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BackgroundThe current opioid crisis in the United States impacts broad population groups, including pregnant women. Opioid use during pregnancy can affect the health and wellness of both mothers and their infants. Understanding women’s efforts to self-manage opioid use or misuse in pregnancy is needed to identify intervention points for improving maternal outcomes. ObjectiveThis study aims to identify the characteristics of women in an online health community (OHC) with opioid use or misuse during pregnancy and the self-management support needs of these mothers. MethodsA total of 200 web posts by pregnant women with opioid use participating in an OHC were double coded. Concepts and their thematic connections were identified through an inductive process until theoretical saturation was reached. Statistical tests were performed to identify patterns. ResultsThe majority of pregnant women (150/200, 75.0%) in the OHC exhibited signs of misuse, and 62.5% (125/200) of the participants were either contemplating or pursuing dosage reduction. Self-managed withdrawal was more common (P

Accumulating evidence has implicated impaired extracellular matrix (ECM) clearance as a key factor in fibrotic disease. Despite decades of research elucidating the effectors of ECM clearance, relatively little is understood regarding the upstream regulation of this process. Collagen is the most abundant constituent of normal and fibrotic ECM in mammalian tissues. Its catabolism occurs through extracellular proteolysis and cell-mediated uptake of collagen fragments for intracellular degradation. Given the paucity of information regarding the regulation of this latter process, here we execute unbiased genome-wide screens to understand the molecular underpinnings of cell-mediated collagen clearance. Using this approach, we discover a mechanism through which collagen biosynthesis is sensed by cells internally and directly regulates clearance of extracellular collagen. The sensing mechanism appears to be dependent on endoplasmic reticulum-resident protein SEL1L and occurs via a noncanonical function of this protein. This pathway functions as a homeostatic negative feedback loop that limits collagen accumulation in tissues. In human fibrotic lung disease, the induction of this collagen clearance pathway by collagen synthesis is impaired, thereby contributing to the pathological accumulation of collagen in lung tissue. Thus, we describe cell-autonomous, rheostatic collagen clearance as an important pathway of tissue homeostasis.

BackgroundLittle is known about the ability of individual stakeholder groups to achieve health information dissemination goals through Twitter. ObjectiveThis study aimed to develop and apply methods for the systematic evaluation and optimization of health information dissemination by stakeholders through Twitter. MethodsTweet content from 1790 followers of @SafetyMD (July-November 2012) was examined. User emphasis, a new indicator of Twitter information dissemination, was defined and applied to retweets across two levels of retweeters originating from @SafetyMD. User interest clusters were identified based on principal component analysis (PCA) and hierarchical cluster analysis (HCA) of a random sample of 170 followers. ResultsUser emphasis of keywords remained across levels but decreased by 9.5 percentage points. PCA and HCA identified 12 statistically unique clusters of followers within the @SafetyMD Twitter network. ConclusionsThis study is one of the first to develop methods for use by stakeholders to evaluate and optimize their use of Twitter to disseminate health information. Our new methods provide preliminary evidence that individual stakeholders can evaluate the effectiveness of health information dissemination and create content-specific clusters for more specific targeted messaging.

PRCIS: Intraoperative anterior chamber (AC) washout reduces the odds of hypertensive phase (HP) by 95% compared with those not receiving washout during Ahmed glaucoma implant (AGI), suggesting AC washout is an effective prophylaxis for postoperative HP. PURPOSE: To characterize the incidence of postoperative HP in patients receiving AC washout at the time of AGI. PATIENTS AND METHODS: A total of 24 patients with medically refractory glaucoma who underwent AGI surgery at a tertiary academic medical center in Southern California from December 2018 through March 2021 were included in this retrospective comparative case series. Patients who received a pediatric implant and underwent concurrent intraocular surgery or did not complete a minimum of 6 months of follow-up were excluded. Nine patients eyes received AC washout and 15 controls that were analyzed through 6 months postprocedure. HP was defined as intraocular pressure (IOP) above 21 mm Hg within 6 months postprocedure with maximum tolerated medical therapy. AC washout was performed by irrigating the AC with a 5 mL balanced salt solution before placing the tube of the AGI into the AC. The rate of postoperative HP, defined as peak IOP >21 mm Hg, at 6 months follow-up, was observed as the primary outcome measure. RESULTS: Patients included in the study had a high mean preoperative IOP ( X̅ = 44.11, SD = 13.85). There were no baseline differences between the washout and control groups. The odds of HP were significantly reduced (odds ratio: 0.050; 95% CI: 0.004, 0.706; P = 0.027) for patients who underwent intraoperative AC washout compared with those who did not. Bivariate analysis of subject baseline characteristics revealed that only washout status was significantly different in subjects with HP compared with subjects without HP ( P = 0.015). A multivariate logistic regression model using washout status and autoimmune conditions as covariates was significant in predicting HP ( X2 (2) = 12.337, P = 0.002), with washout as a significant predictor when controlling for autoimmune comorbidities ( P = 0.027). CONCLUSIONS: AC washout significantly decreases the odds of HP after AGI surgery. AC washout with balanced salt solution adds minimal time and risks to surgery and therefore may be an effective adjunct during AGI placement.

