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The glandular odontogenic cyst is a rare developmental cyst with odontogenic origins that shares similarities with other odontogenic lesions. While generally considered benign, GOCs can exhibit aggressive behavior and may transform into more aggressive lesions like ameloblastoma or mucoepidermoid carcinoma. Therefore, it is crucial to closely monitor and follow up after treatment. With less than 200 reported cases in the literature, GOCs are important due to their propensity for recurrence and potential confusion with other lesions. Oral and maxillofacial radiologists play a key role in diagnosing GOC, given its complex histopathology. In this particular case, a 14-year-old male presented with a well-defined radiolucent lesion in his posterior mandibular ramus, making it the third reported case of GOC in Syria. The discovery of the lesion was incidental, as it was found during a routine panoramic view. The radiolucent lesion extended from the distal border and apex of the lower right first premolar into the ramus, indicating the need for thorough diagnostic evaluation and proper management of the condition. This case report emphasizes the importance of recognizing and addressing such lesions early on to prevent any potential complications. As GOCs can be locally aggressive and have a tendency to recur, timely diagnosis and appropriate treatment are crucial in ensuring the best possible outcome for the patient. Further studies and research are needed to better understand the characteristics and behavior of GOCs in the mandibular ramus region.

Abstract Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.

Large bone tumors that form in the bones of the upper and lower jaws in the mouth are among the diseases associated with dental and jaw lesions. These bony tumors forming in the jaws are an obstacle to performing dental prosthetics in the jaws, whether they are dental prosthetics or restorative ones. In this case report, we present a case of surgery on the jaws of a forty-year-old Syrian young man with the aim of removing a number of bone tumors formed in the jaw bones. The dental clinic at the International University of Science and Technology performed this surgery after it became clear that it was impossible to perform dental prosthetics in the jaws due to the presence of these bone tumors in both the upper and lower jaws. The surgical operation was performed successfully, and the tumorous lesions removed from the young man's upper and lower jaws were examined, and the jaws were prepared for full dental restorations afterwards.

Abstract While quantitative methods have been used to examine changes in word usage in books, studies have focused on overall trends, such as the shapes of narratives, which are independent of book length. We instead look at how words change over the course of a book as a function of the number of words, rather than the fraction of the book, completed at any given point; we define this measure as “cumulative word-time”. Using ousiometrics, a reinterpretation of the valence–arousal–dominance framework of meaning obtained from semantic differentials, we convert text into time series of power and danger scores, with time corresponding to cumulative word-time. Each time series is then decomposed using empirical mode decomposition into a sum of constituent oscillatory modes and a non-oscillatory trend. By comparing the decomposition of the original power and danger time series with those derived from shuffled text, we find that shorter books exhibit only a general trend, while longer books have fluctuations in addition to the general trend. These fluctuations typically have a period of a few thousand words regardless of the book length or library classification code but vary depending on the content and structure of the book. Our findings suggest that, in the ousiometric sense, longer books are not expanded versions of shorter books, but rather are more similar in structure to a concatenation of shorter texts. Further, they are consistent with editorial practices that require longer texts to be broken down into sections, such as chapters. Our method also provides a data-driven denoising approach that works for texts of various lengths, in contrast to the more traditional approach of using large window sizes that may inadvertently smooth out relevant information, especially for shorter texts. Altogether, these results open up avenues for future work in computational literary analysis, particularly the possibility of measuring a basic unit of narrative.

Abstract Online misogyny has become a fixture in female politicians’ lives. Backlash theory suggests that it may represent a threat response prompted by female politicians’ counterstereotypical, power-seeking behaviors. We investigated this hypothesis by analyzing Twitter references to Hillary Clinton before, during, and after her presidential campaign. We collected a corpus of over 9 million tweets from 2014 to 2018 that referred to Hillary Clinton, and employed an interrupted time series analysis on the relative frequency of misogynistic language within the corpus. Prior to 2015, the level of misogyny associated with Clinton decreased over time, but this trend reversed when she announced her presidential campaign. During the campaign, misogyny steadily increased and only plateaued after the election, when the threat of her electoral success had subsided. These findings are consistent with the notion that online misogyny towards female political nominees is a form of backlash prompted by their ambition for power in the political arena.

