The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.

In the clinical diagnosis and treatment of pituitary adenomas, MRI plays a crucial role. However, traditional manual interpretations are plagued by inter-observer variability and limitations in recognizing details. Radiomics, based on MRI, facilitates quantitative analysis by extracting high-throughput data from images. This approach elucidates correlations between imaging features and pituitary tumor characteristics, thereby establishing imaging biomarkers. Recent studies have demonstrated the extensive application of radiomics in differential diagnosis, subtype identification, consistency evaluation, invasiveness assessment, and treatment response in pituitary adenomas. This review succinctly presents the general workflow of radiomics, reviews pertinent literature with a summary table, and provides a comparative analysis with traditional methods. We further elucidate the connections between radiological features and biological findings in the field of pituitary adenoma. While promising, the clinical application of radiomics still has a considerable distance to traverse, considering the issues with reproducibility of imaging features and the significant heterogeneity in pituitary adenoma patients.

Impairment of affective and cognitive empathy in high functioning autism is mediated by alterations in emotional reactivity.

Empathy impairments are considered a key aspect of autism-spectrum disorders (ASD). Previous research consistently shows reduced cognitive empathy, but findings on affective empathy vary, possibly due to experimental design variations (e.g., stimulus modality, social distance) and individual psychological factors (e.g., perceptual abilities, emotional reactivity). This study aims to clarify deficits in affective and cognitive empathy in ASD by addressing these contributing factors. Empathy was examined in 34 autistic individuals and 33 typically developed controls (TDCs) through the Textual Empathy Test (TET). The TET was developed to assess emotional responses when imagining oneself (emotional reactivity) as compared to a target person (friend, stranger) in emotional situations presented via short verbal descriptions. Participants rated emotional states of the target person (cognitive empathy) as well as their own emotional responses when imagining the target person in that situation (affective empathy). Ratings were interpreted relative to normative mean values through standardized regression coefficients. Results showed that high-functioning autism was associated with lower cognitive and affective empathy irrespective of social distance as well as with decreased emotional reactivity compared to controls. Moreover, emotional reactivity mediated the impact of ASD on both empathic components. In summary, altered emotional reactivity may underlie impaired empathy in autistic individuals.

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

BACKGROUND: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient's status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. OBJECTIVE: This study aimed to investigate the system's performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. METHODS: We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. RESULTS: Our system underestimates by 13.3 percentage points (74.0%-60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician's progress notes, followed by the pharmacist's and nursing records. CONCLUSIONS: Considering the inherent cost that requires constant monitoring of the patient's condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents.

The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.

Background: Collaborative documentation (CD) is a behavioral health practice involving shared writing of clinic visit notes by providers and consumers. Despite widespread dissemination of CD, research on its effectiveness or impact on person-centered care (PCC) has been limited. Principles of PCC planning, a recovery-based approach to service planning that operationalizes PCC, can inform the measurement of person-centeredness within clinical documentation. Objective: This study aims to use the clinical informatics approach of natural language processing (NLP) to examine the impact of CD on person-centeredness in clinic visit notes. Using a dictionary-based approach, this study conducts a textual analysis of clinic notes from a community mental health center before and after staff were trained in CD. Methods: This study used visit notes (n=1981) from 10 providers in a community mental health center 6 months before and after training in CD. LIWC-22 was used to assess all notes using the Linguistic Inquiry and Word Count (LIWC) dictionary, which categorizes over 5000 linguistic and psychological words. Twelve LIWC categories were selected and mapped onto PCC planning principles through the consensus of 3 domain experts. The LIWC-22 contextualizer was used to extract sentence fragments from notes corresponding to LIWC categories. Then, fixed-effects modeling was used to identify differences in notes before and after CD training while accounting for nesting within the provider. Results: Sentence fragments identified by the contextualizing process illustrated how visit notes demonstrated PCC. The fixed effects analysis found a significant positive shift toward person-centeredness; this was observed in 6 of the selected LIWC categories post CD. Specifically, there was a notable increase in words associated with achievement (ß=.774, P<.001), power (ß=.831, P<.001), money (ß=.204, P<.001), physical health (ß=.427, P=.03), while leisure words decreased (ß=-.166, P=.002). Conclusions: By using a dictionary-based approach, the study identified how CD might influence the integration of PCC principles within clinical notes. Although the results were mixed, the findings highlight the potential effectiveness of CD in enhancing person-centeredness in clinic notes. By leveraging NLP techniques, this research illuminated the value of narrative clinical notes in assessing the quality of care in behavioral health contexts. These findings underscore the promise of NLP for quality assurance in health care settings and emphasize the need for refining algorithms to more accurately measure PCC.