Enterocytes modulate the extent of postprandial lipemia by storing dietary fats in cytoplasmic lipid droplets (cLDs). We have previously shown that the integrin ligand MFGE8 links absorption of dietary fats with activation of triglyceride (TG) hydrolases that catabolize cLDs for chylomicron production. Here, we identify CES1D as the key hydrolase downstream of the MFGE8-αvβ5 integrin pathway that regulates catabolism of diet-derived cLDs. Mfge8 knockout (KO) enterocytes have reduced CES1D transcript and protein levels and reduced protein levels of the transcription factor HNF4γ. Both Ces1d and Hnf4γ KO mice have decreased enterocyte TG hydrolase activity coupled with retention of TG in cLDs. Mechanistically, MFGE8-dependent fatty acid uptake through CD36 stabilizes HNF4γ protein level; HNF4γ then increases Ces1d transcription. Our work identifies a regulatory network that regulates the severity of postprandial lipemia by linking dietary fat absorption with protein stabilization of a transcription factor that increases expression of hydrolases responsible for catabolizing diet-derived cLDs.

Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence the learners' progress for self-regulated learning (SRL) and course achievements. However, learners tend to be unsure about how note-taking is performed using online learning materials and lack knowledge of effective strategies for SRL. Furthermore, few studies have investigated blended learners' sequential patterns of e-book note-taking for SRL. Thus, in this paper, an exploratory study was conducted in an undergraduate course that implemented the BL design. The learning task for the blended learners in the present study was to study the learning material using BookRoll, an e-book system, during in-class and out-of-class learning sessions. Lag sequential analysis of the e-book learning behavior data was conducted to identify the blended learners' sequential behaviors of e-book note-taking for the cognitive strategy use of SRL. Moreover, the difference between higher- and lower-achievement blended learners in terms of their sequential behaviors of e-book note-taking for SRL was revealed. This study can help educators provide evidence-based educational feedback to learners regarding the identified sequential patterns of e-book note-taking that can be applied as effective strategies for promoting the cognitive strategy use of SRL and improvement of course achievement in BL.

BackgroundExpressing emotion in online support communities is an important aspect of enabling e-patients to connect with each other and expand their social resources. Indirectly it increases the amount of support for coping with health issues. Exploring the supportive interaction patterns in online health social networking would help us better understand how technology features impacts user behavior in this context. ObjectiveTo build on previous research that identified different types of social support in online support communities by delving into patterns of supportive behavior across multiple computer-mediated communication formats. Each format combines different architectural elements, affecting the resulting social spaces. Our research question compared communication across different formats of text-based computer-mediated communication provided on the MedHelp.org health social networking environment. MethodsWe identified messages with nurturant support (emotional, esteem, and network) across three different computer-mediated communication formats (forums, journals, and notes) of an online support community for alcoholism using content analysis. Our sample consisted of 493 forum messages, 423 journal messages, and 1180 notes. ResultsNurturant support types occurred frequently among messages offering support (forum comments: 276/412 messages, 67.0%; journal posts: 65/88 messages, 74%; journal comments: 275/335 messages, 82.1%; and notes: 1002/1180 messages, 84.92%), but less often among messages requesting support. Of all the nurturing supports, emotional (ie, encouragement) appeared most frequently, with network and esteem support appearing in patterns of varying combinations. Members of the Alcoholism Community appeared to adapt some traditional face-to-face forms of support to their needs in becoming sober, such as provision of encouragement, understanding, and empathy to one another. ConclusionsThe computer-mediated communication format may have the greatest influence on the supportive interactions because of characteristics such as audience reach and access. Other factors include perception of community versus personal space or purpose of communication. These results lead to a need for further research.

