Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2023-06-08 Cooperative journals: 《中国全科医学》
Abstract： Background Lymph node metastasis is an important factor affecting the prognosis of patients with intrahepatic cholangiocarcinoma，but lymphadenectomy extent remains controversial both domestically and internationally. Objective To explore the current status of lymphadenectomy during radical resection of intrahepatic cholangiocarcinoma. Methods A retrospective analysis of the clinical data of 152 patients with intrahepatic cholangiocarcinoma who underwent radical resection at Zhejiang Cancer Hospital from 2017 to 2022 was conducted to determine the current status of lymphadenectomy during radical resection of intrahepatic cholangiocarcinoma，including the decision to perform lymphadenectomy，the extent of lymphadenectomy and the distribution of positive lymph nodes. The patients were divided into the left hemi-liver group and right hemi-liver group according to the location of the tumour in the liver. Results A total of 152 patients were selected，including 83 patients in the left hemi-liver group and 69 in the right hemi-liver group. 86 of them underwently mphadenectomy，accounting for higher proportion in the left hemi-liver group〔61 cases（73.5%）〕 than the right hemi-liver group〔25 cases（36.2%）〕（P<0.05）. The average number of dissected lymph nodes was （7.6±6.1），with no significant difference between the left〔7.0（4.0，10.5）〕 and right hemi-liver groups 〔5.0（1.5，9.5）〕（P>0.05）.Of the 86 patients underwent lymphadenectomy，39（45.3%） cases showed lymph node metastasis（positive lymph nodes） on pathological examination，accounting for higher proportion in the left hemi-liver group〔34 cases（55.7%）〕 than the right hemi-liver group〔5 cases（20.0%）〕（P<0.05）. Regardless of which lobe the tumour was located，lymph node stations 8，12，and 13 accounted for a higher proportion of metastasis in routine dissection areas，among which the proportion of lymph nodes station 12 was the highest，with 79.4%（27/34）in the left hemi-liver group and 80.0%（4/5） in the right hemi-liver group. Conclusion Regardless of the location of tumour，lymph node stations 8，12 and 13 have a higher incidence of lymph node metastasis and should be considered for routine dissection during radical resection.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： In recent years, impacts of societal changes on human culture and psychology have become a cutting-edge research area in cultural psychology. The research from the perspective of psychology mainly concerns psychological and behavioral changes as well as their potential drives, which often involves three kinds of effect, that is, age/maturation effect， period/time effect, and cohort/generation effect. Time effect refers to effects caused by socioecological changes in a certain period (e.g., the influences of modernization on Chinese people since 1980s). Age effect refers to development growth caused by individual maturation (e.g., developmental growth during a specific period). Cohort effect refers to effects associated with a specific born year (e.g., enduring effect of modernization on 1970 generation in China). Among these effects, time effect and cohort effect are related to socioecological change, whereas age effect usually constitutes a confounder. In examining psychological changes as well as their drives, widely used research designs includes cross-time comparison, cross-generation comparison, and cross-region comparison (or historical reconstruction). By examining psychology and behaviors of people in different times, cross-time comparison allows researchers to infer how surveyed psychology and behaviors have changed with time. This examination usually involves cross-temporal analysis of published data, archive data and survey data. The survey data may be resulted from diverse designs, including cross-sequential design, longitudinal design, revolving panel design, total population design and retrospective panel design. These designs vary in difficulty of data collection. Cross-generation comparison allows researchers to infer the changes of psychology and behaviors across time by examining differences across people born in different cohorts. The cohort can be decided based on special years (e.g., 1980s, 1990s and so on) or special events (e.g., China’s opening up and reform; China’s joining in WTO). In doing this, research can compare representative samples born in different cohorts. A special case is to compare grandparents, parents, and youngest generation within a family. Cross-generation comparison within a family also allows to examine similarities and dissimilarities of different generations. Cross-regional comparison allows researchers to infer the changes of psychology and behavior by examining differences across regions at different modernization levels. A typical example is to infer psychological changes by comparing