What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation Model

Minority groups have been using social media to organize social movements that create profound social impacts. Black Lives Matter (BLM) and Stop Asian Hate (SAH) are two successful social movements that have spread on Twitter that promote protests and activities against racism and increase the public's awareness of other social challenges that minority groups face. However, previous studies have mostly conducted qualitative analyses of tweets or interviews with users, which may not comprehensively and validly represent all tweets. Very few studies have explored the Twitter topics within BLM and SAH dialogs in a rigorous, quantified and data-centered approach. Therefore, in this research, we adopted a mixed-methods approach to comprehensively analyze BLM and SAH Twitter topics. We implemented (1) the latent Dirichlet allocation model to understand the top high-level words and topics and (2) open-coding analysis to identify specific themes across the tweets. We collected more than one million tweets with the #blacklivesmatter and #stopasianhate hashtags and compared their topics. Our findings revealed that the tweets discussed a variety of influential topics in depth, and social justice, social movements, and emotional sentiments were common topics in both movements, though with unique subtopics for each movement. Our study contributes to the topic analysis of social movements on social media platforms in particular and the literature on the interplay of AI, ethics, and society in general.Comment: Accepted at AAAI and ACM Conference on AI, Ethics, and Society, August 1 to 3, 2022, Oxford, United Kingdo

Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources

Objectives: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM), and compares the developed model's outcomes with each other, as well as that from the standard R package (`lme4'). Methods: The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation), which supports federated decomposition of GLMM to bring computation to data. Results: Our developed method can handle GLMM to accommodate hierarchical data with multiple non-independent levels of observations in a federated setting. The experiment results demonstrate comparable (Laplace) and superior (Gaussian-Hermite) performances with simulated and real-world data. Conclusion: We developed and compared federated GLMMs with different approximations, which can support researchers in analyzing biomedical data to accommodate mixed effects and address non-independence due to hierarchical structures (i.e., institutes, region, country, etc.).Comment: 19 pages, 5 figures, submitted to Journal of Biomedical Informatic

Predictors of lung adenocarcinoma with leptomeningeal metastases: A 2022 targeted-therapy-assisted molGPA model

Objective: To explore prognostic indicators of lung adenocarcinoma with leptomeningeal metastases (LM) and provide an updated graded prognostic assessment model integrated with molecular alterations (molGPA). Methods: A cohort of 162 patients was enrolled from 202 patients with lung adenocarcinoma and LM. By randomly splitting data into the training (80%) and validation (20%) sets, the Cox regression and random survival forest methods were used on the training set to identify statistically significant variables and construct a prognostic model. The C-index of the model was calculated and compared with that of previous molGPA models. Results: The Cox regression and random forest models both identified four variables, which included KPS, LANO neurological assessment, TKI therapy line, and controlled primary tumor, as statistically significant predictors. A novel targeted-therapy-assisted molGPA model (2022) using the above four prognostic factors was developed to predict LM of lung adenocarcinoma. The C-indices of this prognostic model in the training and validation sets were higher than those of the lung-molGPA (2017) and molGPA (2019) models. Conclusions: The 2022 molGPA model, a substantial update of previous molGPA models with better prediction performance, may be useful in clinical decision making and stratification of future clinical trials

Asymmetric 3D Elasticâ Plastic Strainâ Modulated Electron Energy Structure in Monolayer Graphene by Laser Shocking

Graphene has a great potential to replace silicon in prospective semiconductor industries due to its outstanding electronic and transport properties; nonetheless, its lack of energy bandgap is a substantial limitation for practical applications. To date, straining graphene to break its lattice symmetry is perhaps the most efficient approach toward realizing bandgap tunability in graphene. However, due to the weak lattice deformation induced by uniaxial or inâ plane shear strain, most strained graphene studies have yielded bandgaps <1 eV. In this work, a modulated inhomogeneous local asymmetric elasticâ plastic straining is reported that utilizes GPaâ level laser shocking at a high strain rate (dε/dt) â 106â 107 sâ 1, with excellent formability, inducing tunable bandgaps in graphene of up to 2.1 eV, as determined by scanning tunneling spectroscopy. Highâ resolution imaging and Raman spectroscopy reveal strainâ induced modifications to the atomic and electronic structure in graphene and firstâ principles simulations predict the measured bandgap openings. Laser shock modulation of semimetallic graphene to a semiconducting material with controllable bandgap has the potential to benefit the electronic and optoelectronic industries.Both the bandgap structure and the Fermi level of monolayer graphene are modulated using an easy and effective optomechanical method. Laserâ shockâ induced 3D nanoshaping enables an asymmetric elasticâ plastic straining of graphene, resulting in a wide graphene bandgap of over 2.1 eV and a wide Fermi level adjustment range of 0.6 eV.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149335/1/adma201900597.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149335/2/adma201900597-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149335/3/adma201900597_am.pd

