The effect of plastic bag containment of the head on the rate and pattern of decomposition

Plastic bag suffocation has been reported in cases of homicide, suicide, and accidental death, with an increase in numbers of suicide and accidental deaths. Though case reports are abundant, decomposition studies have not been performed. This study utilised 20 Sus scrofa domesticus to quantify the effect of a plastic bag covering the head on the rate and pattern of decomposition. A sample group of ten carcasses had plastic bags placed over the heads, with another ten carcasses acting as a control group, without a head covering. The carcasses were placed in an open field to decompose. Over the course of 52 days, data were collected bi-weekly on the rate and pattern of decomposition. The results show that a plastic bag covering the head of a carcass has an overall decreased effect on the rate of decomposition, compared to the control group. The decomposition pattern of head > trunk > limb in decreasing decomposition rate was not affected by the plastic bag; however, in comparison to the control group, the decomposition of the head and trunk regions differed significantly, while the limbs stayed unaffected. The heads of the sample group showed a decrease in decomposition rate, while the trunks showed an increase. This was deemed due to an increase in insect activity at the trunk and a decrease in activity at the head. An altered PMI calculation is provided

Late Miocene contourite channel system reveals intermittent overflow behavior

Paleoceanographic information from submarine overflows in the vicinity of oceanic gateways is of major importance for resolving the role of ocean circulation in modulating Earth’s climate. Earth system models are currently the favored way to study the impact of gateways on global-scale processes, but studies on overflow-related deposits are more suitable to understand the detailed changes. Such deposits, however, had not yet been documented in outcrop. Here, we present a unique late Miocene contourite channel system from the Rifian Corridor (Morocco) related to the initiation of Mediterranean Outflow Water (MOW). Two channel branches were identified consisting of three vertically stacked channelized sandstone units encased in muddy deposits. Both branches have different channel-fill characteristics. Our findings provide strong evidence for intermittent behavior of overflow controlled by tectonic processes and regional climatic change. These fluctuations in paleo-MOW intermittently influenced global ocean circulation

Predicting the severity of the grass pollen season and the effect of climate change in Northwest Europe

Allergic rhinitis is an inflammation in the nose caused by overreaction of the immune system to allergens in the air. Managing allergic rhinitis symptoms is challenging and requires timely intervention. The following are major questions often posed by those with allergic rhinitis: How should I prepare for the forthcoming season? How will the season's severity develop over the years? No country yet provides clear guidance addressing these questions. We propose two previously unexplored approaches for forecasting the severity of the grass pollen season on the basis of statistical and mechanistic models. The results suggest annual severity is largely governed by preseasonal meteorological conditions. The mechanistic model suggests climate change will increase the season severity by up to 60%, in line with experimental chamber studies. These models can be used as forecasting tools for advising individuals with hay fever and health care professionals how to prepare for the grass pollen season

Future MEDIC: Student-led Health Care Exploration for Underserved Youths

The Future Medical Education and Development Introductory Course (MEDIC) is a medical student-led program designed to inspire primary school students in underserved communities by sharing a passion for medicine through hands-on activities and interprofessional guest speakers. A five-point Likert scale pre- and postsurvey assessed the impact of Future MEDIC. Results were analyzed with Mann Whitney’s U Test. Students were engaged in interactive activities, although this did not correlate with an interest in joining the health care field. Students expressed that they had resources to pursue a medical career, and we hope that continuing Future MEDIC will strengthen students’ perception of achieving their career goals

Neighborhood Characteristics Associated with Loss to Follow-up in a Community-based Free Health Center

This study identified demographic/neighborhood factors associated with loss to follow-up (LTF) at Neighborhood Resilience Project’s Free Health Center. English-speaking African Americans residing in neighborhoods with moderate disadvantage are at lowest risk of LTF. These findings inform strategies to retain free clinic patients who may be more vulnerable to LTF

Late Miocene contourite channel system reveals intermittent overflow behavior

Paleoceanographic information from submarine overflows in the vicinity of oceanic gateways is of major importance for resolving the role of ocean circulation in modulating Earth’s climate. Earth system models are currently the favored way to study the impact of gateways on global-scale processes, but studies on overflow-related deposits are more suitable to understand the detailed changes. Such deposits, however, had not yet been documented in outcrop. Here, we present a unique late Miocene contourite channel system from the Rifian Corridor (Morocco) related to the initiation of Mediterranean Outflow Water (MOW). Two channel branches were identified consisting of three vertically stacked channelized sandstone units encased in muddy deposits. Both branches have different channel-fill characteristics. Our findings provide strong evidence for intermittent behavior of overflow controlled by tectonic processes and regional climatic change. These fluctuations in paleo-MOW intermittently influenced global ocean circulation

