Massively parallel in vivo Perturb-seq reveals cell-type-specific transcriptional networks in cortical development.

Leveraging AAVs' versatile tropism and labeling capacity, we expanded the scale of in vivo CRISPR screening with single-cell transcriptomic phenotyping across embryonic to adult brains and peripheral nervous systems. Through extensive tests of 86 vectors across AAV serotypes combined with a transposon system, we substantially amplified labeling efficacy and accelerated in vivo gene delivery from weeks to days. Our proof-of-principle in utero screen identified the pleiotropic effects of Foxg1, highlighting its tight regulation of distinct networks essential for cell fate specification of Layer 6 corticothalamic neurons. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% by lentivirus, to achieve analysis of over 30,000 cells in one experiment and enable massively parallel in vivo Perturb-seq. Compatible with various phenotypic measurements (single-cell or spatial multi-omics), it presents a flexible approach to interrogate gene function across cell types in vivo, translating gene variants to their causal function.

Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex.

Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development.

Pyramidal neuron subtype diversity governs microglia states in the neocortex.

Microglia are specialized macrophages in the brain parenchyma that exist in multiple transcriptional states and reside within a wide range of neuronal environments1-4. However, how and where these states are generated remains poorly understood. Here, using the mouse somatosensory cortex, we demonstrate that microglia density and molecular state acquisition are determined by the local composition of pyramidal neuron classes. Using single-cell and spatial transcriptomic profiling, we unveil the molecular signatures and spatial distributions of diverse microglia populations and show that certain states are enriched in specific cortical layers, whereas others are broadly distributed throughout the cortex. Notably, conversion of deep-layer pyramidal neurons to an alternate class identity reconfigures the distribution of local, layer-enriched homeostatic microglia to match the new neuronal niche. Leveraging the transcriptional diversity of pyramidal neurons in the neocortex, we construct a ligand-receptor atlas describing interactions between individual pyramidal neuron subtypes and microglia states, revealing rules of neuron-microglia communication. Our findings uncover a fundamental role for neuronal diversity in instructing the acquisition of microglia states as a potential mechanism for fine-tuning neuroimmune interactions within the cortical local circuitry.

Autism genes converge on asynchronous development of shared neuron classes.

Genetic risk for autism spectrum disorder (ASD) is associated with hundreds of genes spanning a wide range of biological functions1-6. The alterations in the human brain resulting from mutations in these genes remain unclear. Furthermore, their phenotypic manifestation varies across individuals7,8. Here we used organoid models of the human cerebral cortex to identify cell-type-specific developmental abnormalities that result from haploinsufficiency in three ASD risk genes-SUV420H1 (also known as KMT5B), ARID1B and CHD8-in multiple cell lines from different donors, using single-cell RNA-sequencing (scRNA-seq) analysis of more than 745,000 cells and proteomic analysis of individual organoids, to identify phenotypic convergence. Each of the three mutations confers asynchronous development of two main cortical neuronal lineages-γ-aminobutyric-acid-releasing (GABAergic) neurons and deep-layer excitatory projection neurons-but acts through largely distinct molecular pathways. Although these phenotypes are consistent across cell lines, their expressivity is influenced by the individual genomic context, in a manner that is dependent on both the risk gene and the developmental defect. Calcium imaging in intact organoids shows that these early-stage developmental changes are followed by abnormal circuit activity. This research uncovers cell-type-specific neurodevelopmental abnormalities that are shared across ASD risk genes and are finely modulated by human genomic context, finding convergence in the neurobiological basis of how different risk genes contribute to ASD pathology.

Fetal brain response to maternal inflammation requires microglia.

