Rare Broad Ligament Cavernous Hemangioma Mimicking Advanced Endometriosis: A Case Report.

Cavernous hemangioma within the female genital tract is an extremely rare pathology, characterized by irregular vascular spaces containing blood or thrombus. We present a unique case of a 42-year-old primiparous woman who presented with typical endometriosis symptoms such as dysmenorrhea, dyspareunia, and heavy menstrual bleeding. The patient also experienced complex postpartum symptoms, which were misdiagnosed as cholecystitis and retained placental products. Imaging studies suggested deep infiltrative endometriosis with extraovarian endometriotic lesions. Surgical exploration revealed a hemangioma within the right anterior broad ligament alongside peritoneal endometriosis lesions. The hemangioma itself expresses estrogen and progesterone receptors in stromal cells. The presence of steroid hormone receptors strongly suggests symptom alleviation during the menstrual cycle and the postpartum period. The coexistence of cavernous hemangioma and endometriosis in the broad ligament, previously unreported, and symptomatic overlap between the two conditions complicates diagnosis and management, emphasizing the need for comprehensive evaluation integrating clinical symptoms and imaging findings.

A fear conditioned cue orchestrates a suite of behaviors in rats.

Pavlovian fear conditioning has been extensively used to study the behavioral and neural basis of defensive systems. In a typical procedure, a cue is paired with foot shock, and subsequent cue presentation elicits freezing, a behavior theoretically linked to predator detection. Studies have since shown a fear conditioned cue can elicit locomotion, a behavior that - in addition to jumping, and rearing - is theoretically linked to imminent or occurring predation. A criticism of studies observing fear conditioned cue-elicited locomotion is that responding is non-associative. We gave rats Pavlovian fear discrimination over a baseline of reward seeking. TTL-triggered cameras captured 5 behavior frames/s around cue presentation. Experiment 1 examined the emergence of danger-specific behaviors over fear acquisition. Experiment 2 examined the expression of danger-specific behaviors in fear extinction. In total, we scored 112,000 frames for nine discrete behavior categories. Temporal ethograms show that during acquisition, a fear conditioned cue suppresses reward seeking and elicits freezing, but also elicits locomotion, jumping, and rearing - all of which are maximal when foot shock is imminent. During extinction, a fear conditioned cue most prominently suppresses reward seeking, and elicits locomotion that is timed to shock delivery. The independent expression of these behaviors in both experiments reveals a fear conditioned cue to orchestrate a temporally organized suite of behaviors.

Knowing that an animal is fearful is crucial for many psychology and neuroscience studies. For instance, this knowledge allows researchers to examine the brain pathways involved in processing and responding to fear. Typically, researchers consider that a rodent is experiencing fear if it 'freezes' ­ a response which, in the wild, helps to evade detection by predators. In Pavlovian fear conditioning experiments, for example, rats and mice freeze when exposed to a stimulus (often a specific sound) previously associated with unpleasant sensations. However, rodents can also respond more actively to threats, for instance by running or jumping away. It remains unclear whether the 'fearful stimuli' used in Pavlovian approaches specifically elicits only freezing, or other fear-related behaviors as well. To investigate this, Chu et al. used high-speed cameras to record rats' responses to a sound cue they had 'learned' to associate with a mild foot shock. In addition to freezing, the animals ran, jumped, stood on their hind legs and stopped their usual reward-seeking behavior in response to the cue. Crucially, these reactions were absent when the rats were exposed to sound cues not associated with pain. Overall, these experiments demonstrate that Pavlovian conditioning can elicit a full range of fear-related behaviors beyond freezing. Understanding the neural activity behind these diverse responses could lead to more targeted therapies and interventions addressing the various ways stress and anxiety manifest in people.

COVID-19 Pandemic Risk Assessment: Systematic Review.

Background: The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries' pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era. Objective: We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning. Methods: This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts. Results: Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles. Conclusion: Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.

Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data.

