Investment efficiency of the new energy industry in China

This paper evaluates the investment efficiency of the new energy industry in China and investigates factors that explain variations in investment efficiency across firms and over time. Applying a four-stage semi-parametric DEA analysis framework to a sample of listed new energy firms over the period 2012-2015, we find that the overall investment efficiency of the new energy industry is relatively low, with an average total technical efficiency of 44%, pure technical efficiency of 48%, and scale efficiency of 90%. We also find that new energy firms’ investment efficiency is affected by both macroeconomic conditions and firm-specific characteristics. Our results are robust and have significant implications for policy makers and firm managers

Direct yaw-moment control of electric vehicles based on adaptive sliding mode

The direct yaw-moment control (DYC) system consisting of an upper controller and a lower controller is developed on the basis of sliding mode theory and adaptive control technique. First, the two-degree of freedom (2-DOF) model is utilized to calculate the ideal yaw rate. Then, the seven-degree of freedom (7-DOF) electric vehicle model is given to design the upper controller by employing first-order sliding mode (FOSM) method, which is constructed to guarantee the actual yaw rate to approach the ideal value and gain the additional yaw moment. On this basis, an adaptive first-order sliding mode (AFOSM) controller is designed to enhance the system robustness against probable modelling error and parametric uncertainties. In order to mitigate the chattering issue present in the FOSM controller, a novel adaptive super-twisting sliding mode (ASTSM) controller is proposed for the design of DYC. Furthermore, the lower controller converting the additional yaw moment into driving or braking torque acting on each wheel is also developed. Finally, The simulation results indicate that the proposed DYC system can improve the electric vehicle driving stability effectively

Genomic agonism and phenotypic antagonism between estrogen and progesterone receptors in breast cancer

The functional role of progesterone receptor (PR) and its impact on estrogen signaling in breast cancer remain controversial. In primary ER+ (estrogen receptor-positive)/PR+ human tumors, we report that PR reprograms estrogen signaling as a genomic agonist and a phenotypic antagonist. In isolation, estrogen and progestin act as genomic agonists by regulating the expression of common target genes in similar directions, but at different levels. Similarly, in isolation, progestin is also a weak phenotypic agonist of estrogen action. However, in the presence of both hormones, progestin behaves as a phenotypic estrogen antagonist. PR remodels nucleosomes to noncompetitively redirect ER genomic binding to distal enhancers enriched for BRCA1 binding motifs and sites that link PR and ER/PR complexes. When both hormones are present, progestin modulates estrogen action, such that responsive transcriptomes, cellular processes, and ER/PR recruitment to genomic sites correlate with those observedwith PR alone, but not ER alone. Despite this overall correlation, the transcriptome patterns modulated by dual treatment are sufficiently different from individual treatments, such that antagonism of oncogenic processes is both predicted and observed. Combination therapies using the selective PRmodulator/antagonist (SPRM) CDB4124 in combination with tamoxifen elicited 70% cytotoxic tumor regression of T47D tumor xenografts, whereas individual therapies inhibited tumor growth without net regression. Our findings demonstrate that PR redirects ER chromatin binding to antagonize estrogen signaling and that SPRMs can potentiate responses to antiestrogens, suggesting that cotargeting of ER and PR in ER+/PR+ breast cancers should be explored

Combined early palliative care for non-small-cell lung cancer patients: a randomized controlled trial in Chongqing, China

PurposeMore effective approaches are needed to improve the prognosis of non-small-cell lung cancer (NSCLC) patients. Thus, we used the E-warm model to assess how early integration of interdisciplinary palliative care was related to the quality of life (QoL), psychological functioning, pain management, and nutrition factors of NSCLC patients.MethodsThis randomized controlled trial enrolled 280 newly diagnosed NSCLC patients, which were randomly divided (1:1) into combined early palliative care (CEPC) and standard oncological care (SC) groups. At baseline and after 24 weeks, the Functional Assessment of Cancer Therapy-Lung (FACT-L) scale, Hospital Anxiety and Depression Scale (HADS), and the Patient Health Questionnaire-9 (PHQ-9) were used to assess QoL and psychological function, respectively. The Numerical Rating Scale (NRS) and Patient-Generated Subjective Global Assessment (PG-SGA) were used to assess cancer patients’ pain and nutrition levels. The primary outcome was overall survival (OS). Secondary outcomes comprised changes in the QoL, psychological functioning, pain, and nutrition state. The intention-to-treat method was applied for analysis. This study was registered at www.chictr.org.cn (ChiCTR2200062617).ResultsOf the 140 patients enrolled in the CEPC and SC groups, 102 and 82 completed the research. The CEPC group presented higher QoL than the SC group (p < 0.05). Additionally, fewer patients presented depressive symptoms in the CEPC group than in the SC group (p < 0.05), as well as better nutritional status (p = 0.007) and pain management (p = 0.003). Compared to the SC group, CEPC patients had significantly longer OS (20.4 vs. 24.6 months, p = 0.042; HR: 0.19; 95% CI: 0.04-0.85, p = 0.029).ConclusionWith combined early palliative care, NSCLC patients lived longer, had better QoL, were psychologically stable, were in less pain, and were more nutritionally satisfied