In utero infection and maternal inflammation can adversely impact fetal brain development. Maternal systemic illness, even in the absence of direct fetal brain infection, is associated with an increased risk of neuropsychiatric disorders in affected offspring. The cell types mediating the fetal brain response to maternal inflammation are largely unknown, hindering the development of novel treatment strategies. Here, we show that microglia, the resident phagocytes of the brain, highly express receptors for relevant pathogens and cytokines throughout embryonic development. Using a rodent maternal immune activation (MIA) model in which polyinosinic:polycytidylic acid is injected into pregnant mice, we demonstrate long-lasting transcriptional changes in fetal microglia that persist into postnatal life. We find that MIA induces widespread gene expression changes in neuronal and non-neuronal cells; importantly, these responses are abolished by selective genetic deletion of microglia, indicating that microglia are required for the transcriptional response of other cortical cell types to MIA. These findings demonstrate that microglia play a crucial durable role in the fetal response to maternal inflammation, and should be explored as potential therapeutic cell targets.

Cell-type specific defects in PTEN-mutant cortical organoids converge on abnormal circuit activity.

De novo heterozygous loss-of-function mutations in phosphatase and tensin homolog (PTEN) are strongly associated with autism spectrum disorders; however, it is unclear how heterozygous mutations in this gene affect different cell types during human brain development and how these effects vary across individuals. Here, we used human cortical organoids from different donors to identify cell-type specific developmental events that are affected by heterozygous mutations in PTEN. We profiled individual organoids by single-cell RNA-seq, proteomics and spatial transcriptomics and revealed abnormalities in developmental timing in human outer radial glia progenitors and deep-layer cortical projection neurons, which varied with the donor genetic background. Calcium imaging in intact organoids showed that both accelerated and delayed neuronal development phenotypes resulted in similar abnormal activity of local circuits, irrespective of genetic background. The work reveals donor-dependent, cell-type specific developmental phenotypes of PTEN heterozygosity that later converge on disrupted neuronal activity.

Scanned information exposure and support for tobacco regulations among US youth and young adult tobacco product users and non-users.

The influences of information exposure on youth and young adults' (YYA) support for smoking/vaping regulations have been understudied. This study examines (i) the relationships between routine exposure to (i.e. scanning) anti-smoking/pro-vaping information and YYA support for anti-smoking/vaping regulations and (ii) whether these relationships differ across YYA users and non-users of tobacco products. We analyzed the data from a nationally representative two-wave rolling cross-sectional survey of YYA in the United States, collected from 2014 to 2017 (baseline n = 10 642; follow-up n = 4001). Less than 5% of the participants ever scanned pro-smoking and anti-vaping information. Scanning anti-smoking information had significant positive relationships with support for all anti-smoking policies cross-sectionally, and this pattern was longitudinally significant in two anti-smoking policy contexts. Scanning pro-vaping information had significant negative associations with support for anti-vaping policies cross-sectionally, but not longitudinally. The lagged positive relationships between scanning anti-smoking information and support for anti-smoking regulations were stronger among YYA smokers than among YYA non-smokers, whereas evidence from adult data suggested the opposite pattern. The findings suggest that scanning information can affect YYA support for tobacco regulations. Future efforts are required to investigate mechanisms underlying the influences of scanned information on YYA support for tobacco regulations.

A comprehensive examination of association between belief in vaccine misinformation and vaccination intention in the COVID-19 context.

Misinformation about COVID-19 vaccines is widely available in the public communication environment. Exposure to the misinformation may increase perceived risk of and evoke negative emotions toward COVID-19 vaccines that may eventually reduce COVID-19 vaccination intentions. The negative influences of misinformation may vary by aspects of individuals' social networks. Expanding the reasoned action approach, we proposed a comprehensive model to examine the roles of misinformation beliefs, perceived risk, fear, worry, and social networks in explaining COVID-19 vaccination intentions. We tested the model using survey data of South Korean adults, collected when the Korean government launched its nationwide vaccination program in April 2021 (n = 744). The results from our step-by-step path analyses indicated that COVID-19 vaccination intentions had positive direct associations with vaccination-specific factors such as attitudes toward, injunctive norms on, and perceived behavioral control over COVID-19 vaccination. Perceived risk was also directly linked to intentions. Among these factors, attitudes and injunctive norms were most strongly related to intentions. Misinformation beliefs and worry had negative indirect relationships with intentions via the mediation of these variables directly connected to intentions. The negative influences of misinformation beliefs were greater among respondents reported stronger tie strengths. Theoretical and practical implications were discussed.