Background: The COVID-19 pandemic has posed various difficulties for policymakers, such as the identification of health issues, establishment of policy priorities, formulation of regulations, and promotion of economic competitiveness. Evidence-based practices and data-driven decision-making have been recognized as valuable tools for improving the policymaking process. Nevertheless, due to the abundance of data, there is a need to develop sophisticated analytical techniques and tools to efficiently extract and analyze the data. Methods: Using Oxford COVID-19 Government Response Tracker, we categorize the policy responses into 6 different categories: (a) containment and closure, (b) health systems, (c) vaccines, (d) economic, (e) country, and (f) others. We proposed a novel research framework to compare the response times of the scholars and the general public. To achieve this, we analyzed more than 400,000 research abstracts published over the past 2.5 years, along with text information from Google Trends as a proxy for topics of public concern. We introduced an innovative text-mining method: coherent topic clustering to analyze the huge number of abstracts. Results: Our results show that the research abstracts not only discussed almost all of the COVID-19 issues earlier than Google Trends did, but they also provided more in-depth coverage. This should help policymakers identify core COVID-19 issues and act earlier. Besides, our clustering method can better reflect the main messages of the abstracts than a recent advanced deep learning-based topic modeling tool. Conclusion: Scholars generally have a faster response in discussing COVID-19 issues than Google Trends.

Optogenetic inhibition of the caudal substantia nigra inflates behavioral responding to uncertain threat and safety.

Defensive responding is adaptive when it approximates the current threat but maladaptive when it exceeds the current threat. Here we asked if the substantia nigra, a region consistently implicated in reward, is necessary to show appropriate levels of defensive responding in Pavlovian fear discrimination. Rats received bilateral transduction of the caudal substantia nigra with halorhodopsin or a control fluorophore and bilateral ferrule implants. Rats then behaviorally discriminated cues predicting unique foot shock probabilities (danger, p = 1; uncertainty, p = .25; and safety, p = 0). Green-light illumination (532 nm) during cue presentation inflated defensive responding of halorhodopsin rats-measured by suppression of reward seeking-to uncertainty and safety beyond control levels. Green-light illumination outside of cue presentation had no impact on halorhodopsin or control rat responding. The results reveal caudal substantia nigra cue activity is necessary to inhibit defensive responding to nonthreatening and uncertain threat cues. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Standardized local assortativity in networks and systemic risk in financial markets.

The study of assortativity allows us to understand the heterogeneity of networks and the implication of network resilience. While a global measure has been predominantly used to characterize this network feature, there has been little research to suggest a local coefficient to account for the presence of local (dis)assortative patterns in diversely mixed networks. We build on existing literature and extend the concept of assortativity with the proposal of a standardized scale-independent local coefficient to observe the assortative characteristics of each entity in networks that would otherwise be smoothed out with a global measure. This coefficient provides a lens through which the granular level of details can be observed, as well as capturing possible pattern (dis)formation in dynamic networks. We demonstrate how the standardized local assortative coefficient discovers the presence of (dis)assortative hubs in static networks on a granular level, and how it tracks systemic risk in dynamic financial networks.

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.

BACKGROUND: The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks. OBJECTIVE: This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk. METHODS: We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated. RESULTS: Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores. CONCLUSIONS: The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.

Optogenetic inhibition of the caudal substantia nigra inflates behavioral responding to uncertain threat and safety.

Defensive responding is adaptive when it approximates current threat, but maladaptive when it exceeds current threat. Here we asked if the substantia nigra, a region consistently implicated in reward, is necessary to show appropriate levels of defensive responding in Pavlovian fear discrimination. Rats received bilateral transduction of the caudal substantia nigra with halorhodopsin or a control fluorophore, and bilateral ferrule implants. Rats then behaviorally discriminated cues predicting unique foot shock probabilities (danger, p =1; uncertainty, p =0.25; and safety, p =0). Green-light illumination (532 nm) during cue presentation inflated defensive responding of halorhodopsin rats - measured by suppression of reward seeking - to uncertainty and safety beyond control levels. Green-light illumination outside of cue presentation had no impact on halorhodopsin or control rat responding. The results reveal caudal substantia nigra cue activity is necessary to inhibit defensive responding to non-threatening and uncertain threat cues.

A moving-window bayesian network model for assessing systemic risk in financial markets.

Systemic risk refers to the uncertainty that arises due to the breakdown of a financial system. The concept of "too connected to fail" suggests that network connectedness plays an important role in measuring systemic risk. In this paper, we first recover a time series of Bayesian networks for stock returns, which allow the direction of links among stock returns to be formed with Markov properties in directed graphs. We rank the stocks in the time series of Bayesian networks based on the topological orders of the stocks in the learned Bayesian networks and develop an order distance, a new measure with which to assess the changes in the topological orders of the stocks. In an empirical study using stock data from the Hang Seng Index in Hong Kong and the Dow Jones Industrial Average, we use the order distance to predict the extreme absolute return, which is a proxy of extreme market risks, or a signal of systemic risks, using the LASSO regression model. Our results indicate that the network statistics of the time series of Bayesian networks and the order distance substantially improve the predictability of extreme absolute returns and provide insights into the assessment of systemic risk.