Top-k sentiment analysis over spatio-temporal data.

In recent years, social media has become much more popular to use to express people's feelings in different forms. Social media such as X (i.e., Twitter) provides a huge amount of data to be analyzed by using sentiment analysis tools to examine the sentiment of people in an understandable way. Many works study sentiment analysis by taking in consideration the spatial and temporal dimensions to provide the most precise analysis of these data and to better understand people's opinions. But there is a need to facilitate and speed up the searching process to allow the user to find the sentiment analysis of recent top-k tweets in a specified location including the temporal aspect. This work comes with the aim of providing a general framework of data indexing and search query to simplify the search process and to get the results in an efficient way. The proposed query extends the fundamental spatial distance query, commonly used in spatial-temporal data analysis. This query, coupled with sentiment analysis, operates on an indexed dataset, classifying temporal data as positive, negative, or neutral. The proposed query demonstrates over a tenfold improvement in query time compared to the baseline index with various parameters such as top-k, query distance, and the number of query keywords.

Textual emotion classification using MPNet and cascading broad learning.

As one of the most important tasks of natural language processing, textual emotion classification (TEC) aims to recognize and detect all emotions contained in texts. However, most existing methods are implemented using deep learning approaches, which may suffer from long training time and low convergence. Motivated by these challenges, in this paper, we provide a new solution for TEC by using cascading broad learning (CBL) and sentence embedding using a masked and permuted pre-trained language model (MPNet), named CBLMP. Texts are input into MPNet to generate sentence embedding containing emotional semantic information. CBL is adopted to improve the ability of feature extraction in texts and to enhance model performance for general broad learning, by cascading feature nodes and cascading enhancement nodes, respectively. The L-curve model is adopted to ensure the balance between under-regularization and over-regularization for regularization parameter optimization. Extensive experiments have been carried out on datasets of SMP2020-EWECT and SemEval-2019 Task 3, and the results show that CBLMP outperforms the baseline methods in TEC.

[Examination of the Thermal Stability and Swallowing Safety of a Self-Developed Enteral Nutrition Thickening Agent]. / è‡ªåˆ¶è‚ å† è¥å »æ¶²å¢žç¨ å‰‚çš„çƒ­ç¨³å®šæ€§åŠåžå’½å®‰å ¨æ€§æŽ¢ç´¢.

Objective: To experimentally validate the effects of a self-developed heat-stable thickening agent on the textual characteristics of enteral nutrition solutions of standard concentration and its applicability in improving dysphagia. Methods: A gradient of different doses of the self-developed thickening agent (1.0 g, 1.5 g, 2.0 g, 2.5 g, and3.0 g) and three commonly used commercial thickeners were mixed with 23.391 g of a complete nutrition formula powder dissolved in 85 mL of purified water to prepare 100 mL standard concentration nutrition solutions. The textual parameters (cohesiveness, viscosity, thickness, and hardness) of these nutrition solutions were measured using a texture analyzer at various temperature gradients (20 ℃, 40 ℃, 60 ℃, and 80 ℃) to compare their thermal stability. A dysphagia rat model was created via epiglottectomy to explore the effects of the thickener on lung tissue damage scores and levels of inflammatory markers. The rats were divided into a test intervention group, a positive control group, a negative control group, and a blank control group (no surgery and normal feeding after fasting for one day), with 15 rats in each group. After fasting for one day post-surgery, the test intervention group was fed with the standard concentration nutrition solution thickened with the self-developed thickener, while the positive control group was given a standard concentration nutrition solution thickened with product 3, and the negative control group was fed a normal diet. All groups were fed for two weeks with food dyed with food-grade green dye. General conditions, body mass, and food intake were observed and recorded. After two weeks, abdominal aorta blood was collected, and heart, liver, spleen, lung, and kidney tissues were harvested and weighed to calculate the lung tissue organ coefficient. The organ conditions were evaluated using routine H&E staining, and lung damage was semi-quantitatively analyzed based on the Mikawa scoring criteria. Blood supernatants were collected to measure the total serum protein and albumin levels to determine the nutritional status of the rats. The expression of IL-6 and TNF-α genes in lung tissues was measured by RT-qPCR. IL-6 and TNF-α protein expression levels in lung tissues, lung tissue homogenate, and serum were measured by ELISA. The aspiration incidence rate was calculated. Results: Within the dosage range of 1.0 g to 3.0 g, the self-developed thickener in the test samples exhibited superior thermal stability in cohesiveness compared to the three commercially available thickeners, with a statistically significant difference (P<0.01). The differences in the thermal stability of viscosity and hardness between the self-developed thickener and the three commercially available thickeners were not statistically significant. The viscosity stability was optimal for the self-developed thickener, followed by the commercially available thickeners 1 and 3, with thickeners 2 being the least stable, though the differences were not statistically significant (P>0.05). Product 1 showed the best thermal stability in thickness, followed by the self-developed thickener and product 2, while the product 3 exhibited the worst performance, with the difference being statistically significant (P<0.01). The self-developed thickener had the best thermal stability in hardness at temperatures ranging from 20℃ to 80 ℃, followed by products 1 and 2, with product 3 being the least stable. However, the differences were not statistically significant (P>0.05). Animal experiment results indicated that the body weight gain in the positive control group and the test intervention group was lower than that in the blank and negative control groups (P<0.01). The spleen coefficient of the intervention group was lower than that of the positive control group and the blank control group (P<0.01), while the heart, liver, and kidney coefficients were lower than those of the blank control group (P<0.01). The differences in the lung coefficient of the intervention group and those of the other three groups were no statistically significant. Levels of TP and ALB in the test intervention group, the positive control group, and the negative control group were all lower than those in the blank control group, with statistically significant differences (P<0.01). ELISA results showed that serum IL-6 levels in the blank and test intervention groups were lower than those in the negative and positive control groups (P<0.05), while the difference in the other indicators across the four groups were not statistically significant (P>0.05). There were no statistically significant differences among the four groups in terms of lung tissue damage pathology scores, or in the levels of IL-6 and TNF-α gene expression in lung tissues. The aspiration incidence rate was 0% in all groups. Conclusion: The self-developed enteral nutrition thickening agent demonstrated excellent thermal stability and swallowing safety. Further research to explore its application in patients with dysphagia is warranted.