The application of student interaction data is a promising field for blended learning (BL), which combines conventional face-to-face and online learning activities. However, the application of online learning technologies in BL settings is particularly challenging for students with lower self-regulatory abilities. In this study, a personalized learning analytics (LA) intervention approach that incorporates ebook and recommendation systems is proposed. The proposed approach provides students with actionable feedback regarding personalized remedial actions as the intervention to help them to strategically engage in the use of the ebook system and avoid academic failure when engaged in BL. A quasi-experiment was conducted to examine two classes of an undergraduate course that implemented a conventional BL model. The experimental group comprised 45 students from one class who learned using the proposed approach and received personalized intervention, whereas the control group comprised 42 students from the other class who learned using the conventional BL approach without receiving personalized intervention. The experimental results indicated that the proposed approach can improve students' learning achievements and behavioral engagement in BL. The findings provide pedagogical insights into the application of LA intervention with actionable feedback in BL environments.

Language learners' engagement with a specific task is crucial to improving their academic achievement. To enhance student engagement and academic achievement in language learning, personalized language learning (PLL) can be employed to consider individual learning needs. Personalized review learning has emerged to facilitate PLL as a promising means of enhancing the long-term preservation of skills and knowledge in language education. In this paper, a personalized review learning approach is proposed that improves behavioral engagement and academic achievement in language learning through e-books. It involves implementing an e-book system, namely BookRoll, which allows users to browse uploaded learning materials anytime and anywhere, in concert with a personalized review learning system based on repeated retrieval practice. To evaluate the effects of this approach, a quasi-experiment was conducted on two classes of sophomore undergraduate students majoring in accounting who were enrolled in a Japanese course. 47 students from one class were assigned to an experimental group, whereas 44 students from another class were assigned to a control group. The duration of the experiment was 8 weeks. The experimental group learned using both the e-book system and personalized review learning system, whereas the control group learned only using the e-book system. The experimental group significantly outperformed the control group in terms of both behavioral engagement and academic achievement. The findings indicate that the proposed approach enhanced the students' PLL experiences.

A challenge in learning phonological grammars is learning how phonological processes interact. It has been argued that some process interactions are easier to learn than others. One basis for this argument is asymmetries observed in experimental settings: artificial languages generated from certain process interactions are more likely to be successfully reproduced by participants than others. In this paper, we argue that asymmetries in production do not necessarily provide direct support that some phonological interactions are easier to learn. Rather, we show that these asymmetries can instead emerge due to differences in the number of consistent or nearly-consistent grammars each pattern has. We present a noisy channel model of morpho-phonological learning and apply it to a recent behavioral study examining the learnability of phonological process interactions. We find that, due to the relative difference in the number of grammars that can exactly match or nearly match the observed data, the model achieves the same qualitative results as those observed in experimental settings.

The role of integrins, in particular αv integrins, in regulating insulin resistance is incompletely understood. We have previously shown that the αvβ5 integrin ligand milk fat globule epidermal growth factor like 8 (MFGE8) regulates cellular uptake of fatty acids. In this work, we evaluated the impact of MFGE8 on glucose homeostasis. We show that acute blockade of the MFGE8/β5 pathway enhances while acute augmentation dampens insulin-stimulated glucose uptake. Moreover, we find that insulin itself induces cell-surface enrichment of MFGE8 in skeletal muscle, which then promotes interaction between the αvβ5 integrin and the insulin receptor leading to dampening of skeletal-muscle insulin receptor signaling. Blockade of the MFGE8/β5 pathway also enhances hepatic insulin sensitivity. Our work identifies an autoregulatory mechanism by which insulin-stimulated signaling through its cognate receptor is terminated through up-regulation of MFGE8 and its consequent interaction with the αvβ5 integrin, thereby establishing a pathway that can potentially be targeted to improve insulin sensitivity.