Interviewer bias when using multiple mini-interviews in selecting student nurses in a Chinese setting.

BACKGROUND: Interviewer effects may cause unfairness in assessments in multiple mini-interviews (MMIs). Due to cultural differences, the bias factors of interviewers may vary between the East and the West. MMIs are a relatively new type of assessment setting in China and few studies have been conducted to examine the interviewer effects of MMIs in this context. OBJECTIVES: We adopted a multi-faceted Rasch measurement (MFRM) to measure interviewer effects in assessments in Hong Kong. METHODS: Data were collected from a nursing school in Hong Kong. There were 431 candidates and 12 interviewers engaged in a six-station MMI setting. The scores collected from the interviews were analyzed in terms of 1) interviewer stringency/leniency, 2) candidate gender, 3) interview time, and 4) rating category in the station. The Student's t-statistic values were calculated to investigate the marking tendencies of individual interviewers. RESULTS: The research findings suggest that interviewers differ in their degree of stringency/leniency, but the number of candidates examined by each interviewer does not affect interviewer stringency/leniency in terms of the interviewer's assessment. There is not sufficient evidence indicating that candidate gender and interview time are bias factors affecting assessment score in this study. Among the six rating categories examined, honesty/integrity is the most stringent category, while self-awareness is the most lenient category. Interview bias from individuals was identified. When we consider the interview scores given by individual interviewers, it is evident that some interviewers may have been biased toward a certain gender or rating categories. CONCLUSIONS: MMIs are useful when selecting student nurses in a Chinese setting. However, interviewer bias may exist. We used an MFRM to better understand interviewer bias across various dimensions. The present study contributes to the development and use of MMIs in non-Western countries and can be used as a reference to extend this research to other locations.

An automatic speech analytics program for digital assessment of stress burden and psychosocial health.

The stress burden generated from family caregiving makes caregivers particularly prone to developing psychosocial health issues; however, with early diagnosis and intervention, disease progression and long-term disability can be prevented. We developed an automatic speech analytics program (ASAP) for the detection of psychosocial health issues based on clients' speech. One hundred Cantonese-speaking family caregivers were recruited with the results suggesting that the ASAP can identify family caregivers with low or high stress burden levels with an accuracy rate of 72%. The findings indicate that digital health technology can be used to assist in the psychosocial health assessment. While the conventional method requires rigorous assessments by specialists with multiple rounds of questioning, the ASAP can provide a cost-effective and immediate initial assessment to identify high levels of stress among family caregivers so they can be referred to social workers and healthcare professionals for further assessments and treatments.

Habit-Does It Matter? Bringing Habit and Emotion into the Development of Consumer's Food Waste Reduction Behavior with the Lens of the Theory of Interpersonal Behavior.

The immense food waste, generated by restaurants is not only a serious burden for the foodservice business but also a cause of anguish for the emerging nations in which eating out is becoming increasingly trendy. Consumers' food wastes account for a significant portion of restaurant food waste, indicating the need for a change in consumers' behavior to minimize food waste. To examine this problem, our study sought to identify the elements that influence restaurant consumers' behaviors on food waste reduction, reuse, and recycling. The influence of anticipated positive emotions, awareness of consequences, environmental knowledge, and social norms on waste reduction intentions were examined by using a quantitative technique in the investigation. Furthermore, the influence of habits, waste reduction intentions, and facilitating conditions on food waste reduction, reuse, and recycling behaviors have also been investigated. The study collected 1063 responses and employed the PLS-SEM approach to verify the hypotheses. The results suggested that anticipated positive emotions, awareness of consequences, environmental knowledge, and social norms all have substantial impacts on waste reduction intentions. In addition, habits, waste reduction intentions, and facilitating conditions have noteworthy influences on consumers' behaviors towards food waste reduction, reuse, and recycling in restaurants. Understanding these elements could help in correcting customers' waste behaviors in restaurants. The findings in this study are useful for managers, policymakers, and researchers who want to solve the problems of food waste. The implications, limits, and suggestions for further studies have also been discussed in our study.

Isthmocele endometriosis: the relationship between cesarean section and endometriosis.

