is used to sift relevant features that have the strongest relationship with the class feature. Next, the proposed model effectively extends the multivariate grey prediction model for time series to bankruptcy prediction irrespective of time series. It turns out that the proposed model uses the genetic algorithms to avoid indexing by time and using the ordinary least squares with statistical assumptions for the traditional GM(1, N ). The empirical results obtained from the financial data of Taiwanese firms in the information and technology industry demonstrated that the proposed prediction model performs well compared with other GM(1, N ) variants considered.