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Key Clinical Message Button battery ingestion has been a common condition encountered by otorhinolaryngologists. Impaction in the esophagus can lead to serious and fatal complications such as tracheoesophageal fistula. Management involves a multidisciplinary team and varies from supportive therapy to surgical intervention. Abstract Ingestion of button batteries has been seen with increasing frequency over the past decade. In several small numbers of reported cases, their impaction in the esophagus has led to severe, sometimes fatal, complications. The management of these cases has varied from expectant, supportive therapy to early surgical intervention. We report a case of button battery ingestion that was diagnosed late and resulted in a complication of tracheoesophageal fistula with migration to the thorax which was managed by open surgery.

Using a novel dataset of 590M messages by 21M users, we present the first large-scale examination of the behavior of likely Bernie supporters on Twitter during the 2020 U.S. Democratic primaries and presidential election. We use these data to dispel empirically the notion of a unified, stereotypical Bernie supporter (e.g., the "Bernie Bro"). Instead, our work uncovers significant variation in the identities and ideologies of Bernie supporters who were active on Twitter. Our work makes three contributions to the literature on social media and social movements. Methodologically, we present a novel mixed methods approach to surface identity and ideological variation within a movement via use of patterns in who retweets whom (i.e. who retweets which other users) and who retweets what (i.e. who retweets which specific tweets). Substantively, documentation of these variations challenges a trend in the social movement literature to assume actors within a particular movement are unified in their ideology, identity, and values.

Abstract We propose and extend a qualitative, complex systems methodology from cognitive engineering, known as the abstraction hierarchy, to model how potential interventions that could be carried out by social media platforms might impact social equality. Social media platforms have come under considerable ire for their role in perpetuating social inequality. However, there is also significant evidence that platforms can play a role in reducing social inequality, e.g. through the promotion of social movements. Platforms’ role in producing or reducing social inequality is, moreover, not static; platforms can and often do take actions targeted at positive change. How can we develop tools to help us determine whether or not a potential platform change might actually work to increase social equality? Here, we present the abstraction hierarchy as a tool to help answer this question. Our primary contributions are two-fold. First, methodologically, we extend existing research on the abstraction hierarchy in cognitive engineering with principles from Network Science. Second, substantively, we illustrate the utility of this approach by using it to assess the potential effectiveness of a set of interventions, proposed in prior work, for how online dating websites can help mitigate social inequality.

This paper proposes a novel method to improve georeferencing of airborne laser scanning by improved trajectory estimation using Vehicle Dynamic Model. In Vehicle Dynamic Model (VDM), the relationship between the dynamics of the platform and control inputs is used as additional observations for sensor fusion. This relationship is available for most platforms and can be used without the need for additional hardware. However, this relationship is modeled using parameters that are a priori unknown. The proposed in-flight calibration methodology can achieve less than 2% error in the estimated model parameters compared to the values used in simulation. The effect of Inertial Measurement Unit (IMU) noise on the accuracy of airborne laser scanning is further investigated to demonstrate the reduction in the position error of georeferenced points when VDM measurements are used. The results are evaluated through a Monte-Carlo simulation involving an open-source autopilot. The reduction in the error of the estimated attitude due to vehicle modeling increases with the higher intensity of time-correlated IMU noise. Using a higher quality inertial sensor does not lead to an improvement in the position error of georeferenced points when VDM measurements are employed; however, a lower quality Inertial Measurement Unit, such as those on an autopilot, shows a 33% and 46% reduction in the mean and standard deviation of the position error of the georeferenced points, respectively.

The term glass ceiling is applied to the well-established phenomenon in which women and people of color are consistently blocked from reaching the uppermost levels of the corporate hierarchy. Focusing on gender, we present an agent-based model that explores how empirically established mechanisms of interpersonal discrimination coevolve with social norms at both the organizational (meso) and societal (macro) levels to produce this glass ceiling effect for women. Our model extends the understanding of how the glass ceiling arises and why it can be resistant to change. We do so by synthesizing existing psychological and structural theories of discrimination into a mathematical model that quantifies explicitly how complex organizational systems can produce and maintain inequality. We discuss implications of our findings for both intervention and future empirical analyses and provide open-source code for those wishing to adapt or extend our work.