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. “Superspreading,” or “over-dispersion,” is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

The increasing volumes of data produced at light sources such as the Linac Coherent Light Source (LCLS) enable the direct observation of materials and molecular assemblies at the length and timescales of molecular and atomic motion. This exponential increase in the scale and speed of data production is prohibitive to traditional analysis workflows that rely on scientists tuning parameters during live experiments to adapt data collection and analysis. User facilities will increasingly rely on the automated delivery of actionable information in real time for rapid experiment adaptation which presents a considerable challenge for data acquisition, data processing, data management, and workflow orchestration. In addition, the desire from researchers to accelerate science requires rapid analysis, dynamic integration of experiment and theory, the ability to visualize results in near real-time, and the introduction of ML and AI techniques. We present the LCLS-II Data System architecture which is designed to address these challenges via an adaptable data reduction pipeline (DRP) to reduce data volume on-thefly, online monitoring analysis software for real-time data visualization and experiment feedback, and the ability to scale to computing needs by utilizing local and remote compute resources, such as the ASCR Leadership Class Facilities, to enable quasi-real-time data analysis in minutes. We discuss the overall challenges facing LCLS, our ongoing work to develop a system responsive to these challenges, and our vision for future developments.

Abstract We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur. In particular, we consider tweets with hashtags #imwithher and #crookedhillary, both relating to Hillary Clinton’s presidential bid in 2016. We then analyze the network properties in conjunction with the word scores by comparing with null models to separate the effects of the network structure and the score distribution. Neutral words are found to be dominant and most words, regardless of polarity, tend to co-occur with neutral words. We do not observe any score homophily among positive and negative words. However, when we perform network backboning, community detection results in word groupings with meaningful narratives, and the happiness scores of the words in each group correspond to its respective theme. Thus, although we observe no clear relationship between happiness scores and co-occurrence at the node or edge level, a community-centric approach can isolate themes of competing sentiments in a corpus.

The murder of George Floyd by police in May 2020 sparked international protests and brought unparalleled levels of attention to the Black Lives Matter movement. As we show, his death set record levels of activity and amplification on Twitter, prompted the saddest day in the platform’s history, and caused his name to appear among the ten most frequently used phrases in a day, where he is the only individual to have ever received that level of attention who was not known to the public earlier that same week. Importantly, we find that the Black Lives Matter movement’s rhetorical strategy to connect and repeat the names of past Black victims of police violence—foregrounding racial injustice as an ongoing pattern rather than a singular event—was exceptionally effective following George Floyd’s death: attention given to him extended to over 185 prior Black victims, more than other past moments in the movement’s history. We contextualize this rising tide of attention among 12 years of racial justice activism on Twitter, demonstrating how activists and allies have used attention and amplification as a recurring tactic to lift and memorialize the names of Black victims of police violence. Our results show how the Black Lives Matter movement uses social media to center past instances of police violence at an unprecedented scale and speed, while still advancing the racial justice movement’s longstanding goal to “say their names.”

Two-pore channels (TPCs) are cation channels that localize to acidic organelles to regulate Ca2+ dependent events. Here, Li et al. characterize a TPC from Toxoplasma gondii(TgTPC) that localizes to the apicoplast, is critical for maintaining its integrity and relevant for Ca2+ uptake from the ER through stabilizing inter-organelle contact.