OBJECTIVE: To present a case of endometriosis within an isthmocele membrane and concomitant diffuse peritoneal endometriosis after cesarean sections. In addition, we describe a unique, color-contrasted surgical repair technique and propose a possible correlation between isthmocele formation and endometriosis. DESIGN: Narrated video article featuring the diagnosis, unique surgical management, and pathological findings of a case of isthmocele endometriosis. Informed consent was obtained from the patient, and all identifiers were removed. SETTING: University-affiliated hospital. PATIENT(S): A 44-year-old patient with three prior cesarean sections and a laparoscopic appendectomy, in none of which endometriosis was visualized. She presented with progressive pelvic pain, dysmenorrhea, dyspareunia, and secondary infertility with recurrent embryo transfer failures. The progressively debilitating symptoms started 14 years ago, shortly after her last cesarean section. Magnetic resonance imaging and ultrasound demonstrated a retroverted uterus and a prominent, thin, fluid-filled cesarean scar defect with a residual myometrial thickness of 1.1 mm. INTERVENTION(S): A combined hysteroscopic and laparoscopic approach was performed to allow for complete resection of the defect and reconstruction of the myometrium. The bladder was backfilled with indocyanine green dye to help identify its borders. Methylene blue was added to the hysteroscopy irrigation solution to create contrast and assist with the isthmocele identification. Wide excision of the isthmocele was performed, followed by a three-layer closure and excision of all apparent peritoneal lesions using the Aqua Blue Contrast Technique. MAIN OUTCOME MEASURE(S): Restoration of normal anatomy, resection of isthmocele, and resolution of the symptoms. RESULT(S): In the pathological assessment, multiple foci of endometriosis were identified within the isthmocele membrane, clearly differentiated from intrauterine endometrial tissue. Additionally, all seven excised peritoneal specimens contained peritoneal endometriosis. Two weeks after the procedure, a transvaginal sonographic scan confirmed a thick anterior uterine wall with a myometrial thickness of 9.2 mm, and the patient reported almost complete resolution of her symptoms. CONCLUSION(S): This case demonstrates endometriosis within the isthmocele membrane, with concomitant symptomatic peritoneal endometriosis. We propose a laparoscopic isthmocele excision technique and a three-layer reconstruction, followed by peritoneal endometriosis excision using methylene blue contrast. We suggest a possible link between isthmocele and endometriosis and emphasize the need for wide excision of the isthmocele margins and maintaining clean borders, given the possibility of endometriosis within the isthmocele, which may be a cause or a contributor to the tissue weakness and isthmocele formation.

Assessing systemic risk in financial markets using dynamic topic networks.

Systemic risk in financial markets refers to the breakdown of a financial system due to global events, catastrophes, or extreme incidents, leading to huge financial instability and losses. This study proposes a dynamic topic network (DTN) approach that combines topic modelling and network analysis to assess systemic risk in financial markets. We make use of Latent Dirichlet Allocation (LDA) to semantically analyse news articles, and the extracted topics then serve as input to construct topic similarity networks over time. Our results indicate how connected the topics are so that we can correlate any abnormal behaviours with volatility in the financial markets. With the 2015-2016 stock market selloff and COVID-19 as use cases, our results also suggest that the proposed DTN approach can provide an indication of (a) abnormal movement in the Dow Jones Industrial Average and (b) when the market would gradually begin to recover from such an event. From a practical risk management point of view, this analysis can be carried out on a daily basis when new data come in so that we can make use of the calculated metrics to predict real-time systemic risk in financial markets.

Learning from work-from-home issues during the COVID-19 pandemic: Balance speaks louder than words.

During the 2019 novel coronavirus disease (COVID-19) pandemic, many employees have switched to working from home. Despite the findings of previous research that working from home can improve productivity, the scale, nature, and purpose of those studies are not the same as in the current situation with the COVID-19 pandemic. We studied the effects that three stress relievers of the work-from-home environment-company support, supervisor's trust in the subordinate, and work-life balance-had on employees' psychological well-being (stress and happiness), which in turn influenced productivity and engagement in non-work-related activities during working hours. In order to collect honest responses on sensitive questions or negative forms of behavior including stress and non-work-related activities, we adopted the randomized response technique in the survey design to minimize response bias. We collected a total of 500 valid responses and analyzed the results with structural equation modelling. We found that among the three stress relievers, work-life balance was the only significant construct that affected psychological well-being. Stress when working from home promoted non-work-related activities during working hours, whereas happiness improved productivity. Interestingly, non-work-related activities had no significant effect on productivity. The research findings provide evidence that management's maintenance of a healthy work-life balance for colleagues when they are working from home is important for supporting their psychosocial well-being and in turn upholding their work productivity.

Outcomes analysis of microsurgical physiologic lymphatic procedures for the upper extremity from the United States National Surgical Quality Improvement Program.