Artisanal and small-scale mining (ASM) – small sized, largely unrecognized, rudimentary, and an informal form of mining – occurs in more than 70 countries around the world and is mainly hailed for its socioeconomic benefits and reviled for its environmental devastation. As a result, many people are confused about the future of ASM. In Ghana, the government banned ASM in 2017 and formed a security taskforce drawn from the military and police to crack down on nomadic and local ASM workers who defy the ban. This approach is unsustainable, deals less with the fundamental problems, and increases poverty among the already impoverished local populations who depend on this type of mining as their only means of livelihood. To support the argument for sustainable reforms, revenue growth decomposition and growth accounting analyses were performed to determine the factors shaping ASM revenue over 25 years (1990–2016). Results show that production (gold output) is the most important factor that influences revenue growth from ASM, contrary to the usual view that the price of the metal is mainly responsible for the increase in revenue. Thus, increasing labor hours in ASM could significantly increase mining revenue, reduce unemployment, and improve local commerce. We strongly conclude that sustainable reforms such as increasing local participation in decision making, education and training, adoption of improved technology, strengthening regulatory institutions, legislation and enforcement of enactments, and the provision of technical support and logistics could ensure socio-environmental sustainability. Keywords: Artisanal and small-scale mining, Environmental pollution and sustainability, ASM formalization, Livelihood, Mercury and heavy metals

Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of issues have been identified with the machine learning models used to analyze social data. These issues range from technical problems with the data used and features constructed, to problematic modeling assumptions, to limited interpretability, to the models' contributions to bias and inequality. Computational researchers have sought out technical solutions to these problems. The primary contribution of the present work is to argue that there is a limit to these technical solutions. At this limit, we must instead turn to social theory. We show how social theory can be used to answer basic methodological and interpretive questions that technical solutions cannot when building machine learning models, and when assessing, comparing, and using those models. In both cases, we draw on related existing critiques, provide examples of how social theory has already been used constructively in existing work, and discuss where other existing work may have benefited from the use of specific social theories. We believe this paper can act as a guide for computer and social scientists alike to navigate the substantive questions involved in applying the tools of machine learning to social data.

Purpose: This pilot study sought to determine the efficacy of using the Developmental Eye Movement (DEM) test in the adult, acquired brain injury (ABI) population to quantify clinically the effects of controlled, laboratory-performed, oculomotor-based vision therapy/vision rehabilitation. Methods: Nine adult subjects with mild traumatic brain injury (mTBI) and five with stroke were assessed before and after an eight-week, computer-based, versional oculomotor (fixation, saccades, pursuit, and simulated reading) training program (9.6 h total). The protocol incorporated a cross-over, interventional design with and without the addition of auditory feedback regarding two-dimensional eye position. The clinical outcome measure was the Developmental Eye Movement (DEM) test score (ratio, errors) taken before, midway, and immediately following training. Results: For the DEM ratio parameter, improvements were found in 80–89% of the subjects. For the DEM error parameter, improvements were found in 100% of the subjects. Incorporation of the auditory feedback component revealed a trend toward enhanced performance. The findings were similar for both DEM parameters, as well as for incorporation of the auditory feedback, in both diagnostic groups. Discussion: The results of the present study demonstrated considerable improvements in the DEM test scores following the oculomotor-based training, thus reflecting more time-optimal and accurate saccadic tracking after the training. The DEM test should be considered as another clinical test of global saccadic tracking performance in the ABI population. Resumen: Objetivo: Este estudio piloto trató de determinar la eficacia del uso de la prueba DEM (Developmental Eye Movement) en la población adulta con daño cerebral adquirido (DCA) para cuantificar clínicamente los efectos de la rehabilitación/terapia visual controlada, realizada en laboratorio, y de carácter oculomotor. Métodos: Se valoraron nueve sujetos adultos con daño cerebral traumático leve (mTBI) y cinco con ictus, con anterioridad y posterioridad a un programa de entrenamiento de ocho semanas, informático oculomotor y versional (movimientos de fijación, sacádicos, de persecución y lectura simulada, de 9,6 horas en total). El protocolo incorporó un diseño cruzado e intervencional, con y sin adición de retroalimentación auditiva en relación a la posición bi-dimensional de los ojos. La medida del resultado clínico se llevó a cabo mediante la puntuación de la prueba DEM (Developmental Eye Movement) (ratio, errores) realizada con anterioridad, en el punto medio, e inmediatamente tras el entrenamiento. Resultados: Para el parámetro de ratio DEM, se encontraron mejoras en el 80-89% de los sujetos. Para el parámetro de error DEM, se encontraron mejoras en el 100% de los sujetos. La incorporación del componente de retroalimentación auditiva reveló una tendencia hacia la mejora del rendimiento. Los hallazgos fueron similares para ambos parámetros DEM, así como para la incorporación de la retroalimentación auditiva, en ambos grupos diagnósticos. Discusión: Los resultados del presente estudio demostraron mejoras considerables de las puntuaciones de la prueba DEM tras la terapia oculomotora, lo que refleja una mayor optimización y precisión del seguimiento sacádico en el tiempo tras la terapia. Deberá considerarse la prueba DEM como una prueba clínica para valorar el rendimiento del seguimiento sacádico global en la población de DCA. Keywords: Traumatic brain injury (TBI), Cerebral vascular accident (CVA)/stroke, Developmental Eye Movement (DEM) test, Eye movements, Neuro-optometric rehabilitation, Palabras clave: Daño cerebral traumático (DCT), Accidente cerebro-vascular (ACV)/ictus, Prueba DEM (Developmental Eye Movement), Movimientos oculares, Rehabilitación neuro-optométrica

