Innovation Novelty and Firm Value: Deep Learning based Text Understanding

Innovation is widely acknowledged as a key driver of firm performance, with patents serving as unique indicators of a company’s technological advancements. This study aims to investigate the impact of textual novelty within patents on firm performance, focusing specifically on biotechnology startups listed on the Nasdaq. Utilizing deep learning-based approaches, we construct measures for semantic originality in patent texts. Through panel vector autoregressive (VAR) analysis, our empirical findings demonstrate a positive correlation between textual novelty and abnormal stock returns. Further, impulse response function analysis indicates that the impact of textual novelty peaks approximately one week after patent issuance and gradually diminishes within a month. These insights offer valuable contributions to both the theoretical understanding and practical application of innovation management and strategic planning

Financial transfers from adult children and depressive symptoms among mid-aged and elderly residents in China - evidence from the China health and retirement longitudinal study.

Although the awareness of mental health problems in late life is rising, the association between financial transfers to the older generations from children and mental health at older ages in China has received little attention. This study examines the association between financial transfers from children and depressive symptoms among the mid-aged and elderly residents (from 45 years of age and older) in China. We used the data from the China Health and Retirement Longitudinal Study (CHARLS, 2013) (n = 10,935) This included data on financial transfers from all non-co-resident children to their parents, and the individual scores on depressive symptoms as measured by the 10-item Center for Epidemiologic Studies-Depression Scale (CESD-10). A two-level - individual and community levels - mixed linear model was deployed to explore their association. Financial transfers from children to parents was the major component of inter-generational financial transfers in Chinese families. A higher financial support from non-co-resident children was signivicantly and positively related to fewer depressive symptoms (coef. = - 0.195,P-value< 0.001) among both the mid-aged and elderly parents. Financial transfers from non-co-resident children are associated with depressive symptoms among mid-aged and elderly residents in the China situation. Taxation and other policy measures should encourage and facilitate these type of financial transfers and prevent a decrease of support from children to parents

CTpathway: A Crosstalk-Based Pathway Enrichment Analysis Method for Cancer Research

Background: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. Methods: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with \u3e440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. Results: Analysis of \u3e8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. Conclusions: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Celebrity worshipers' attentional bias toward idol faces: Evidence from empirical studies

Celebrity worship refers to the social identification and emotional attachment of individuals to their idols. With the development of social media, the phenomenon of celebrity worship has become more and more common, and the cognitive processing of celebrity worshipers has been concerned by researchers. In this study, high and low celebrity worship groups were selected by questionnaires, and idol faces and stranger faces were selected as experimental materials. Experiment 1 explored the attentional bias toward idol faces during the attentional orienting phase in the high and low worship groups, and found that high group exhibited shorter reaction times in the idol face and probe dot consistent condition, and that there was a positive correlation between the celebrity worship score and the attention bias index for idol faces. Experiment 2 explored the attentional disengagement of idol faces during the attentional release phase in the high and low worship groups, and found that the high group exhibited longer reaction times when idol faces were used as invalid stimuli, and high group showed larger attention bias index for idol faces than stranger faces, while there was no significant difference in the low group. In conclusion, it was found that the high worship group showed attentional facilitation and attentional disengagement difficulties for idol faces, and the higher the worship score the stronger the attentional bias.N

METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of each specific aspect in a given sentence. Existing researches have realized the importance of the aspect for the ABSA task and have derived many interactive learning methods that model context based on specific aspect. However, current interaction mechanisms are ill-equipped to learn complex sentences with multiple aspects, and these methods underestimate the representation learning of the aspect. In order to solve the two problems, we propose a mutual enhanced transformation network (METNet) for the ABSA task. First, the aspect enhancement module in METNet improves the representation learning of the aspect with contextual semantic features, which gives the aspect more abundant information. Second, METNet designs and implements a hierarchical structure, which enhances the representations of aspect and context iteratively. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of METNet, and we further prove that METNet is outstanding in multi-aspect scenarios

Comparing the income-related inequity of tested prevalence and self-reported prevalence of hypertension in China