people from rural areas with those from urban areas. In this case, rural areas represent the past or tradition, whereas urbane areas represent current or modern time. Thus, rural-urban differences can be mapped onto tradition-modern differences. In examining psychological changes as well as their drives, widely used data analysis methods includes classic correlation and regression analyses, and modern time series analysis. In exploring possible causal relationships, cross-lagged correlation analysis and Granger causal test are often used. In doing correlation and regression analysis, researchers usually use year or potential socioeconomic factors to predict targeted psychological outcomes, thereby inferring the psychological trends as well as their covariations with diverse socioecological factors. Cross-lagged correlation analysis allows us to infer the direction of the covariation. Granger causal test may provide further causality test while controlling for potential influences of autoregression. Vector autoregression has received increasing attention in recent years, which can be used to model multivariate time-series data. Despite salient advances in data analysis technique, how to decompose and estimate the age effect, period effect, and cohort effect is still a challenge. More studies are needed to tackle this issue. In summary, we summarized the main research designs and data analysis techniques in studying culture, psychology, and behavior changes. It is notable that each design has specific pros and cons, researchers need to choose suitable design in terms of research question and data collection possibility. If possible, it is highly recommended to pursue convergent evidence by conducting multiple studies with diverse research designs.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： As a fundamental technique in natural language processing (NLP), word embedding quantifies a word as a low-dimensional, dense, and continuous numeric vector (i.e., word vector). This process is based on machine learning algorithms such as neural networks, through which semantic features of a word can be extracted automatically. There are two types of word embeddings: static and dynamic. Static word embeddings aggregate all contextual information of a word in an entire corpus into a fixed vectorized representation. The static word embeddings can be obtained by predicting the surrounding words given a word or vice versa (Word 2Vec and FastText) or by predicting the probability of co-occurrence of multiple words (GloVe) in large-scale text corpora. Dynamic or contextualized word embeddings, in contrast, derive a word vector based on a specific context, which can be generated through pre-trained language models such as ELMo, GPT, and BERT. Theoretically, the dimensions of a word vector reflect the pattern of how the word can be predicted in contexts; however, they also connote substantial semantic information of the word. Therefore, word embeddings can be used to analyze semantic meanings of text. In recent years, word embeddings have been increasingly applied to study human psychology. In doing this, word embeddings have been used in various ways, including the raw vectors of word embeddings, vector sums or differences, absolute or relative semantic similarity and distance. So far, the Word Embedding Association Test (WEAT) has received the most attention. Based on word embeddings, psychologists have explored a wide range of topics, including human semantic processing, cognitive judgment, divergent thinking, social biases and stereotypes, and sociocultural changes at the societal or population level. Particularly, the WEAT has been widely used to investigate attitudes, stereotypes, social biases, the relationship between culture and psychology, as well as their origin, development, and cross-temporal changes. As a novel methodology, word embeddings offer several unique advantages over traditional approaches in psychology, including lower research costs, higher sample representativeness, stronger objectivity of analysis, and more replicable results. Nonetheless, word embeddings also have limitations, such as their inability to capture deeper psychological processes, limited generalizability of conclusions, and dubious reliability and validity. Future research using word embeddings should address these limitations by (1) distinguishing between implicit and explicit components of social cognition, (2) training fine-grained word vectors in terms of time and region to facilitate cross-temporal and cross-cultural research, and (3) applying contextualized word embeddings and large pre-trained language models such as GPT and BERT. To enhance the application of word embeddings in psychological research, we have developed the R package “PsychWordVec”, an integrated word embedding toolkit for researchers to study human psychology in natural language.