Inadequate energy supply is one of the major problems confronting Malawi and limiting its social, economic and industrial development. This paper reviews the current status of energy supply and demand in Malawi; examines the major sources of energy, current exploitation status and their potential contribution to the electricity supply of the country; discusses key issues facing the energy sector; and identifies broad strategies to be implemented to tackle the energy supply challenges. Using secondary data for its critical analysis, the paper also presents modelling of long-term energy demand forecast in the economic sectors of Malawi using the Model for Analysis of Energy Demand (MAED) for a study period from 2008-2030. Three scenarios namely reference (REF), moderate growth (MGS) and accelerated growth (AGS) were formulated to simulate possible future long-term energy demand based on socio-economic and technological development with the base year of 2008. Results from all scenarios suggest an increased energy demand in consuming sectors with biomass being a dominant energy form in household and industry sectors in the study period. Forecast results reveal that energy demand will increase at an annual growth rate of 1.2% and reach 5160 ktoe in 2030 under REF scenario. The growth rates for MGS and AGS are projected at 1.5% each reaching 4639 ktoe and 5974 ktoe in 2030, respectively. The final electricity demand of about 105 ktoe in the base year will grow annually at average rates of 13.8%, 15.3% and 12.6% for REF, AGS and MGS, respectively. Over the study period 2008-2030 the annual electricity per capita will increase from about 111 kWh to 1062, 1418 and 844 kWh for the REF, AGS and MGS, respectively. The final energy intensity will decrease continuously from about 13.71 kWh/US$ in the base year to 3.88 kWh/US$, 2.98 kWh/US$ and 5.27 kWh/US$ for the REF, AGS and MGS, respectively in the year 2030. In conclusion, the paper outlines strategies that could be utilized to ensure adequate supply of modern energy which is a key ingredient for achieving sustainable social and economic growth.

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS' unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and "lone wolf" attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS' sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group's propaganda dissemination through retweets.

Patients in whom the standard of care has failed or who have uncommon tumors for which no standard of care exists are often treated with drugs selected based on the physician’s best guess. The rate of success for this method is generally low. With the advent of fast, affordable tumor profiling technologies, and a growth in the understanding of predictive biomarkers, it is now possible to identify drugs potentially associated with clinical benefit for such patients. We present the Caris approach to evidence-based tumor profiling and two patients with advanced ovarian and prostate cancer in whom standard of care had failed and tumor profiling identified an effective treatment schedule. To establish Caris Molecular Intelligence™ (CMI), over 120,000 clinical publications were screened and graded to characterize the predictive value of biomarkers that form the panel of tests. CMI includes multiple technologies to measure changes in proteins, ribonucleic acid (RNA), and deoxyribonucleic acid (DNA) and proprietary software that matches the test results with the published evidence. The CMI results enable physicians to select drugs that are more likely to benefit the patients, avoid drugs that are not likely to work, and find treatment options that otherwise would not be considered. Worldwide, over 60,000 cancer patients have undergone evidence based tumor profiling with CMI. In the cases reported in this article, CMI identified treatments that would not have been routinely used in the respective clinical setting. The clinical outcomes observed help to illustrate the utility of this approach.