INTRODUCTION: Physiologic microsurgical procedures to treat lymphedema include vascularized lymph node transfer (VLNT) and lymphovenous bypass (LVB). The purpose of this study was to assess 30-day outcomes of VLNT and LVB using the National Surgical Quality Improvement Program (NSQIP) database. METHODS: NSQIP was queried (2012-2018) for lymphatic procedures for upper extremity lymphedema after mastectomy. Prophylactic lymphatic procedures and those for lower extremity lymphedema were excluded. Outcomes were assessed for three groups: LVB, VLNT, and patients who had procedures simultaneously (VLNA+LVB). Primary outcomes measured were operative time, 30-day morbidities, and hospital length of stay. RESULTS: The study included 199 patients who had LVB (n = 43), VLNT (n = 145), or VLNT+LVB (n = 11). There was no difference in co-morbidities between the groups (p = 0.26). 30-day complication rates including unplanned reoperation (6.9% VLNT vs. 2.3% LVB) and readmission (0.69% VLNT vs. none in LVB) were not statistically significant (p = 0.54). Surgical site infection, wound complications, deep vein thromboembolism, and cardiac arrest was also similar among the three groups. Postoperative length of stay for VLNT (2.5 days± 2.3), LVB (1.9 days± 1.9), and VLNT+LVB (2.8 days± 0.3) did not differ significantly (p = 0.20). Operative time for LVB (305.4 min ± 186.7), VLNT (254 min ± 164.4), and VLNT+LVB (295.3 min ± 43.2) was not significantly different (p = 0.21). CONCLUSIONS: Our analysis of the NSQIP data revealed that VLNT and LVB are procedures with no significant difference in perioperative morbidity. Our results support that choice of VLNT versus LVB can be justifiably made per the surgeon's preference and experience as the operations have similar complication rates.

Predicting the burden of family caregivers from their individual characteristics.

This study examined the association between caregivers' burdens and their individual characteristics and identified characteristics that are useful for predicting the level of caregiver burden. We successfully surveyed 387 family caregivers, having them complete the caregiver burden inventory scale (CBI) and an individual characteristic questionnaire. When we compared the average CBI scores between groups with a particular individual characteristic (including caring for older adult(s), educational level, employment status, place of birth, marital status, financial status, need for family support, need for friend support, and need for nonprofit organizational support), we found a significant difference in the average scores. From a logistic regression model, with burden level as the outcome, we found that caring for older adult(s), educational level, employment status, place of birth, financial situation, and need for nonprofit organizational support were significant predictors of the burden level of caregivers. The research findings suggest that certain individual characteristics can be adopted for identifying and quantifying caregivers who may have a higher level of burden. The findings are useful to uncover caregivers who may need prompt support and social care.

Stochastic actor-oriented modelling of the impact of COVID-19 on financial network evolution.

The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor-oriented model (SAOM) to model dynamic/longitudinal financial networks with the covariates constructed from the network statistics of COVID-19 dynamic pandemic networks. Our findings provide evidence that the transmission risk of the COVID-19, measured in the transformed pandemic risk scores, is a main explanatory factor of financial network connectedness from March to May 2020. The pandemic statistics and transformed pandemic risk scores can give early signs of the intense connectedness of the financial markets in mid-March 2020. We can make use of the SAOM approach to predict possible financial contagion using pandemic network statistics and transformed pandemic risk scores of the COVID-19 and other pandemics.

Antecedents of Consumer Food Waste Reduction Behavior: Psychological and Financial Concerns through the Lens of the Theory of Interpersonal Behavior.

This study sought to investigate the role of consumers' emotional, cognitive, and financial concerns in the development of food waste reduction, reuse, and recycling behavior among restaurant patrons. Food waste in restaurants is a major problem for the food service industry, and it is a growing source of concern in developing countries, where eating out is becoming increasingly popular. A large portion of restaurant food waste in these markets originates from the plates of customers, highlighting the importance of consumer behavior changes in reducing waste. The current study has used a quantitative approach to analyze the impact of anticipated negative emotion of guilt, awareness of consequences, habit, and financial concern on food waste reduction behaviors, i.e., reduce, reuse, and recycle. The study collected 492 responses and data is analyzed for hypotheses testing through Partial Least Square-Structural Equation Modelling. The findings showed that anticipated negative emotions of guilt, awareness of consequences, habit, and financial concern have a significant impact on restaurants' consumer food waste reduction behaviors. Managers, policymakers, and researchers interested in resolving the food waste problem will find the study useful. Other topics discussed include the implications and limitations as well as possible future research directions.