Abstract Sleep loss has been linked to heart disease, diabetes, cancer, and an increase in accidents, all of which are among the leading causes of death in the United States. Population-scale sleep studies have the potential to advance public health by helping to identify at-risk populations, changes in collective sleep patterns, and to inform policy change. Prior research suggests other kinds of health indicators such as depression and obesity can be estimated using social media activity. However, the inability to effectively measure collective sleep with publicly available data has limited large-scale academic studies. Here, we investigate the passive estimation of sleep loss through a proxy analysis of Twitter activity profiles. We use “Spring Forward” events, which occur at the beginning of Daylight Savings Time in the United States, as a natural experimental condition to estimate spatial differences in sleep loss across the United States. On average, peak Twitter activity occurs 15 to 30 min later on the Sunday following Spring Forward. By Monday morning however, activity curves are realigned with the week before, suggesting that the window of sleep opportunity is compressed in Twitter data, revealing Spring Forward behavioral change.

Background:. Facial feminization surgery (FFS) is an integral aspect of gender-affirming surgery (GAS) for individuals seeking to align secondary sex characteristics and gender identity. Despite the importance of FFS in treating gender dysphoria, current trends and prevalence remain unknown. We sought to examine trends in GAS and FFS and investigate the payer status of facial feminization procedures in the United States. Methods:. Data was extracted from the National Inpatient Sample from 2008 to 2017 by using International Classification of Diseases Ninth or Tenth diagnosis codes for gender identity disorder and procedure codes for FFS. Results:. From 2008 to 2017, 3015 patients underwent GAS. The yearly number of cases increased as did the average cost of GAS, which rose from $13,657 in 2008 to $50,789 in 2017. From 2015 to 2017, when FFS data was available, 110 of 1215 (9.1%) GAS patients had FFS. Most were non-Hispanic White (66.7%) or Black (23.8%). Fifty percent of FFS cases occurred in the West, followed by the Northeast (31.8%), South (13.6%), and Midwest (4.8%) (P = 0.015). By payer the cases were, 36.4% self-pay, 31.8% Medicaid, and 27.3% private insurance (P < 0.0001). Approximately, 18% of patients undergoing male-to-female transition received FFS. Conclusions:. From 2008 to 2017, GAS cases increased nationwide while the average cost of surgery rose steeply. FFS cases were primarily in the Western and Northeast United States. Despite high cost, roughly 18% of transgender women in our sample received FFS, highlighting the importance of FFS in gender transition.

Abstract Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being the most dominant. To quantify social spreading in each language over time, we compute the ‘contagion ratio’: The balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1—the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.

In the last century, the paradigm of fear conditioning has greatly evolved in a variety of scientific fields. The techniques, protocols, and analysis methods now most used have undergone a progressive development, theoretical and technological, improving the quality of scientific productions. Fear-induced bradycardia is among these techniques and represents the temporary deceleration of heart beats in response to negative outcomes. However, it has often been used as a secondary measure to assess defensive responding to threat, along other more popular techniques. In this review, we aim at paving the road for its employment as an additional tool in fear conditioning experiments in humans. After an overview of the studies carried out throughout the last century, we describe more recent evidence up to the most contemporary research insights. Lastly, we provide some guidelines concerning the best practices to adopt in human fear conditioning studies which aim to investigate fear-induced bradycardia.

As a 19 year old young man, Thayer Greene had his “close encounter” with the great world conflict at the end of winter of 1945, as he was deployed as an infantryman to Europe and took part in the battle for Cologne. In this article, the author recollects a few of his relevant close encounters with war and with the emotions that characterised those moments, from fear to hilariousness, up to sheer terror that eventually haunted him for decades as PTSD. This long suppressed spiritual suffering urged him to train in Jungian psychotherapy in the 1960s and finally to deal with his traumatic memories in the late 1970s. This text tells the story of that journey from trauma to healing.