A inovação aberta no processo de internacionalização de empresas: estudo de caso da Brasil Foods

TCC (graduação) - Universidade Federal de Santa Catarina. Centro Sócio-Econômico. Relações Internacionais.A presente monografia tem como objetivo o estudo do papel da inovação aberta no processo de internacionalização de empresas, a partir da revisão teórica dos conceitos na literatura e de um estudo de caso real de uma empresa brasileira de grande porte: a Brasil Foods. A presente pesquisa possui caráter de pesquisa exploratória. Para desenvolver o objetivo principal, o trabalho apresenta três objetivos específicos, que são: primeiro apresentar o conceito de inovação, seus graus de inserção e destacar a sua relevância no setor empresarial; segundo apresentar o conceito de inovação aberta e de inovação fechada e esclarecer a importância da difusão de informações; e, terceiro, apresentar os aspectos históricos da internacionalização de empresas, introduzindo duas teorias do processo: Modelo de Uppsala e Perspectiva de Networks. Assim, pode-se exibir, portanto, um modelo conceitual às relações entre as atividades de inovação aberta e a internacionalização de empresas em redes, levandose em consideração que a gestão de inovação nas empresas, atualmente, transcende a visão de inovação tecnológica, e, as redes internacionais ganham cada vez mais relevância como vantagem competitiva nas empresas ao atuar em mercados exteriores. Como resultado, concluiu-se que as estratégias de internacionalização de empresas em redes e as estratégias de inovação aberta, quando empregadas juntas, aumentam a velocidade de aprendizagem organizacional da Brasil Foods, acelerando os processos de internacionalização, confirmando que a inovação aberta estimula e intensifica a internacionalização de empresas que trabalham em redes

A patient-derived explant (PDE) model of hormone-dependent cancer.

Breast and prostate cancer research to date has largely been predicated on the use of cell lines in vitro or in vivo. These limitations have led to the development of more clinically relevant models, such as organoids or murine xenografts that utilize patient-derived material; however, issues related to low take rate, long duration of establishment, and the associated costs constrain use of these models. This study demonstrates that ex vivo culture of freshly resected breast and prostate tumor specimens obtained from surgery, termed patient-derived explants (PDEs), provides a high-throughput and cost-effective model that retains the native tissue architecture, microenvironment, cell viability, and key oncogenic drivers. The PDE model provides a unique approach for direct evaluation of drug responses on an individual patient's tumor, which is amenable to analysis using contemporary genomic technologies. The ability to rapidly evaluate drug efficacy in patient-derived material has high potential to facilitate implementation of personalized medicine approaches.Cancer Research UK and ERC

Minimum Sample Size Estimate for Classifying Invasive Lung Adenocarcinoma

Statistical Learning Theory (SLT) plays an important role in prediction estimation and machine learning when only limited samples are available. At present, determining how many samples are necessary under given circumstances for prediction accuracy is still an unknown. In this paper, the medical diagnosis on lung cancer is taken as an example to solve the problem. Invasive adenocarcinoma (IA) is a main type of lung cancer, often presented as ground glass nodules (GGNs) in patient’s CT images. Accurately discriminating IA from non-IA based on GGNs has important implications for taking the right approach to treatment and cure. Support Vector Machine (SVM) is an SLT application and is used to classify GGNs, wherein the interrelation between the generalization and the lower bound of necessary sampling numbers can be effectively recovered. In this research, to validate the interrelation, 436 GGNs were collected and labeled using surgical pathology. Then, a feature vector was constructed for each GGN sample through the fully connected layer of AlexNet. A 10-dimensional feature subset was then selected with the p-value calculated using Analysis of Variance (ANOVA). Finally, four sets with different sample sizes were used to construct an SVM classifier. Experiments show that a theoretical estimate of minimum sample size is consistent with actual values, and the lower bound on sample size can be solved under various generalization requirements

A Radiomics Approach Based on Follow-Up CT for Pathological Subtypes Classification of Pulmonary Ground Glass Nodules

Preoperative, non-invasive, and accurate identification of the pathological subtypes of pulmonary ground glass nodules (GGNs) play an important role in the precise selection of clinical surgical operations and individualized treatment plans. Efforts have been made for the classification of pathological subtypes of GGNs, but most existing methods focus on benign or malignant diagnosis of GGNs by means of a one-time computed tomography image (CTI), which fails to capture the nodule development based on follow-up CTI. In this paper, a novel method for subtype classification based on follow-up CTIs is presented as a viable option to the existing one-time CTI-based approach. A total of 383 follow-up CTIs with GGNs from 146 patients was collected and retrospectively labeled via posterior surgical pathology. Feature extraction is performed individually to the follow-up CTIs. The extracted feature differences were represented as a vector, which was then used to construct a set of vectors for all the patients. Finally, a subspace K-nearest neighbor classifier was built to predict the pathological subtypes of GGNs. Experimental validation confirmed the efficacy of the new method over the existing method. Results showed that the accuracy of the new method could reach 72.5%, while the existing methods had an upper bound of 67.5% accuracy. Subsequent three-category comparison experiments were also performed to demonstrate that the new method could increase the accuracy up to 21.33% compared to the existing methods that use one-time CTI