A Study on the Non-Linear Impact of Digital Technology Innovation on Carbon Emissions in the Transportation Industry

Transportation is an important part of social and economic development and is also a typical high-energy and high-emissions industry. Achieving low-carbon development in the transportation industry is a much-needed requirement and the only way to achieve high-quality development. Therefore, based on the relevant data of 30 provinces in China from 2010 to 2018, this research uses the static panel model, panel threshold model and spatial Durbin model to conduct an empirical study on the impact and mechanism of digital innovation on carbon emissions in the transportation industry, and draws the following conclusions. (1) Carbon emissions in the transportation industry have dynamic and continuous adjustment characteristics. (2) There is a significant inverted U-shape non-linear relationship between the level of digital innovation and carbon emissions in the industry. In regions with a low level of digital innovation, the application of digital technology increases carbon emissions in this industry, but as the level of digital innovation continues to increase its application suppresses carbon emissions, showing an effect of carbon emission reduction. (3) The impact of digital innovation on carbon emissions in the transportation industry has a spatial spillover effect, and its level in one province significantly impacts carbon emissions in other provinces’ transportation industry through the spatial spillover effect. Therefore, it is recommended to further strengthen the exchange and cooperation of digital innovation in the transportation industry between regions, improve the scale of digitalization in this industry, and accelerate its green transformation through digital innovation, thus promoting the green, low-carbon, and sustainable development of China’s economy

Overexpression of PvGF14c from Phyllostachys violascens Delays Flowering Time in Transgenic Arabidopsis

14-3-3 Proteins are a family of highly conserved regulatory molecules expressed in all eukaryotic cells and regulate a diverse set of biological responses in plants. However, their functions in flowering of Phyllostachys violascens are poorly understood. In this study, four non- Pv14-3-3 genes from P. violascens were identified and named PvGF14b, PvGF14c, PvGF14e, and PvGF14f. qRT-PCR analyses revealed that PvGF14b and PvGF14e exhibited widely expressed in all tested bamboo tissues. PvGF14b was highest expression in root and lowest in immature leaf. Whereas PvGF14c and PvGF14f showed tissue-specific expression. PvGF14c was mainly expressed in immature and mature leaves. PvGF14f was highest expression in mature leaves. These four genes were not significantly differentially expressed in mature leaf before bamboo flowering and during flower development. PvGF14b and PvGF14c were not induced by circadian rhythm. PvGF14c displayed subcellular localization in the cytoplasm and PvFT in nucleus and cytoplasm. Yeast two-hybrid screening and bimolecular fluorescence complementation confirmed the interaction between PvGF14c and PvFT. The overexpression of PvGF14b, PvGF14c, and PvGF14e significantly delayed flowering time in transgenic Arabidopsis under long-day condition. These findings suggested that at least three PvGF14 genes are involved in flowering and may act as a negative regulator of flowering by interacting with PvFT in bamboo

Development and validation of a prognostic nomogram model in primary cutaneous and subcutaneous soft tissue angiosarcoma

Objective The current study aimed to investigate the prognosis and treatment of primary cutaneous angiosarcoma (PCA) and primary subcutaneous angiosarcoma (PSCA), and tried to develop a prognostic nomogram model of them. Methods A total of 1763 cases retrieved from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed. Survival analyses were performed to explore the prognosis of patients and the effects of different treatment methods. All data were randomly allocated into a training set and a testing set to develop and validate the nomogram model. Results The findings showed that age, sex, grade, tumor size, multiple primary malignant tumors, stage, primary site surgery (PSS), radiotherapy (RT), and chemotherapy (CT) were correlated with prognosis (p < .05). The nomogram achieved good accuracy in predicting the prognosis. PSS + RT + CT showed the best prognosis for patients in stages I, II, and III (p < .05). Conclusion PCA and PSCA are rare with poor prognoses. Patients undergoing PSS may not gain survival benefits from combining with RT or (and) CT, whereas PSS + RT + CT should be actively performed in earlier stages to improve the prognosis of patients. The nomogram model can be used to predict the overall survival rate and guide better treatment

Clinical application, evaluation and analysis of influencing factors analysis of on tuberculosis ?-Interferon enzyme-linked immunosorbent assay

Objective: To assess sensitivity and specificity of the interferon gamma release assay test, and to pinpoint the influencing factors that should be taken care of in clinical application. Methods: The study was conducted at the First People's Hospital in the Yunnan Province of China from October 2018 to March 2019, and comprised samples collected from outpatient and inpatients. To detect mycobacterium tuberculosis, acid-fast staining on sputum smear was performed on relevant tissues suspected of extrapulmonary tuberculosis. Tuberculosis interferon gamma release assay test and pathology samples were examined. Tuberculosis-specific cell reaction assay kit was used for sampling. SysmexXN-2000 haematology analyser, VACUETTE SRS100/II and Beckman Coulter AU5800 were used to perform various analyses. Data was grouped and analysed using R statistical software. Results: Of the 960 samples, 516(53.75%) cases tested positive for tuberculosis infection and 444(46.25%) tested negative. The sensitivity of the pathological results was 86% and the specificity was 96%. The sensitivity of the interferon gamma release assay test was 95% and specificity 82%. Interferon release test, pathological results and final diagnosis showed significant comparisons (p<0.05). Significant relationships were also established for factors, such as age, interferon release quantity, lymphocyte, C-reactive protein and counts of mono-nuclear cell (p<0.05). Conclusions: Interferon gamma release assay test was found to have high consistency with pathological results and final diagnosis and can be used as a subsidiary to traditional clinical imaging and pathological judgment. Key Words: Tuberculosis, Interferon release, CRP, Hormone use. Continuous...