Background:. No consensus exists on ideal perioperative management or anticoagulation regimen for free flap reconstruction of the head and neck. Perceived benefits from antiplatelet therapy need to be balanced against potential complications. Ketorolac, a platelet aggregation inhibitor and a parenteral analgesic, was introduced as part of a standardized perioperative protocol at our institution. In this study, we aimed to examine the impact of implementation of this protocol as well as complications associated with the routine use of perioperative ketorolac in a diverse group of patients who underwent head and neck free flap reconstruction. Methods:. A single institution retrospective review was performed, including all patients who underwent head and neck free flap reconstruction between October 2016 and November 2019. Patients were divided into two cohorts: those who received ketorolac as part of a standardized protocol, and those who did not. Results:. Twenty-four consecutive patients with 24 head and neck free flaps were evaluated. Eighteen patients were in the standard protocol, and six were not. There were no microvascular thromboses, flap failures, or hematomas in either group. Intensive care unit length of stay and opiate use were significantly reduced in the standardized protocol group. Conclusions:. A standardized perioperative protocol for head and neck free flap reconstruction can reduce hospital and intensive care unit length of stay. No statistically significant differences in complication rates were identified when comparing ketorolac use and perioperative regimens among patients undergoing a diverse set of microsurgical head and neck free flap reconstructions.

BackgroundMental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group. ObjectiveBy measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health. MethodsWe explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification. ResultsWe found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time. ConclusionsThese results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.

Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.

Background and study aims The COVID-19 pandemic has had a profound impact on gastroenterology training programs. We aimed to objectively evaluate procedural training volume and impact of COVID-19 on gastroenterology fellowship programs in the United States. Methods This was a retrospective, multicenter study. Procedure volume data on upper and lower endoscopies performed by gastroenterology fellows was abstracted directly from the electronic medical record. The study period was stratified into 2 time periods: Study Period 1, SP1 (03/15/2020 to 06/30/2020) and Study Period 2, SP2 (07/01/2020 to 12/15/2020). Procedure volumes during SP1 and SP2 were compared to Historic Period 1 (HP1) (03/15/2019 to 06/30/2019) and Historic Period 2 (HP2) (07/01/2019 to 12/15/2019) as historical reference. Results Data from 23 gastroenterology fellowship programs (total procedures = 127,958) with a median of 284 fellows (range 273–289; representing 17.8 % of all trainees in the United States) were collected. Compared to HP1, fellows performed 53.6 % less procedures in SP1 (total volume: 28,808 vs 13,378; mean 105.52 ± 71.94 vs 47.61 ± 41.43 per fellow; P

Abstract We introduce a qualitative, shape-based, timescale-independent time-domain transform used to extract local dynamics from sociotechnical time series—termed the Discrete Shocklet Transform (DST)—and an associated similarity search routine, the Shocklet Transform And Ranking (STAR) algorithm, that indicates time windows during which panels of time series display qualitatively-similar anomalous behavior. After distinguishing our algorithms from other methods used in anomaly detection and time series similarity search, such as the matrix profile, seasonal-hybrid ESD, and discrete wavelet transform-based procedures, we demonstrate the DST’s ability to identify mechanism-driven dynamics at a wide range of timescales and its relative insensitivity to functional parameterization. As an application, we analyze a sociotechnical data source (usage frequencies for a subset of words on Twitter) and highlight our algorithms’ utility by using them to extract both a typology of mechanistic local dynamics and a data-driven narrative of socially-important events as perceived by English-language Twitter.

The physiological impact of walking in nature was quantified via continuous heart rate variability (HRV), pre- and post-walk saliva cortisol measures, and self-reported mood and mindfulness scores for N = 17 participants who walked The Green Road at Walter Reed National Military Medical Center in Bethesda, Maryland. For N = 15 of the participants, HRV analysis revealed two main groups: group one individuals had a 104% increase (mean) in the root mean square standard deviation (RMSSD) and a 47% increase (mean) in the standard deviation of NN values (SDNN), indicating an overall reduction in physiological stress from walking the Green Road, and group two individuals had a decrease (mean) of 42% and 31% in these respective HRV metrics, signaling an increase in physiological stresses. Post-walk self-reported scores for vigor and mood disturbance were more robust for the Green Road than for a comparable urban road corridor and showed that a higher HRV during the walk was associated with improved overall mood. Saliva cortisol was lower after taking a walk for all participants, and it showed that walking the Green Road elicited a significantly larger reduction in cortisol of 53%, on average, when compared with 37% of walking along an urban road. It was also observed that the order in which individuals walked the Green Road and urban road also impacted their cortisol responses, with those walking the urban road before the Green Road showing a substantial reduction in cortisol, suggesting a possible attenuation effect of walking the Green Road first. These findings provide quantitative data demonstrating the stress-reducing effects of being in nature, thus supporting the health benefit value of providing access to nature more broadly in many settings.

