Research landscape of energy transition and green finance: A bibliometric analysis.

This study utilizes bibliometric analysis to examine historical and present research patterns in the area of energy transition and green finance and to forecast potential future domains. Using the bibliometric method, 328 scholarly articles from the Web of Science database were evaluated. This paper identifies influential publications, maps the research landscape, and forecasts emerging tendencies through co-citation and co-word analyses. Co-citation analysis found three main clusters, while co-word analysis revealed four main clusters. Despite the growing significance of research on energy transition and green finance research, further in-depth investigation is necessary to offer a thorough depiction of the research domain. This research represents a pioneering endeavour in the utilization of bibliometric analysis to investigate the interrelationship between two items. It offers valuable insights into the rapidly expanding field of energy transition and green finance, effectively highlighting its contours and indicating potential future developments.

Effect of Financial Incentives on Hypertension Control: A Multicenter Randomized Controlled Trial in China.

BACKGROUND: Poorly controlled hypertension is a great challenge to global public health. Incentive approaches, based on behavioral and economic concepts, may improve patients' adherence to treatment. METHODS: We conducted a 2-arm randomized controlled trial to test whether financial incentives can help patients with poorly controlled hypertension in China reduce their blood pressure (BP). Participants were randomized 1:1 to the control and intervention groups. All participants received WeChat-based standard education and support for hypertension management. The intervention group received financial incentives, including process- and outcome-based incentives. RESULTS: No statistically significant differences in BP reduction and hypertension control rates were found between the two groups from baseline to 12-month follow-up. Mean systolic BP decreased from 158.7 to 149.8 mm Hg in the intervention group and 159.7 to 149.5 mm Hg in the control group (P=0.639). Mean diastolic BP decreased from 93.7 to 86.6 mm Hg in the intervention group and 93.9 to 86.3 mm Hg in the control group (P=0.667). Hypertension control rates in the intervention and control groups were 20.8% and 15.8%, respectively (P=0.318). Medication adherence was 84.2% in the intervention group and 86.2% in the control group (P=0.705). CONCLUSIONS: Financial incentives were effective in the short term for BP control, but a sustained effect of incentive-based BP control was not identified beyond 3 months of intervention. Future studies that focus on identifying the appropriate amount and structure of financial incentives for BP control are warranted. REGISTRATION: URL: www.isrctn.org; Unique identifier: ISRCTN13467677.

Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning.

Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score-based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.

Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing.

In personalized medicine, many factors influence the choice of compounds. Hence, the selection of suitable medicine for patients with non-small-cell lung cancer (NSCLC) is expensive. To shorten the decision-making process for compounds, we propose a computationally efficient and cost-effective collaborative filtering method with ensemble learning. The ensemble learning is used to handle small-sample sizes in drug response datasets as the typical number of patients in a cancer dataset is very small. Moreover, the proposed method can be used to identify the most suitable compounds for patients without genetic data. To the best of our knowledge, this is the first method to provide effective recommendations without genetic data. We also constructed a reliable dataset that includes eight NSCLC cell lines and ten compounds that have been approved by the Food and Drug Administration. With the new dataset, the experimental results demonstrated that the dataset shift phenomenon that commonly occurs in practical biomedical data does not occur in this problem. The experimental results demonstrated that our proposed method can outperform two state-of-the-art recommender system techniques on both the NCI60 dataset and our new dataset. Our model can be applied to the prediction of drug sensitivity with less labor-intensive experiments in the future.

Montreal Cognitive Assessment (MoCA) Performance and Domain-Specific Index Scores in Amnestic Versus Aphasic Dementia.

OBJECTIVE: The Montreal Cognitive Assessment (MoCA) is a popular and simple-to-administer screening instrument to detect cognitive impairment. The MoCA generates a total score and six domain-specific index scores: (1) Memory, (2) Executive Functioning, (3) Attention, (4) Language, (5) Visuospatial, and (6) Orientation. It is unclear whether these MoCA scores can differentiate between distinct clinical dementia syndromes. This study compared MoCA Index scores between amnestic dementia of the Alzheimer's type (DAT) and primary progressive aphasia (PPA), a language-based dementia. METHOD: Baseline MoCA data were analyzed from 33 DAT, 37 PPA, and 83 cognitively normal individuals enrolled in the Clinical Core of the Northwestern Alzheimer's Disease Center. A one-way analysis of covariance adjusted for age was used to compare MoCA scores among groups. A logistic regression model was implemented to observe individual likelihood of group affiliation based on MoCA Index scores. RESULTS: The mean MoCA total score was significantly higher in controls compared to both patient groups (p < .001) but did not differ between DAT and PPA groups. However, in accordance with salient clinical features commonly observed in DAT versus PPA, Memory and Orientation Index scores were lowest in the DAT group (p < .001), whereas Language and Attention Index scores were lowest in the PPA group (p < .001). Multivariate logistic regression analysis showed that the individual effects of Memory (p = .001), Language (p = .002), and Orientation (p = .025) Indices were significant. CONCLUSIONS: MoCA Index scores can help differentiate among distinct cognitive syndromes, suggesting it may be a useful brief screening tool to detect domain-specific cognitive impairment.

Changes in bacterial community structure and antibiotic resistance genes in soil in the vicinity of a pharmaceutical factory.

China is the largest global producer of antibiotics. With the demand for antibiotics increasing every year, it is necessary to assess potential environmental risks and the spread of antibiotic resistance genes (ARGs) associated with antibiotic production. Here, we investigated the occurrence and distribution of ARGs in soil in the vicinity of a pharmaceutical factory. The results showed that antibiotic concentrations were under the detection limit; however, ARGs were present in soil and tended to be enriched near the factory. A significant correlation between the relative abundance of intI-1 and tetracycline ARGs implied that horizontal gene transfer might play an important role in the spread of ARGs. The occurrence of these ARGs could be the results of previous antibiotic contamination. However, the soil bacterial community structure seemed to be more affected by nutrients or other factors than by antibiotics. Overall, this study supports the viewpoint that long-term pharmaceutical activity might have a negative effect on environmental health, thus, underscoring the need to regulate antibiotic production and management.