The Ability of ChatGPT in Paraphrasing Texts and Reducing Plagiarism: A Descriptive Analysis.

Background: The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out. Objective: This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot. Methods: Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated. Results: The average plagiarism rate in the texts generated by ChatGPT was 45% (SD 10%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference -0.51, 95% CI -0.54 to -0.48; P<.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference -0.06, 95% CI -0.08 to -0.03; P<.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P<.001). Conclusions: Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work.

Assessing inclusion and representativeness on digital platforms for health education: Evidence from YouTube.

BACKGROUND: Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and representativeness in the context of healthcare information disseminated via videos on the YouTube social media platform, a widely used online channel for multi-media rich information. With one in three US adults using the Internet to learn about a health concern, it is critical to assess inclusivity and representativeness regarding how health information is disseminated by digital platforms such as YouTube. METHODS: Leveraging methods from fair machine learning (ML), natural language processing and voice and facial recognition methods, we examine inclusivity and representativeness of video content presenters using a large corpus of videos and their metadata on a chronic condition (diabetes) extracted from the YouTube platform. Regression models are used to determine whether presenter demographics impact video popularity, measured by the video's average daily view count. A video that generates a higher view count is considered to be more popular. RESULTS: The voice and facial recognition methods predicted the gender and race of the presenter with reasonable success. Gender is predicted through voice recognition (accuracy = 78%, AUC = 76%), while the gender and race predictions use facial recognition (accuracy = 93%, AUC = 92% and accuracy = 82%, AUC = 80%, respectively). The gender of the presenter is more significant for video views only when the face of the presenter is not visible while videos with male presenters with no face visibility have a positive relationship with view counts. Furthermore, videos with white and male presenters have a positive influence on view counts while videos with female and non - white group have high view counts. CONCLUSION: Presenters' demographics do have an influence on average daily view count of videos viewed on social media platforms as shown by advanced voice and facial recognition algorithms used for assessing inclusion and representativeness of the video content. Future research can explore short videos and those at the channel level because popularity of the channel name and the number of videos associated with that channel do have an influence on view counts.

On the (in)effectiveness of textual disclosures on expectancies, attitudes, and perceived appropriateness of alcohol.