submitted time 2023-03-25 Cooperative journals: 《心理科学进展》
Abstract： As a fundamental technique in natural language processing (NLP), word embedding quantifies a word as a low-dimensional, dense, and continuous numeric vector (i.e., word vector). This process is based on machine learning algorithms such as neural networks, through which semantic features of a word can be extracted automatically. There are two types of word embeddings: static and dynamic. Static word embeddings aggregate all contextual information of a word in an entire corpus into a fixed vectorized representation. The static word embeddings can be obtained by predicting the surrounding words given a word or vice versa (Word 2Vec and FastText) or by predicting the probability of co-occurrence of multiple words (GloVe) in large-scale text corpora. Dynamic or contextualized word embeddings, in contrast, derive a word vector based on a specific context, which can be generated through pre-trained language models such as ELMo, GPT, and BERT. Theoretically, the dimensions of a word vector reflect the pattern of how the word can be predicted in contexts; however, they also connote substantial semantic information of the word. Therefore, word embeddings can be used to analyze semantic meanings of text. In recent years, word embeddings have been increasingly applied to study human psychology. In doing this, word embeddings have been used in various ways, including the raw vectors of word embeddings, vector sums or differences, absolute or relative semantic similarity and distance. So far, the Word Embedding Association Test (WEAT) has received the most attention. Based on word embeddings, psychologists have explored a wide range of topics, including human semantic processing, cognitive judgment, divergent thinking, social biases and stereotypes, and sociocultural changes at the societal or population level. Particularly, the WEAT has been widely used to investigate attitudes, stereotypes, social biases, the relationship between culture and psychology, as well as their origin, development, and cross-temporal changes. As a novel methodology, word embeddings offer several unique advantages over traditional approaches in psychology, including lower research costs, higher sample representativeness, stronger objectivity of analysis, and more replicable results. Nonetheless, word embeddings also have limitations, such as their inability to capture deeper psychological processes, limited generalizability of conclusions, and dubious reliability and validity. Future research using word embeddings should address these limitations by (1) distinguishing between implicit and explicit components of social cognition, (2) training fine-grained word vectors in terms of time and region to facilitate cross-temporal and cross-cultural research, and (3) applying contextualized word embeddings and large pre-trained language models such as GPT and BERT. To enhance the application of word embeddings in psychological research, we have developed the R package “PsychWordVec”, an integrated word embedding toolkit for researchers to study human psychology in natural language.
Subjects: Psychology >> Social Psychology Subjects: Psychology >> Cognitive Psychology Subjects: Psychology >> Psychological Measurement Subjects: Computer Science >> Natural Language Understanding and Machine Translation submitted time 2023-01-30
Abstract： As a basic technique in natural language processing (NLP), word embedding represents a word with a low-dimensional, dense, and continuous numeric vector (i.e., word vector). Word embeddings can be obtained by using neural network algorithms to predict words from the surrounding words or vice versa (Word2Vec and FastText) or words’ probability of co-occurrence (GloVe) in large-scale text corpora. In this case, the values of dimensions of a word vector denote the pattern of how a word can be predicted in a context, substantially connoting its semantic information. Therefore, word embeddings can be utilized for semantic analyses of text. In recent years, word embeddings have been rapidly employed to study human psychology, including human semantic processing, cognitive judgment, individual divergent thinking (creativity), group-level social cognition, sociocultural changes, and so forth. We have developed the R package “PsychWordVec” to help researchers utilize and analyze word embeddings in a tidy approach. Future research using word embeddings should (1) distinguish between implicit and explicit components of social cognition, (2) train fine-grained word vectors in terms of time and region to facilitate cross-temporal and cross-cultural research, and (3) deepen and expand the application of contextualized word embeddings and large pre-trained language models such as GPT and BERT.
Subjects: Psychology >> Social Psychology submitted time 2022-09-01
Abstract：
In recent years, impacts of social change on human culture and psychology have become a cutting-edge research area in cultural psychology. This research often implicates three effects: time or period effect, cohort or generation effect, and age or maturation effect, among which the former two are related to societal change, whereas the last one usually constitutes a confounder. In examining cultural and psychological changes as well as its sources, widely used research designs include cross-time comparison, cross-generation comparison and cross-regional comparison (or historical reconstruction) and widely used statistic methods includes traditional correlation and regression analyses and modern time series analyses (e.g. cross-lagged correlation analysis, Granger causality tests). Since each design has specific pros and cons, researchers need to choose suitable design in terms of research question and data collection possibility. If possible, it is highly recommended to pursue convergent evidence by conducting multiple studies with multiple research designs.
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