Learnability has been a topic of great interest in phonology. In particular is the question of the relative learnability of process interactions. In both historical and experimental domains, researchers have noted that certain kinds of interactions are harder to learn than others. In both domains, however, the results are seemingly in conflict. One potential source of the conflicting outcome is the types of processes involved. In this paper, we investigate the effect of process types on the learnability of different interaction types, using an ideal minimum-description-length learner (MDL). We find that the model indeed predicts different learnability outcomes for each interaction type; however, the asymmetry is largely independent of the process type. This computational model explains certain elements of both the historical as well as some experimental findings of the relative learnability of linguistics process interactions, while contradicting other behavioral findings.

In pathological fibrosis, aberrant tissue remodeling with excess extracellular matrix leads to organ dysfunction and eventual morbidity. Diseases of fibrosis create significant global health and economic burdens and are often deadly. Although fibrosis has traditionally been thought of as an irreversible process, a growing body of evidence demonstrates that organ fibrosis can reverse in certain circumstances, especially if an underlying cause of injury can be removed. This body of evidence has uncovered more and more contributors to persistent and nonresolving tissue fibrosis. Here, we review the present knowledge on resolution of organ fibrosis and restoration of near-normal tissue architecture. We emphasize three critical areas of tissue homeostasis that are necessary for fibrosis resolution, namely, the elimination of matrix-producing cells, the clearance of excess matrix, and the regeneration of normal tissue constituents. In so doing, we also highlight how profibrotic pathways interact with one another and where there may be therapeutic opportunities to intervene and remediate pathological persistent fibrosis.

Adipose tissues dynamically remodel their cellular composition in response to external cues by stimulating beige adipocyte biogenesis; however, the developmental origin and pathways regulating this process remain insufficiently understood owing to adipose tissue heterogeneity. Here, we employed single-cell RNA-seq and identified a unique subset of adipocyte progenitor cells (APCs) that possessed the cell-intrinsic plasticity to give rise to beige fat. This beige APC population is proliferative and marked by cell-surface proteins, including PDGFRα, Sca1, and CD81. Notably, CD81 is not only a beige APC marker but also required for de novo beige fat biogenesis following cold exposure. CD81 forms a complex with αV/β1 and αV/β5 integrins and mediates the activation of integrin-FAK signaling in response to irisin. Importantly, CD81 loss causes diet-induced obesity, insulin resistance, and adipose tissue inflammation. These results suggest that CD81 functions as a key sensor of external inputs and controls beige APC proliferation and whole-body energy homeostasis.

Although aging represents the most important epidemiologic risk factor for fibrotic disease, the reasons for this are incompletely understood. Excess collagen deposition in tissues is the sine qua non of tissue fibrosis and can be viewed as an imbalance between collagen production and collagen degradation. Yet we still lack a detailed understanding of the changes that take place during development, maturation, and aging in extracellular matrix (ECM) dynamics. Resolution of fibrosis is impaired in aging, and this impairment may explain why age is the most important risk factor for fibrotic diseases, such as idiopathic pulmonary fibrosis. However, ECM dynamics and impaired resolution of fibrosis in aging remain understudied. Here we show that cell-mediated collagen uptake and degradation are diminished in aged animals and this finding correlates with downregulation of the collagen endocytic receptor mannose receptor, C-type 2 (Mrc2). We identify myeloid zinc finger-1 as a potentially novel transcriptional regulator of Mrc2, and both this transcription factor and Mrc2 are downregulated in multiple tissues and organisms in an age-dependent manner. Thus, cell-mediated degradation of collagen is an essential process that promotes resolution of fibrosis, and impairment in this process contributes to age-related fibrosis.

