Leveraging Maker Learning in STEM to Promote Children's Interest in Cancer Research: A Pilot Program.

Children's early awareness about cancer, through exposure to cancer biology and prevention strategies and research principles, is a promising focus of education and learning. It may also benefit the pipeline of people entering into science, technology, engineering, and math (STEM) careers. We describe an educational pilot program for elementary school students, using developmentally appropriate activities focused on cancer at a museum dedicated to children's maker-centered learning and STEM. The program was implemented through a public school in Washington, DC serving students underrepresented in STEM. Program conceptualization, museum and school engagement, and maker learning pedagogy are described, as well as curricular outcomes. A total of N = 111 students (44% female, 75% Black/African American, 5% Latine) participated in a day-long field trip. Museum educators, assisted by cancer center researchers, led a multipart workshop on cancer and the environment and hands-on rotation of activities in microbiology, immunology, and ultraviolet radiation safety; students then completed self-report evaluations. Results indicate that nearly all (> 95%) students practiced activities typical of a STEM professional at the program, and > 70% correctly answered factual questions about topics studied. Importantly, 87-94% demonstrated clear STEM interest, a sense of belonging in the field, and practice implementing skills for success in STEM (e.g., perseverance, imagination, teamwork). This pilot demonstrated acceptability and feasibility in delivering a cancer-focused curriculum to underserved elementary students using maker learning while favorably impacting key objectives. Future scale-up of this program is warranted, with the potential to increase students' motivation to engage in STEM and cancer research.

Rice ILI atypical bHLH transcription factors antagonize OsbHLH157/OsbHLH158 during brassinosteroid signaling.

Brassinosteroids (BRs) are a group of steroid hormones that play crucial roles in plant growth and development. Atypical bHLH transcription factors that lack the basic region for DNA binding have been implicated in BR signaling. However, the underlying mechanisms of atypical bHLHs in regulation of rice (Oryza sativa) BR signaling are still largely unknown. Here, we describe a systematic characterization of INCREASED LEAF INCLINATION (ILI) subfamily atypical bHLH transcription factors in rice. A total of 8 members, ILI1 to ILI8, with substantial sequence similarity were retrieved. Knockout and overexpression analyses demonstrated that these ILIs play unequally redundant and indispensable roles in BR-mediated growth and development in rice, with a more prominent role for ILI4 and ILI5. The ili3/4/5/8 quadruple and ili1/3/4/7/8 quintuple mutants displayed tremendous BR-related defects with severe dwarfism, erect leaves, and sterility. Biochemical analysis showed that ILIs interact with OsbHLH157 and OsbHLH158, which are also atypical bHLHs and have no obvious transcriptional activity. Overexpression of OsbHLH157 and OsbHLH158 led to drastic BR-defective growth, whereas the osbhlh157 osbhlh158 double mutant developed a typical BR-enhanced phenotype, indicating that OsbHLH157 and OsbHLH158 play a major negative role in rice BR signaling. Further transcriptome analyses revealed opposite effects of ILIs and OsbHLH157/OsbHLH158 in regulation of downstream gene expression, supporting the antagonism of ILIs and OsbHLH157/OsbHLH158 in maintaining the balance of BR signaling. Our results provide insights into the mechanism of BR signaling and plant architecture formation in rice.

Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists.

Importance: Artificial intelligence (AI) can interpret abnormal signs in chest radiography (CXR) and generate captions, but a prospective study is needed to examine its practical value. Objective: To prospectively compare natural language processing (NLP)-generated CXR captions and the diagnostic findings of radiologists. Design, Setting, and Participants: A multicenter diagnostic study was conducted. The training data set included CXR images and reports retrospectively collected from February 1, 2014, to February 28, 2018. The retrospective test data set included consecutive images and reports from April 1 to July 31, 2019. The prospective test data set included consecutive images and reports from May 1 to September 30, 2021. Exposures: A bidirectional encoder representation from a transformers model was used to extract language entities and relationships from unstructured CXR reports to establish 23 labels of abnormal signs to train convolutional neural networks. The participants in the prospective test group were randomly assigned to 1 of 3 different caption generation models: a normal template, NLP-generated captions, and rule-based captions based on convolutional neural networks. For each case, a resident drafted the report based on the randomly assigned captions and an experienced radiologist finalized the report blinded to the original captions. A total of 21 residents and 19 radiologists were involved. Main Outcomes and Measures: Time to write reports based on different caption generation models. Results: The training data set consisted of 74 082 cases (39 254 [53.0%] women; mean [SD] age, 50.0 [17.1] years). In the retrospective (n = 8126; 4345 [53.5%] women; mean [SD] age, 47.9 [15.9] years) and prospective (n = 5091; 2416 [47.5%] women; mean [SD] age, 45.1 [15.6] years) test data sets, the mean (SD) area under the curve of abnormal signs was 0.87 (0.11) in the retrospective data set and 0.84 (0.09) in the prospective data set. The residents' mean (SD) reporting time using the NLP-generated model was 283 (37) seconds-significantly shorter than the normal template (347 [58] seconds; P < .001) and the rule-based model (296 [46] seconds; P < .001). The NLP-generated captions showed the highest similarity to the final reports with a mean (SD) bilingual evaluation understudy score of 0.69 (0.24)-significantly higher than the normal template (0.37 [0.09]; P < .001) and the rule-based model (0.57 [0.19]; P < .001). Conclusions and Relevance: In this diagnostic study of NLP-generated CXR captions, prior information provided by NLP was associated with greater efficiency in the reporting process, while maintaining good consistency with the findings of radiologists.