OBJECTIVE: Drinking alcohol is frequently portrayed in the media, often overemphasizing its positive attributes. In particular, hip-hop music videos regularly depict alcohol consumption. Building on social cognitive theory, we conduct three pre-registered experimental studies to examine whether textual disclosures from different sources and with varying degrees of explicitness about the consequences of alcohol consumption can influence viewers' alcohol expectancies, attitudes toward alcohol, and the appropriateness of alcohol presentations following a hip-hop video depicting alcohol consumption. METHODS AND MEASURES: We test 1) the established YouTube product placement disclosure, 2) a disclosure that explicitly refers to alcohol and a disclosure that additionally refers to the harmful consequences of alcohol consumption, 3) and finally the role of user comments on YouTube that discuss the negative or positive consequences of alcohol. RESULTS: We found that none of the disclosures tested were able to reduce positive attitudes toward alcohol, positive drinking expectancies, or perceived appropriateness of alcohol portrayals. Instead, one's own drinking behavior was most important in explaining these dependent variables, with frequent drinkers reporting higher scores on attitudes toward alcohol, positive drinking expectancies, and a positive evaluation of alcohol portrayals in the media compared to people who never or rarely drink. CONCLUSIONS: Our findings across the three studies paint a picture of the ineffectiveness of various forms of textual disclosure on alcohol-related attitudes, expectancies, and ratings of the appropriateness of alcohol portrayals in the media. Alternative steps forward, i.e., the creation of offerings for content creators that encourage them to consider the consequences of their representations, are therefore needed.

The topography of thought.

Whether speaking, writing, or thinking, almost everything humans do involves language. But can the semantic structure behind how people express their ideas shed light on their future success? Natural language processing of over 40,000 college application essays finds that students whose writing covers more semantic ground, while moving more slowly (i.e. moving between more semantically similar ideas), end up doing better academically (i.e. have a higher college grade point average). These relationships hold controlling for dozens of other factors (e.g. SAT score, parents' education, and essay content), suggesting that essay topography encodes information that goes beyond family background. Overall, this work sheds light on how language reflects thought, demonstrates that how people express themselves can provide insight into their future success, and provides a systematic, scalable, and objective method for quantifying the topography of thought.

Development and Validation of the Emotion Laden Sentences Toolbox for Emotion Recognition (ELSTER).

Introduction: Emotion recognition plays a crucial role in our social interactions and overall well-being. The present cross-sectional study aimed to develop and validate Emotion Laden Sentences Toolbox for Emotion Recognition (ELSTER), that utilizes emotion-laden sentences as stimuli to assess individuals' ability to perceive and identify emotions conveyed through written language. Methods: In Phase I, a comprehensive set of emotion-laden sentences in English language were validated by 25 (eight males and 17 females) qualified mental health professionals (MHPs). In Phase II, the sentences that received high interrater agreement in Phase I were selected and then a Hindi version of the same sentences was also developed. The English and Hindi database was then validated among 50 healthy individuals (30 males and 20 females). Results: The percentage hit rate for all the emotions after exclusion of contempt was 84.3% with a mean kappa for emotional expression being 0.67 among MHPs. The percentage hit rate of all emotion-laden sentences across the database was 81.43% among healthy lay individuals. The mean hit rate percentage for English sentences was similar to Hindi sentences with a mean kappa for emotional expression being 0.63 for the combined English and Hindi sentences. Conclusion: The ELSTER database would be useful in the Indian context for researching textual emotion recognition. It has been validated among a group of experts as well as healthy lay individuals and was found to have high inter-rater reliability.

Equivalent sentiment measures for cross-language analysis of corporate communications.

This paper presents a technique for sentiment measurement in many languages. The method allows researchers to efficiently analyze corporate documents, management reports, and financial statements using python. When the texts are written in many languages, the method extracts equivalent cross-linguistic sentiment features that can be used for statistical analysis or machine learning. We use Open Multilingual WordNet, a large lexicon organizing words into semantic groups, as the knowledge base about word equivalence in more than 200 languages. We experiment with a parallel English-French corpus and find that our senitment measures across the two languages are comparable. The method produces a consistent classification of positive and negative texts in two languages, and sentiment measure values correlate. The paper provides a detailed account of the method and python code, So that it can be applied to other languages, text mining, quantitative communication studies, and management research.•Method to create equivalent sentiment measures in multiple languages•Based on established lexicons and WordNet•Validated for English and French.

MV-SHIF: Multi-view symmetric hypothesis inference fusion network for emotion-cause pair extraction in documents.