Abstract Background Hypertension has become a global health challenge given its high prevalence and but low awareness and detection. Whether the actual prevalence of hypertension has been estimated is important, especially for the poor. This study aimed to measure tested prevalence and self-reported prevalence of hypertension and compare the inequity between them in China. Methods Data were derived from China Health and Nutrition Survey (CHNS) conducted in 2011. By using the multistage, stratified, random sampling method, 12,168 respondents aged 18 or older were identified for analysis. Both tested prevalence (systolic blood pressure ≥ 140 mmHg or/and diastolic blood pressure ≥ 90 mmHg or /and current use any of antihypertensive medication) and self-reported prevalence (ever diagnosed with hypertension by a doctor) were used to measure the prevalence of hypertension. The concentration index was employed to measure the extent of inequality in tested prevalence and self-reported prevalence. A decomposition method, based on a Probit model, was used to analyze income-related horizontal inequity of tested prevalence and self-reported prevalence. Results The tested prevalence and self-reported prevalence of total respondents were 28.8% [95% CI (28.0%, 29.6%)] and 15.7% [95% CI (15.0%, 16.3%)], and 26.4% [95% CI (25.1%, 27.6%)] and 19.0% [95% CI (17.9%, 20.1%)] in urban areas, and 30.3% [95% CI (29.3%, 31.4%)] and 13.5% [95% CI (12.7%, 14.3%)] in rural areas. The horizontal inequity indexes of mean tested prevalence and self-reported prevalence were − 0.0494 and 0.1203 of total respondents, − 0.0736 and 0.0748 in urban area, and − 0.0177 and 0.0466 in rural area respectively, indicating pro-poor inequity in tested prevalence and pro-rich inequity in self-reported prevalence of hypertension. Economic status, education attainment and age were key factors of the pro-poor inequity in tested prevalence. Economic status, area and age were key factors to explain the poor-rich inequity in self-reported prevalence. Conclusions This study revealed self-reported prevalence of hypertension was much lower than tested prevalence in China, while a larger gap between self-reported and tested prevalence was found in rural areas. Our study suggested social strategies aiming at narrowing economic gap and regional disparities, reducing educational inequity, and facilitating health conditions of the elderly should be implemented. Finally, awareness raising campaigns to test hypertension in rural area need be strengthened by health education programs and improving the access to public health service, especially for those who do not engage with regular health checkups

METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of each specific aspect in a given sentence. Existing researches have realized the importance of the aspect for the ABSA task and have derived many interactive learning methods that model context based on specific aspect. However, current interaction mechanisms are ill-equipped to learn complex sentences with multiple aspects, and these methods underestimate the representation learning of the aspect. In order to solve the two problems, we propose a mutual enhanced transformation network (METNet) for the ABSA task. First, the aspect enhancement module in METNet improves the representation learning of the aspect with contextual semantic features, which gives the aspect more abundant information. Second, METNet designs and implements a hierarchical structure, which enhances the representations of aspect and context iteratively. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of METNet, and we further prove that METNet is outstanding in multi-aspect scenarios

Estimating Metastatic Risk of Pancreatic Ductal Adenocarcinoma at Single-Cell Resolution

Pancreatic ductal adenocarcinoma (PDAC) is characterized by intra-tumoral heterogeneity, and patients are always diagnosed after metastasis. Thus, finding out how to effectively estimate metastatic risk underlying PDAC is necessary. In this study, we proposed scMetR to evaluate the metastatic risk of tumor cells based on single-cell RNA sequencing (scRNA-seq) data. First, we identified diverse cell types, including tumor cells and other cell types. Next, we grouped tumor cells into three sub-populations according to scMetR score, including metastasis-featuring tumor cells (MFTC), transitional metastatic tumor cells (TransMTC), and conventional tumor cells (ConvTC). We identified metastatic signature genes (MSGs) through comparing MFTC and ConvTC. Functional enrichment analysis showed that up-regulated MSGs were enriched in multiple metastasis-associated pathways. We also found that patients with high expression of up-regulated MSGs had worse prognosis. Spatial mapping of MFTC showed that they are preferentially located in the cancer and duct epithelium region, which was enriched with the ductal cells’ associated inflammation. Further, we inferred cell–cell interactions, and observed that interactions of the ADGRE5 signaling pathway, which is associated with metastasis, were increased in MFTC compared to other tumor sub-populations. Finally, we predicted 12 candidate drugs that had the potential to reverse expression of MSGs. Taken together, we have proposed scMetR to estimate metastatic risk in PDAC patients at single-cell resolution which might facilitate the dissection of tumor heterogeneity

Effects of China's urban basic health insurance on preventive care service utilization and health behaviors: Evidence from the China Health and Nutrition Survey.

BackgroundLifestyle choices are important determinants of individual health. Few studies have investigated changes in health behaviors and preventive activities brought about by the 2007 implementation of Urban Resident Basic Health Insurance (URBMI) in China. This study, therefore, aimed to explore whether URBMI has reduced individuals' incentives to adopt healthy behaviors and utilize preventive care services.MethodsData were drawn from two waves of the China Health and Nutrition Survey. Respondents were categorized according to their insurance situation before and after the URBMI reform in 2006 and 2011. Propensity score matching and difference-in-differences methods were used to measure levels of preventive care and behavior changes over time. Estimations were also made based on gender, self-reported health, and income.ResultsWe found that URBMI implementation did not change residents' utilization of preventive care services or their smoking habits, drinking habits, or other risky behaviors overall. However, the likelihood of sedentariness did increase by five percentage points. Females tended to be more sedentary while males were less likely to drink soft drinks. Residents with poor self-reported health exercised less while those who reported good health were more likely to be sedentary. Low- and middle-income residents were likely to be sedentary while middle-income people tended to smoke after becoming insured.ConclusionSince URBMI implementation, some unhealthy behaviors like sedentariness have increased among those who were newly insured, and different subgroups have reacted differently. This suggests that the insurance design needs to be optimized and effective measures need to be adopted to help improve people's lifestyle choices