Post-CMOS Compatible Aluminum Scandium Nitride/2D Channel Ferroelectric Field-Effect-Transistor Memory.

Recent advances in oxide ferroelectric (FE) materials have rejuvenated the field of low-power, nonvolatile memories and made FE memories a commercial reality. Despite these advances, progress on commercial FE-RAM based on lead zirconium titanate has stalled due to process challenges. The recent discovery of ferroelectricity in scandium-doped aluminum nitride (AlScN) presents new opportunities for direct memory integration with logic transistors due to the low temperature of AlScN deposition (approximately 350 °C), making it compatible with back end of the line integration on silicon logic. Here, we present a FE-FET device composed of an FE-AlScN dielectric layer integrated with a two-dimensional MoS2 channel. Our devices show an ON/OFF ratio of ∼106, concurrent with a normalized memory window of 0.3 V/nm. The devices also demonstrate stable memory states up to 104 cycles and state retention up to 105 s. Our results suggest that the FE-AlScN/2D combination is ideal for embedded memory and memory-based computing architectures.

Quantitative Analysis of Attachment Time of Air Bubbles to Solid Surfaces in Water.

The attachment of air bubbles to solid surfaces in water is encountered in many natural processes and industrial applications. It has been established that the attachment can occur between hydrophobic surfaces and air bubbles. In this paper, we present novel experimental results to quantify the attachment in terms of the attachment time. We show that the attachment time can be determined from either the transient force curve or the transient film thickness. These techniques for determining the attachment time are based on the fact that the rupture of a thin liquid film produces a large attachment force and a rapid expansion of the three-phase contact radius in comparison with the expansion of the film radius. The experimental results are quantitatively analyzed using thin-film drainage theory and intermolecular forces, which include the advanced multilayer van der Waals force and the electrical double-layer force. The advanced van der Waals force theory allows us to incorporate the effect of interfacial gas enrichment (IGE) of dissolved gas in water at hydrophobic surfaces on the bubble-surface attachment. Critically, if the presence of IGE is ignored, the experimental results do not agree with the theory. Finally, IGE is shown to be a significant factor in controlling hydrophobic attraction between an air bubble and a hydrophobic surface and their attachment.

Communication about Suicide, Perceived Social Support, and Suicidal Intention in Korean Adults.

The present study investigates indirect effects of communication about suicide in interpersonal relationships and patient-provider relationships on suicidal intention via attitude, descriptive norm, injunctive norm, and self-efficacy in committing suicide among Korean adults, using longitudinal survey data. Moderation effects of perceived social support are also tested. As a result of path analysis (N = 984), communication about suicide with family, friends, and coworkers is significantly related to pro-suicide descriptive norm and pro-suicide injunctive norm. Positive attitude toward suicide, pro-suicide injunctive norm, and high self-efficacy in committing suicide are significantly related to suicidal intention. Interpersonal communication is positively related to pro-suicide injunctive norm, which in turn links to suicidal intention. Communication about suicide with healthcare professionals, however, did not show significant direct and indirect effects. Moderation effects of perceived social support are detected between interpersonal communication and pro-suicide attitude and pro-suicide injunctive norm, as well as between patient-provider communication and pro-suicide attitude.

Association of COVID-19 Misinformation with Face Mask Wearing and Social Distancing in a Nationally Representative US Sample.