Emotion-cause pair extraction (ECPE) is a challenging task that aims to automatically identify pairs of emotions and their causes from documents. The difficulty of ECPE lies in distinguishing valid emotion-cause pairs from many irrelevant ones. Most previous methods have primarily focused on utilizing multi-task learning to extract semantic information solely from documents without explicitly encoding the relations between clauses. We propose a new approach that incorporates textual entailment paradigm aiming to infer the entailment relationship between the original document as the premise and the clauses or pairs described as the hypothesis. Our approach designs label-view hypothesis templates to improve ECPE by filtering out irrelevant emotion and cause clauses. Furthermore, we formulate candidate emotion-cause pairs as hypothesis statements, and define explicit multi-view symmetric templates to capture the emotion-cause relation semantics. The text entailment recognition for ECPE is finally implemented by fusing multi-view semantic information using a simplified capsule network. Our proposed model achieves state-of-the-art performance on ECPE compared to previous baselines. More importantly, this work demonstrates a novel effective way of applying the textual entailment paradigm to ECPE or clause-level causal discovery by designing multi-view hypothesis inference and information fusion.

Key point generation as an instrument for generating core statements of a political debate on Twitter.

Identifying key statements in large volumes of short, user-generated texts is essential for decision-makers to quickly grasp their key content. To address this need, this research introduces a novel abstractive key point generation (KPG) approach applicable to unlabeled text corpora, using an unsupervised approach, a feature not yet seen in existing abstractive KPG methods. The proposed method uniquely combines topic modeling for unsupervised data space segmentation with abstractive summarization techniques to efficiently generate semantically representative key points from text collections. This is further enhanced by hyperparameter tuning to optimize both the topic modeling and abstractive summarization processes. The hyperparameter tuning of the topic modeling aims at making the cluster assignment more deterministic as the probabilistic nature of the process would otherwise lead to high variability in the output. The abstractive summarization process is optimized using a Davies-Bouldin Index specifically adapted to this use case, so that the generated key points more accurately reflect the characteristic properties of this cluster. In addition, our research recommends an automated evaluation that provides a quantitative complement to the traditional qualitative analysis of KPG. This method regards KPG as a specialized form of Multidocument summarization (MDS) and employs both word-based and word-embedding-based metrics for evaluation. These criteria allow for a comprehensive and nuanced analysis of the KPG output. Demonstrated through application to a political debate on Twitter, the versatility of this approach extends to various domains, such as product review analysis and survey evaluation. This research not only paves the way for innovative development in abstractive KPG methods but also sets a benchmark for their evaluation.

Dual modality prompt learning for visual question-grounded answering in robotic surgery.

With recent advancements in robotic surgery, notable strides have been made in visual question answering (VQA). Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the image. This limitation restricts the interpretative capacity of the VQA models and their ability to explore specific image regions. To address this issue, this study proposes a grounded VQA model for robotic surgery, capable of localizing a specific region during answer prediction. Drawing inspiration from prompt learning in language models, a dual-modality prompt model was developed to enhance precise multimodal information interactions. Specifically, two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model. A visual complementary prompter merges visual prompt knowledge with visual information features to guide accurate localization. The textual complementary prompter aligns visual information with textual prompt knowledge and textual information, guiding textual information towards a more accurate inference of the answer. Additionally, a multiple iterative fusion strategy was adopted for comprehensive answer reasoning, to ensure high-quality generation of textual and grounded answers. The experimental results validate the effectiveness of the model, demonstrating its superiority over existing methods on the EndoVis-18 and EndoVis-17 datasets.

Exploring the Association Between Textual Parameters and Psychological and Cognitive Factors.

Background: Textual data analysis has become a popular method for examining complex human behavior in various fields, including psychology, psychiatry, sociology, computer science, data mining, forensic sciences, and communication studies. However, identifying the most relevant textual parameters for analyzing complex behavior is still a challenge. Goal of Study: This paper aims to explore potential textual parameters that could be useful in analyzing behavior through complex textual data. Furthermore, we have examined the randomly generated text based on different textual parameters. Methods: To achieve this goal, we conducted a comprehensive review of the literature on textual data analysis and identified several potential topics that could be relevant, such as sentiment analysis, discourse analysis, lexical analysis, and syntactic analysis. We discuss the theoretical background and practical implications of each parameter and provide examples of how they have been used in previous research. Furthermore, we highlight the importance of considering the context in which these parameters are applied and the need for interdisciplinary collaboration to gain a deeper understanding of complex behavior through textual data analysis. Furthermore, we have provided Python code in the Supplementary Materials to facilitate a comprehensive analysis of such behaviors. In addition, to generate the text for analysis, we utilized ChatGPT 3.5 Turbo by requesting it to generate a random text of 1000 words divided into five paragraphs. Afterwards, we applied the provided Python code to analyze the randomly generated text. Conclusion: Overall, this paper provides a foundation for researchers to identify relevant textual parameters to analyze complex human behavior in their respective fields such as linguistics, sociology, psychiatry, and psychology.