CE-based white-box adversarial attacks will not work using super-fitting

Deep neural networks are widely used in various fields because of their powerful performance. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks, i.e., adding a slight perturbation to the input will make the model obtain wrong results. This is especially dangerous for some systems with high-security requirements, so this paper proposes a new defense method by using the model super-fitting state to improve the model's adversarial robustness (i.e., the accuracy under adversarial attacks). This paper mathematically proves the effectiveness of super-fitting and enables the model to reach this state quickly by minimizing unrelated category scores (MUCS). Theoretically, super-fitting can resist any existing (even future) CE-based white-box adversarial attacks. In addition, this paper uses a variety of powerful attack algorithms to evaluate the adversarial robustness of super-fitting, and the proposed method is compared with nearly 50 defense models from recent conferences. The experimental results show that the super-fitting method in this paper can make the trained model obtain the highest adversarial robustness

Rethinking Classifier and Adversarial Attack

Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i.e., not approaching the lower bound of robustness). To solve this problem, this paper uses the proposed decouple space method to divide the classifier into two parts: non-linear and linear. Then, this paper defines the representation vector of the original example (and its space, i.e., the representation space) and uses the iterative optimization of Absolute Classification Boundaries Initialization (ACBI) to obtain a better attack starting point. Particularly, this paper applies ACBI to nearly 50 widely-used defense models (including 8 architectures). Experimental results show that ACBI achieves lower robust accuracy in all cases

Overexpression of PvCO1, a bamboo CONSTANS-LIKE gene, delays flowering by reducing expression of the FT gene in transgenic Arabidopsis

Abstract Background In Arabidopsis, a long day flowering plant, CONSTANS (CO) acts as a transcriptional activator of flowering under long day (LD) condition. In rice, a short day flowering plant, Hd1, the ortholog of CO, plays dual functions in respond to day-length, activates flowering in short days and represses flowering in long days. In addition, alleles of Hd1 account for ~ 44% of the variation in flowering time observed in cultivated rice and sorghum. How does it work in bamboo? The function of CO in bamboo is similar to that in Arabidopsis? Results Two CO homologous genes, PvCO1 and PvCO2, in Phyllostachys violascens were identified. Alignment analysis showed that the two PvCOLs had the highest sequence similarity to rice Hd1. Both PvCO1 and PvCO2 expressed in specific tissues, mainly in leaf. The PvCO1 gene had low expression before flowering, high expression during the flowering stage, and then declined to low expression again after flowering. In contrast, expression of PvCO2 was low during the flowering stage, but rapidly increased to a high level after flowering. The mRNA levels of both PvCOs exhibited a diurnal rhythm. Both PvCO1 and PvCO2 proteins were localized in nucleus of cells. PvCO1 could interact with PvGF14c protein which belonged to 14–3-3 gene family through B-box domain. Overexpression of PvCO1 in Arabidopsis significantly caused late flowering by reducing the expression of AtFT, whereas, transgenic plants overexpressing PvCO2 showed a similar flowering time with WT under LD conditions. Taken together, these results suggested that PvCO1 was involved in the flowering regulation, and PvCO2 may either not have a role in regulating flowering or act redundantly with other flowering regulators in Arabidopsis. Our data also indicated regulatory divergence between PvCOLs in Ph. violascens and CO in Arabidopsis as well as Hd1 in Oryza sativa. Our results will provide useful information for elucidating the regulatory mechanism of COLs involved in the flowering. Conclusions Unlike to the CO gene in Arabidopsis, PvCO1 was a negative regulator of flowering in transgenic Arabidopsis under LD condition. It was likely that long period of vegetative growth of this bamboo species was related with the regulation of PvCO1

The impact of long-term aspirin use on the patients undergoing shoulder arthroplasty

Abstract Background Although aspirin is increasingly utilized to reduce the event of severe perioperative complications, the effect of long-term aspirin use (L-AU) on perioperative complications in patients undergoing shoulder arthroplasty (SA) has not been well studied. The goal of the present study is to identify the influence of L-AU on perioperative complications in individuals undergoing SA. Methods We selected data from the National Inpatient Sample database between 2010 and 2019, to identify adult patients with SA. Patients were subsequently categorized into L-AU and whole non-L-AU cohorts according to the presence of aspirin use. The demographic and comorbidity characteristics were matched using propensity score matching (PSM). The Pearson chi-square test, Wilcoxon rank test and logistic regression were utilized to assess the association of L-AU with perioperative complications. Results From 2010 to 2019, a total of 162,418 SA patients satisfied the inclusion criteria, with 22,659 (13.95%) using aspirin on a long-term basis. The vast majority of the patients with pre-existing L-AU were aged 65–74 years, female, White and had Medicare insurance. L-AU before surgery was linked to increased risks of perioperative complications, such as blood transfusion (adjusted odds ratio [aOR]: 1.339), genitourinary disease (aOR: 1.349), acute renal failure (aOR: 1.292), acute myocardial infarction (aOR: 1.494), higher total charge (L-AU vs. the whole non-L-AU vs. matched non-L-AU: $66,727.15 vs.$59,697.08 vs. $59,926.32), and prolonged hospitalization stay (LOS) (aOR: 0.837). However, L-AU was considered a protective factor of acute cerebrovascular disease (aOR: 0.722) and stroke (aOR: 0.725). Conclusions Our study is based on the largest open-access all-payer inpatient database, revealing a noteworthy finding of aspirin's protective and adverse impact on different postoperative complications in the US population, such as acute cardiovascular disease, and stroke, etc. Further studies assessing the optimum preoperative aspirin duration and dosage to meet the best benefit quantity for patients with planned joint arthroplasties are suggested