Wide-spread misinformation about the COVID-19 pandemic has presented challenges for communicating public health recommendations. Should campaigns to promote protective behaviors focus on debunking misinformation or targeting behavior-specific beliefs? To address this question, we examine whether belief in COVID-19 misinformation is directly associated with two behaviors (face mask wearing and social distancing), and whether behavior-specific beliefs can account for this association and better predict behavior, consistent with behavior-change theory. We conducted a nationally representative two-wave survey of U.S. adults from 5/26/20-6/12/20 (n = 1074) and 7/15/20-7/21//20 (n = 889; follow-up response 83%). Scales were developed and validated for COVID-19 related misinformation beliefs, social distancing and face mask wearing, and beliefs about the consequences of both behaviors. Cross-lagged panel linear regression models assessed relationships among the variables. While belief in misinformation was negatively associated with both face mask wearing (B = -.27, SE =.06) and social-distancing behaviors (B = -.46, SE =.08) measured at the same time, misinformation did not predict concurrent or lagged behavior when the behavior-specific beliefs were incorporated in the models. Beliefs about behavioral outcomes accounted for face mask wearing and social distancing, both cross-sectionally (B =.43, SE =.05; B =.63, SE =.09) and lagged over time (B =.20, SE = 04; B =.30, SE =.08). In conclusion, belief in COVID-19-related misinformation is less relevant to protective behaviors, but beliefs about the consequences of these behaviors are important predictors. With regard to misinformation, we recommend health campaigns aimed at promoting protective behaviors emphasize the benefits of these behaviors, rather than debunking unrelated false claims.

Valence of Media Coverage About Electronic Cigarettes and Other Tobacco Products From 2014 to 2017: Evidence From Automated Content Analysis.

INTRODUCTION: As media exposure can influence people's opinions and perceptions about vaping and smoking, analyzing the valence of media content about tobacco products (ie, overall attitude toward tobacco, cigars, electronic cigarettes, etc.) is an important issue. This study advances the field by analyzing a large amount of media content about multiple tobacco products across six different media sources. AIMS AND METHODS: From May 2014 to December 2017, we collected all English-language media items about tobacco products that U.S. young people might see from mass media and websites (long-form) and social media (Twitter and YouTube). We used supervised machine learning to develop validated algorithms to label the valence of these media items. Using the labeled results, we examined the impact of product type (e-cigarettes vs. other tobacco products), source (long-form vs. social media), and time (by month) on the valence of coverage. RESULTS: We obtained 152 886 long-form media texts (20% with more than a passing mention), nearly 86 million tweets, and 12 262 YouTube videos about tobacco products. Most long-form media content opposed, while most social media coverage supported, the use of e-cigarettes and other tobacco products. Over time, within-source valence proportions were stable, though in aggregate, the amount of media coverage against the use of tobacco products decreased. CONCLUSIONS: This study describes the U.S. public communication environment about vaping and smoking for young people and offers a novel big data approach to analyzing media content. Results suggest that content has gradually become less negative toward the use of e-cigarettes and other tobacco products. IMPLICATIONS: This study is the first to examine how the valence of media coverage differs for e-cigarettes versus other tobacco products, across several media sources, and over time using a large corpus of media items. Unlike prior studies, these data allow us to draw conclusions about relative support and opposition for these two categories of products in a variety of media coverage because the same coding scheme was used across products and media sources.

Exploring the Effect of Health App Use on Fruit and Vegetable Consumption.

People may use multiple health apps at the same time or in close sequence, and the effect of health apps may vary across different socioeconomic groups. However, in the context of healthy eating, studies have focused on the effect of using a single health app. This study aims to test the relationship of using multiple health apps with fruit and vegetable consumption, assessing moderation by education level. Employing longitudinal survey data from South Korean adults, we conducted regression analyses to test the lagged association between use of health apps and intake of fruits and vegetables, and whether education moderates the relationship. The results supported the positive lagged relationship of health app use with fruit and vegetable consumption, significant after controlling for confounders and baseline fruit and vegetable consumption. The association was smaller among respondents with higher education. Our findings suggest the positive effect of using multiple health apps on fruit and vegetable consumption, implying the importance of finding the best combination of health apps to maximize their effectiveness in promoting healthy eating. Also, the negative interaction of health app use and education support using mobile communication technology to improve the public health of low socioeconomic status individuals.

Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data.

Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.

Measurement of the Attachment Force between an Air Bubble and a Mineral Surface: Relationship between the Attachment Force and Flotation Kinetics.

The interaction forces between air bubbles and mineral surfaces were directly measured during the attachment process using an apparatus developed in our laboratory, and they are defined as the attachment forces. The attachment forces were measured between the air bubble and mineral surfaces modified with surfactants to have different hydrophobicities. Chalcopyrite and galena were used as the mineral surfaces, and their hydrophobicity was controlled by adsorbing xanthates with different hydrocarbon chain lengths. The hydrophobicity is represented by the static contact angle of water on the mineral surface. When the static contact angle was less than 90°, the attachment force increased considerably with increasing static contact angle of the surfaces, irrespective of the mineral type or the hydrocarbon chain length of the adsorbed xanthate. The hydrophobicity of the mineral surface is found to be the dominant factor determining the attachment force. The measured attachment forces agree well with those calculated based on the force balance model derived from the capillary force and Laplace pressure equation. Microflotation experiments to examine the relationship between the attachment force and flotation kinetics were carried out under the same conditions to control surface hydrophobicity. The variation in the flotation kinetic constants and attachment forces with the water contact angle are very similar. As a result, the attachment forces measured by the developed apparatus can provide quantitative information on the interaction between an air bubble and the mineral surface and can be used for predicting the flotation kinetics.

A Moderated Mediation Model of the Relationship between Media, Social Capital, and Cancer Knowledge.

We combined insights from various theories and models of media learning, and advanced an indirect model accounting for the mechanisms underlying the media influences on knowledge acquisition. Our model was largely supported by the data from a two-wave longitudinal panel survey with a nationwide sample of Korean adults. It was found that both personal cancer history and cancer worry were positively associated with exposure to stomach cancer information from the media. In turn, exposure to media information was positively related to reflective integration of that information, which ultimately leads to stomach cancer knowledge only among people with high levels of social capital. These findings suggest that media uses and effects are not only an individual but also a contextually dependent experience.

An Information Retrieval Approach for Robust Prediction of Road Surface States.

Recently, due to the increasing importance of reducing severe vehicle accidents on roads (especially on highways), the automatic identification of road surface conditions, and the provisioning of such information to drivers in advance, have recently been gaining significant momentum as a proactive solution to decrease the number of vehicle accidents. In this paper, we firstly propose an information retrieval approach that aims to identify road surface states by combining conventional machine-learning techniques and moving average methods. Specifically, when signal information is received from a radar system, our approach attempts to estimate the current state of the road surface based on the similar instances observed previously based on utilizing a given similarity function. Next, the estimated state is then calibrated by using the recently estimated states to yield both effective and robust prediction results. To validate the performances of the proposed approach, we established a real-world experimental setting on a section of actual highway in South Korea and conducted a comparison with the conventional approaches in terms of accuracy. The experimental results show that the proposed approach successfully outperforms the previously developed methods.

A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices.

The safety of children has always been an important issue, and several studies have been conducted to determine the stress state of a child to ensure the safety. Audio signals and biological signals including heart rate are known to be effective for stress state detection. However, collecting those data requires specialized equipment, which is not appropriate for the constant monitoring of children, and advanced data analysis is required for accurate detection. In this regard, we propose a stress state detection framework which utilizes both audio signal and heart rate collected from wearable devices, and adopted machine learning methods for the detection. Experiments using real-world data were conducted to compare detection performances across various machine learning methods and noise levels of audio signal. Adopting the proposed framework in the real-world will contribute to the enhancement of child safety.

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs.

Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user's next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user's past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user's next places than the previous approaches considered in most cases.