Research has revealed the positive effects of flipped classroom approaches on students' learning engagement and performance compared with conventional lecture-based classrooms. However, because of a lack of out-of-class learning support, many students fail to comprehensively prepare the provided lecture materials before class. One promising solution to this problem is recommendation systems in the educational area, which have been instrumental in helping learners identify useful and relevant lecture materials that satisfy their learning needs. Thus, in this study, we propose a summarized lecture material recommendation system, which is integrated into an e-book reading system as an enhancement of the flipped classroom approach. This system helps students identify pages that contain essential knowledge that must be thoroughly studied before class. The proposed system was constructed on the basis of our previous work. In this study, a quasi-experiment was conducted in a graduate course that implemented the flipped classroom model: experimental group students learned with the proposed system, whereas the control group students had no access to the additional features. The findings of this study suggest that students who learn with the proposed recommendation system significantly outperform those who learn without the system in a flipped classroom in terms of their learning outcomes and engagement in preclass preparation.

Precision education is now recognized as a new challenge of applying artificial intelligence, machine learning, and learning analytics to improve both learning performance and teaching quality. To promote precision education, digital learning platforms have been widely used to collect educational records of students' behavior, performance, and other types of interaction. On the other hand, the increasing volume of students' learning behavioral data in virtual learning environments provides opportunities for mining data on these students' learning patterns. Accordingly, identifying students' online learning patterns on various digital learning platforms has drawn the interest of the learning analytics and educational data mining research communities. In this study, the authors applied data analytics methods to examine the learning patterns of students using an ebook system for one semester in an undergraduate course. The authors used a clustering approach to identify subgroups of students with different learning patterns. Several subgroups were identified, and the students' learning patterns in each subgroup were determined accordingly. In addition, the association between these students' learning patterns and their learning outcomes from the course was investigated. The findings of this study provide educators opportunities to predict students' learning outcomes by analyzing their online learning behaviors and providing timely intervention for improving their learning experience, which achieves one of the goals of learning analytics as part of precision education.

This methodological-theoretical synergy provides an integrative framework of learning analytics through the development of the human-and-machine symbiotic reinforcement learning. The framework intends to address the challenges of the current learning analytics model, including a lack of internal validity, generalizability, immediacy, transferability, and interpretability for precision education. The proposed framework consists of a master component (the brain) and its four subsuming components: social networking, the smart classroom, the intelligent agent, and the dashboard. The brain component takes in and analyzes multimodal streams of student data from the other components with the model-based reinforcement learning, which forms policies of adequate actions that maximize the long-term rewards for both the human and machine in the seamless learning environment. An example case plan in advanced statistics was demonstrated to illustrate the course description, data collected in each component, and how the components meet different features of the smart learning environment to deliver precision education. An empirical demonstration was provided using some selected mulitmodal data to inform the effectiveness of the proposed framework. The human-and-machine symbiotic reinforcement learning has theoretical and practical implications for the next-generation learning analytics models and research.

Transportation is a major emitter of greenhouse gas (GHG) emissions in the United States accounting for 27% of the country’s emissions, second only to the electricity sector. As a result, reducing GHG emissions are essential for mitigating some of the most damaging potential impacts associated with climate change and because of the importance and relative size of the transportation sector, it would need to contribute a significant amount of emissions reduction.This report describes the development and use of an U.S. energy system optimization model (US-TIMES) in order to analyze the reductions in GHG emissions that can come about through policy targets.  These policy targets induce technology investments and operation in order to satisfy the demand for energy services and environmental policy constraints (notably GHG emission targets).The model development focused on two key areas within the transportation sector, light-duty vehicles and heavy-duty vehicles.  In the light-duty space, we incorporated consumer choice elements into the energy system optimization framework through increasing consumer heterogeneity and adding non-monetary decision factors such as risk and fueling inconvenience.  For heavy-duty vehicles, we adopt a segmentation approach and update vehicle cost and performance assumptions from our recent work. The model is used to project scenarios for low carbon futures from a reference scenario all the way to an 80% GHG reduction target.View the NCST Project Webpage