The Temperature Feature of ChatGPT: Modifying Creativity for Clinical Research.

More clinicians and researchers are exploring uses for large language model chatbots, such as ChatGPT, for research, dissemination, and educational purposes. Therefore, it becomes increasingly relevant to consider the full potential of this tool, including the special features that are currently available through the application programming interface. One of these features is a variable called temperature, which changes the degree to which randomness is involved in the model's generated output. This is of particular interest to clinicians and researchers. By lowering this variable, one can generate more consistent outputs; by increasing it, one can receive more creative responses. For clinicians and researchers who are exploring these tools for a variety of tasks, the ability to tailor outputs to be less creative may be beneficial for work that demands consistency. Additionally, access to more creative text generation may enable scientific authors to describe their research in more general language and potentially connect with a broader public through social media. In this viewpoint, we present the temperature feature, discuss potential uses, and provide some examples.

Association between textual and pictorial warnings on tumbac (waterpipe tobacco) boxes and motivation to quit waterpipe smoking among Lebanese and Iraqi adolescents.

BACKGROUND: Waterpipe tobacco smoking has increased tremendously at a global level among all age groups, particularly young people. Previous studies have examined the impact of waterpipe tobacco pictorial health warnings on adults but scarce studies were done on adolescents. The aim of this study was to assess the association of textual versus pictorial warnings on tumbac boxes and the motivation to quit waterpipe smoking among adolescents located in two Eastern Mediterranean countries Lebanon and Iraq. METHODS: A cross-sectional study was conducted between May and November 2022, involving 294 adolescents waterpipe smokers from Lebanon and Iraq. The questionnaire included the Lebanese Waterpipe Dependence Smoking-11, the Depression, Anxiety and Stress Scale, the Waterpipe Harm Perception Scale, Waterpipe Knowledge Scale, Waterpipe Attitude Scale, the Fagerstrom Test for Nicotine Dependence, and the Motivation to Stop Scale. RESULTS: When adjusting the results over confounding variables, the results showed that compared to finding the warnings to stop smoking not efficacious at all, adolescents who find the warnings moderately (aOR = 2.83) and very (aOR = 6.64) efficacious had higher motivation to quit. Compared to finding the warnings not increasing their curiosity for information about how to stop waterpipe smoking at all, participants who confessed that warnings increased their curiosity a little (aOR = 2.59), moderately (aOR = 3.34) and very (aOR = 3.58) had higher motivation to quit. Compared to not considering changing the tumbac brand if the company uses pictorial warnings, adolescents who would consider changing the tumbac brand (aOR = 2.15) had higher motivation to quit. CONCLUSION: Pictorial and textual warnings on waterpipe packs were associated with higher motivation to stop waterpipe smoking. Public health education programs for this purpose seem warranted.

Inter-sexual Mate Competition in Humans: A Historical Example from Seventeenth Century Portugal.

Inter-sexual mate competition occurs any time opposite-sex individuals simultaneously seek to acquire or maintain exclusive access to the same sexual partner. This underappreciated form of mate competition has been anecdotally documented in several avian and mammalian species, and systematically described among Japanese macaques and humans. Here, we extend the concept of inter-sexual mate competition by reassessing a remarkable series of Portuguese letters, penned in 1664 and later discovered and translated by Mott and Assunção (J Homosex 16:91-104, 1989). The letters comprise one side of a correspondence between two males, former lovers who were scrutinized by the Portuguese Inquisition. After ending the relationship, the recipient of the letters was betrothed to a woman, which provoked a jealous response from his jilted male lover and pleas to reunite. We argue that the letters portray a prolonged sequence of inter-sexual mate competition in which a male and female competitor vied for the same man. An established taxonomy of mate competition tactics was applied to the behavior of both competitors illustrating many parallels with contemporary examples of inter-sexual mate competition. Through this comparison, we show that modern mate competition taxonomies can be fruitfully applied to historical texts and that inter-sexual mate competition occurred hundreds of years before the present. Other examples of inter-sexual mate competition are likely to exist in the historical record, providing a rich source of scientific information if appropriate theoretical frameworks are employed. Indeed, any time individuals are attracted to sexual partners who behave in a bisexual manner, then inter-sexual mate competition can ensue with members of the other sex.