The study explores whether racial identity and appearance-based trustworthiness judgments can affect recognition of pain in medical students differing in levels of resting heart rate variability (HRV), a measure of parasympathetic control of the heart. After undergoing HRV assessment, 68 medical students (37 females) participated in a dynamic pain recognition task, viewing video clips of White and Black faces, which differed in perceived trustworthiness based on facial appearance, transitioning from neutral to intense pain expressions. Response time, pain intensity attribution and treatment recommendations were analyzed. Pain was recognized slower and estimated as less intense in Black compared to White faces, leading to a lower likelihood of recommending therapy. Pain recognition was faster for untrustworthy-looking White faces compared to trustworthy ones, while perceived trustworthiness had a minimal impact on the speed of pain recognition in Black faces. However, untrustworthy-looking faces were estimated to express more pain, particularly for Black faces. Notably, these biases were more pronounced in individuals with low, rather than high, resting HRV. Considering that therapeutic decisions mirrored pain intensity attribution, it would be important to increase awareness of these biases during medical training in order to promote equity in future pain assessment and treatment.

BackgroundCystic fibrosis (CF) is a rare, life-shortening, multiorgan disease, the treatment of which has seen significant increases in the life expectancy of those with CF. Many advances in CF care are thanks to the dedicated and active participation of people with CF as research participants. Unfortunately, most CF research teams still do not fully partner with people with CF or their caregivers. ObjectiveThe aim of this study was to determine the interest, knowledge gaps, and desired format for patient-centered outcomes research (PCOR) training in the CF community. MethodsWe surveyed patients, caregivers, researchers, research staff, and diverse health care providers via list servers and social media outreach about their knowledge of, experience with, and preferences for PCOR training components. We followed the survey with 3 small-group discussion sessions with 22 participants who completed the survey to establish consensus and prioritize key learning components of a PCOR training program. We summarized results using descriptive statistics. ResultsA total of 170 participants completed the survey (patients/caregivers: 96/170, 56.5%; researchers/health care providers: 74/170, 43.5%). Among providers, 26% (19/74) were physicians/advanced practice providers, 20% (15/74) were nurses, and 54% (40/74) were from other disciplines. Among all participants, 86.5% (147/170) expressed interest in learning about PCOR, although training topics and training format differed between the patient/caregiver and researcher/health care provider groups. Before participating in PCOR, patients/caregivers wanted to understand more about expectations of them as partners on PCOR research teams (82/96, 85%). Meanwhile, researchers/health care providers desired information on how to include outcomes important to patients/caregivers (55/74, 74%) and the quality and impact of PCOR research (52/74, 70% and 51/74, 69%, respectively). Patients/caregivers were most interested in learning about the time commitment as a PCOR team member (75/96, 78%). Researchers/health care providers wanted to receive training about how to establish trust (47/74, 64%) and maintain confidentiality (47/74, 64%) when including patient or caregiver partners on the PCOR team. During follow-up discussions, participants emphasized the importance of addressing the traditional patient/caregiver and researchers/health care provider hierarchy by teaching about transparency, appreciation, creating a common language between the groups, and providing specific training on “how” to do PCOR. ConclusionsOur findings suggest CF community members are interested in PCOR. A high-quality training program would fill a current deficit in methodological research. This assessment identified the topics and formats desired and can be used to develop targeted training to enhance meaningful PCOR in CF.