The California Regional Multisector Air Quality Emissions (CA-REMARQUE) model is developed to predict changes to criteria pollutant emissions inventories in California in response to sophisticated emissions control programs implemented to achieve deep greenhouse gas (GHG) emissions reductions. Two scenarios for the year 2050 act as the starting point for calculations: a business-as-usual (BAU) scenario and an 80% GHG reduction (GHG-Step) scenario. Each of these scenarios was developed with an energy economic model to optimize costs across the entire California economy and so they include changes in activity, fuels, and technology across economic sectors. Separate algorithms are developed to estimate emissions of criteria pollutants (or their precursors) that are consistent with the future GHG scenarios for the following economic sectors: (i) on-road, (ii) rail and off-road, (iii) marine and aviation, (iv) residential and commercial, (v) electricity generation, and (vi) biorefineries. Properly accounting for new technologies involving electrification, biofuels, and hydrogen plays a central role in these calculations. Critically, criteria pollutant emissions do not decrease uniformly across all sectors of the economy. Emissions of certain criteria pollutants (or their precursors) increase in some sectors as part of the overall optimization within each of the scenarios. This produces nonuniform changes to criteria pollutant emissions in close proximity to heavily populated regions when viewed at 4km spatial resolution with implications for exposure to air pollution for those populations. As a further complication, changing fuels and technology also modify the composition of reactive organic gas emissions and the size and composition of particulate matter emissions. This is most notably apparent through a comparison of emissions reductions for different size fractions of primary particulate matter. Primary PM2.5 emissions decrease by 4% in the GHG-Step scenario vs. the BAU scenario while corresponding primary PM0.1 emissions decrease by 36%. Ultrafine particles (PM0.1) are an emerging pollutant of concern expected to impact public health in future scenarios. The complexity of this situation illustrates the need for realistic treatment of criteria pollutant emissions inventories linked to GHG emissions policies designed for fully developed countries and states with strict existing environmental regulations.

California's goal to reduce greenhouse gas (GHG) emissions to a level that is 80 % below 1990 levels by the year 2050 will require adoption of low-carbon energy sources across all economic sectors. In addition to reducing GHG emissions, shifting to fuels with lower carbon intensity will change concentrations of short-lived conventional air pollutants, including airborne particles with a diameter of less than 2.5 µm (PM2.5) and ozone (O3). Here we evaluate how business-as-usual (BAU) air pollution and public health in California will be transformed in the year 2050 through the adoption of low-carbon technologies, expanded electrification, and modified activity patterns within a low-carbon energy scenario (GHG-Step). Both the BAU and GHG-Step statewide emission scenarios were constructed using the energy–economic optimization model, CA-TIMES, that calculates the multi-sector energy portfolio that meets projected energy supply and demand at the lowest cost, while also satisfying scenario-specific GHG emissions constraints. Corresponding criteria pollutant emissions for each scenario were then spatially allocated at 4 km resolution to support air quality analysis in different regions of the state. Meteorological inputs for the year 2054 were generated under a Representative Concentration Pathway (RCP) 8.5 future climate. Annual-average PM2.5 and O3 concentrations were predicted using the modified emissions and meteorology inputs with a regional chemical transport model. In the final phase of the analysis, mortality (total deaths) and mortality rate (deaths per 100 000) were calculated using established exposure-response relationships from air pollution epidemiology combined with simulated annual-average PM2.5 and O3 exposure. Net emissions reductions across all sectors are −36 % for PM0.1 mass, −3.6 % for PM2.5 mass, −10.6 % for PM2.5 elemental carbon, −13.3 % for PM2.5 organic carbon, −13.7 % for NO x, and −27.5 % for NH3. Predicted deaths associated with air pollution in 2050 dropped by 24–26 % in California (1537–2758 avoided deaths yr−1) in the climate-friendly 2050 GHG-Step scenario, which is equivalent to a 54–56 % reduction in the air pollution mortality rate (deaths per 100 000) relative to 2010 levels. These avoided deaths have an estimated value of USD 11.4–20.4 billion yr−1 based on the present-day value of a statistical life (VSL) equal to USD 7.6 million. The costs for reducing California GHG emissions 80 % below 1990 levels by the year 2050 depend strongly on numerous external factors such as the global price of oil. Best estimates suggest that meeting an intermediate target (40 % reduction in GHG emissions by the year 2030) using a non-optimized scenario would reduce personal income by USD 4.95 billion yr−1 (−0.15 %) and lower overall state gross domestic product by USD 16.1 billion yr−1 (−0.45 %). The public health benefits described here are comparable to these cost estimates, making a compelling argument for the adoption of low-carbon energy in California, with implications for other regions in the United States and across the world.