Phenolic compounds weaken the impact of drought on soil enzyme activity in global wetlands

Soil enzymes play a central role in carbon and nutrient cycling, and their activities can be affected by drought-induced oxygen exposure. However, a systematic global estimate of enzyme sensitivity to drought in wetlands is still lacking. Through a meta-analysis of 55 studies comprising 761 paired observations, this study found that phosphorus-related enzyme activity increased by 38% as result of drought in wetlands, while the majority of other soil enzyme activities remained stable. The expansion of vascular plants under long-term drought significantly promoted the accumulation of phenolic compounds. Using a 2-week incubation experiment with phenol supplementation, we found that phosphorus-related enzyme could tolerate higher biotoxicity of phenolic compounds than other enzymes. Moreover, a long-term (35 years) drainage experiment in a northern peatland in China confirmed that the increased phenolic concentration in surface layer resulting from a shift in vegetation composition inhibited the increase in enzyme activities caused by rising oxygen availability, except for phosphorus-related enzyme. Overall, these results demonstrate the complex and resilient nature of wetland ecosystems, with soil enzymes showing a high degree of adaptation to drought conditions. These new insights could help evaluate the impact of drought on future wetland ecosystem services and provide a theoretical foundation for the remediation of degraded wetlands

Automated vulnerability discovery and exploitation in the internet of things

Recently, automated software vulnerability detection and exploitation in Internet of Things (IoT) has attracted more and more attention, due to IoT’s fast adoption and high social impact. However, the task is challenging and the solutions are non-trivial: the existing methods have limited effectiveness at discovering vulnerabilities capable of compromising IoT systems. To address this, we propose an Automated Vulnerability Discovery and Exploitation framework with a Scheduling strategy, AutoDES that aims to improve the efficiency and effectiveness of vulnerability discovery and exploitation. In the vulnerability discovery stage, we use our Anti-Driller technique to mitigate the “path explosion” problem. This approach first generates a specific input proceeding from symbolic execution based on a Control Flow Graph (CFG). It then leverages a mutation-based fuzzer to find vulnerabilities while avoiding invalid mutations. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits, via the use of several proposed attack techniques that can produce a shell based on the detected vulnerabilities. We also propose a genetic algorithm (GA)-based scheduling strategy (AutoS) that helps with assigning the computing resources dynamically and efficiently. The extensive experimental results on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset clearly demonstrate the effectiveness and efficiency of the proposed framework

Retinoic Acids Potentiate BMP9-Induced Osteogenic Differentiation of Mesenchymal Progenitor Cells

As one of the least studied bone morphogenetic proteins (BMPs), BMP9 is one of the most osteogenic BMPs. Retinoic acid (RA) signaling is known to play an important role in development, differentiation and bone metabolism. In this study, we investigate the effect of RA signaling on BMP9-induced osteogenic differentiation of mesenchymal progenitor cells (MPCs).Both primary MPCs and MPC line are used for BMP9 and RA stimulation. Recombinant adenoviruses are used to deliver BMP9, RARalpha and RXRalpha into MPCs. The in vitro osteogenic differentiation is monitored by determining the early and late osteogenic markers and matrix mineralization. Mouse perinatal limb explants and in vivo MPC implantation experiments are carried out to assess bone formation. We find that both 9CRA and ATRA effectively induce early osteogenic marker, such as alkaline phosphatase (ALP), and late osteogenic markers, such as osteopontin (OPN) and osteocalcin (OC). BMP9-induced osteogenic differentiation and mineralization is synergistically enhanced by 9CRA and ATRA in vitro. 9CRA and ATRA are shown to induce BMP9 expression and activate BMPR Smad-mediated transcription activity. Using mouse perinatal limb explants, we find that BMP9 and RAs act together to promote the expansion of hypertrophic chondrocyte zone at growth plate. Progenitor cell implantation studies reveal that co-expression of BMP9 and RXRalpha or RARalpha significantly increases trabecular bone and osteoid matrix formation.Our results strongly suggest that retinoid signaling may synergize with BMP9 activity in promoting osteogenic differentiation of MPCs. This knowledge should expand our understanding about how BMP9 cross-talks with other signaling pathways. Furthermore, a combination of BMP9 and retinoic acid (or its agonists) may be explored as effective bone regeneration therapeutics to treat large segmental bony defects, non-union fracture, and/or osteoporotic fracture