A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data

BackgroundHealth care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social media, and their performance in the health care context remains relatively unknown. Moreover, existing studies indicate that these tools often lack accuracy and produce inconsistent results. ObjectiveThis study aims to address the lack of comparative analysis on sentiment analysis tools applied to health-related free-text survey data in the context of COVID-19. The objective was to automatically predict sentence sentiment for 2 independent COVID-19 survey data sets from the National Institutes of Health and Stanford University. MethodsGold standard labels were created for a subset of each data set using a panel of human raters. We compared 8 state-of-the-art sentiment analysis tools on both data sets to evaluate variability and disagreement across tools. In addition, few-shot learning was explored by fine-tuning Open Pre-Trained Transformers (OPT; a large language model [LLM] with publicly available weights) using a small annotated subset and zero-shot learning using ChatGPT (an LLM without available weights). ResultsThe comparison of sentiment analysis tools revealed high variability and disagreement across the evaluated tools when applied to health-related survey data. OPT and ChatGPT demonstrated superior performance, outperforming all other sentiment analysis tools. Moreover, ChatGPT outperformed OPT, exhibited higher accuracy by 6% and higher F-measure by 4% to 7%. ConclusionsThis study demonstrates the effectiveness of LLMs, particularly the few-shot learning and zero-shot learning approaches, in the sentiment analysis of health-related survey data. These results have implications for saving human labor and improving efficiency in sentiment analysis tasks, contributing to advancements in the field of automated sentiment analysis

“Is there anything else you would like to tell us?”: An analysis of language features in text responses to a study on mental health during the COVID-19 pandemic

The COVID-19 pandemic and associated restrictions have been a major stressor and exacerbated mental health worldwide. Qualitative data play a unique role in documenting mental state, via both language features and content. Within a longitudinal study on mental health during the COVID-19 pandemic, we analyzed free responses to the question: “Is there anything else you would like to tell us that might be important that we did not ask about?” We applied text analysis methods to ask whether individuals who responded to the item differed from non- responders, whether there were associations between language use and psychological status, and to characterize the content of responses and how responses changed over time. 3,655 individuals provided biweekly measures of mental health and responses to the COVID-19 pandemic for 6 months. Of these, 2,497 participants provided at least one free response (9,741 total responses). Response likelihood was influenced by demographic factors and health status: Participants who were male, Asian, Black, or Hispanic were less likely to respond, and odds of responding increased with age and education as well as with a history of physical health conditions. Although mental illness history did not influence an individual’s overall likelihood of responding, it was associated with more negative sentiment, negative word use, and higher usage of first-person singular pronouns. Responses were dynamically influenced by psychological status, such that distress and loneliness were positively associated with an individuals’ likelihood to respond at a given timepoint, and predicted more negativity. Finally, responses were negative in valence overall, exhibited fluctuations linked with external events, and covered a variety of topics, with the most common being mental health and emotion. Our results identify trends in language use during the first year of the pandemic, and suggest that the both the content of responses and overall sentiment are linked to mental health

Test-retest reliability of an adaptive thermal pain calibration procedure in healthy volunteers

Quantitative sensory testing (QST) allows researchers to evaluate associations between noxious stimuli and acute pain in clinical populations and healthy participants. Despite its widespread use, our understanding of QST’s reliability is limited, as reliability studies have used small samples and restricted time windows. We examined the reliability of pain ratings in response to noxious thermal stimulation in 171 healthy volunteers (n = 99 female, n = 72 male) who completed QST on multiple visits ranging from 1 day to 952 days between visits. On each visit, participants underwent an adaptive pain calibration in which they experienced 24 heat trials and rated pain intensity after stimulus offset on a 0-10 Visual Analog Scale. We used linear regression to determine pain threshold, pain tolerance, and the correlation between temperature and pain for each session and examined the reliability of these measures. Threshold and tolerance were moderately reliable (Intra-class correlation [ICC]=0.66 and 0.67, respectively; p<.001), whereas temperature-pain correlations had low reliability (ICC=0.23). In addition, pain tolerance was significantly more reliable in female participants than male participants, and we observed similar trends for other pain sensitive measures. Our findings indicate that threshold and tolerance are largely consistent across visits, whereas sensitivity to changes in temperature vary over time and may be influenced by contextual factors