Wetting-Enabled Three-Dimensional Interfacial Polymerization (WET-DIP) for Bioinspired Anti-Dehydration Hydrogels.

Anti-dehydration hydrogels have attracted considerable attention due to their promising applications in stretchable sensors, flexible electronics, and soft robots. However, anti-dehydration hydrogels prepared by conventional strategies inevitably depend on additional chemicals or suffer from cumbersome preparation processes. Here, inspired by the succulent Fenestraria aurantiaca a one-step wetting-enabled three-dimensional interfacial polymerization (WET-DIP) strategy for constructing organogel-sealed anti-dehydration hydrogels is developed. By virtue of the preferential wetting on the hydrophobic-oleophilic substrate surfaces, the organogel precursor solution can spread on the three-dimensional (3D) surface and encapsulate the hydrogel precursor solution, forming anti-dehydration hydrogel with 3D shape after in situ interfacial polymerization. The WET-DIP strategy is simple and ingenious, and accessible to discretionary 3D-shaped anti-dehydration hydrogels with a controllable thickness of the organogel outer layer. Strain sensors based on this anti-dehydration hydrogel also exhibit long-term stability in signal monitoring. This WET-DIP strategy shows great potentialities for constructing hydrogel-based devices with long-term stability.

Interfacial Instability-Induced (3I) Adhesives through "Mediator" Solvent Diffusion for Robust Underoil Adhesion.

Underoil adhesives are intensively needed in case of oil spill caused by pipeline rupture, but remain a challenge owing to the obstruction of oil layer or their swelling in oil. Herein, a general solvent diffusion principle is demonstrated by introducing dual-soluble "mediator" solvents to develop a new type of interfacial instability-induced (3I) adhesives, achieving effective underoil adhesion on various substrates and blocking the oil leakage within seconds. Microscopic characterization reveals a fast and dynamic solvent exchange process that destroys the oil layer by liquid-liquid interfacial diffusion between the "mediator" solvent and oil, enabling 3I adhesives to contact the solid surfaces directly. The principle of interfacial instability-induced liquid replacement is quite different from typical immiscible liquid replacement and is not restricted by the surface tension of solvents, surface energy, and roughness of solid surfaces, successfully directing the construction of a series of effective 3I adhesives with commercially available feedstocks. This study provides a unique clue for the design of next-generation adhesives in complex environments.

From Nanoscopic to Macroscopic Materials by Stimuli-Responsive Nanoparticle Aggregation.

Stimuli-responsive nanoparticle (NP) aggregation plays an increasingly important role in regulating NP assembly into microscopic superstructures, macroscopic 2D, and 3D functional materials. Diverse external stimuli are widely used to adjust the aggregation of responsive NPs, such as light, temperature, pH, electric, and magnetic fields. Many unique structures based on responsive NPs are constructed including disordered aggregates, ordered superlattices, structural droplets, colloidosomes, and bulk solids. In this review, the strategies for NP aggregation by external stimuli, and their recent progress ranging from nanoscale aggregates, microscale superstructures to macroscale bulk materials along the length scales as well as their applications are summarized. The future opportunities and challenges for designing functional materials through NP aggregation at different length scales are also discussed.

Integrated Bioinformatic Analysis of the Correlation of HOXA10 Expression with Survival and Immune Cell Infiltration in Lower Grade Glioma.

Homeobox A10 (HOXA10) encodes a transcription factor that regulates developmental processes. Whether HOXA10 mRNA levels in lower grade glioma (LGG) correlate with survival and immune cell infiltration has not been evaluated. The differential expression of HOXA10 in different tumors and their corresponding normal tissues was evaluated by exploring public datasets. The correlations between HOXA10 and survival, tumor immune cell infiltration, diverse gene mutation characteristics, and tumor mutation burden in LGG were also investigated using several independent datasets. Pathway enrichment analysis was conducted to identify HOXA10-associated signaling pathways. We found that HOXA10 expression levels did not significantly differ between LGG tumors and normal tissues. Upon assessing the association between HOXA10 expression and immune cell infiltration in LGG, as expected, HOXA10 gene mRNA levels were positively associated with B-cell and dendritic cell infiltration levels in public online datasets. Different HOXA10 expression groups showed diverse gene mutation characteristics and TMB, and low HOXA10 expression was closely related to improved LGG patient survival. Pathway enrichment analysis of HOXA10-associated genes indicated that the cell cycle signaling pathway may participate in affecting the outcomes of LGG patients. Our findings showed that HOXA10 expression was associated with LGG prognosis and tumor immunity.

OsCPD1 and OsCPD2 are functional brassinosteroid biosynthesis genes in rice.

CONSTITUTIVE PHOTOMORPHOGENIC DWARF (CPD), member of the CYP90A family of cytochrome P450 (CYP450) monooxygenase, is an essential component of brassinosteroids (BRs) biosynthesis pathway. Compared with a single CPD/CYP90A1 in Arabidopsis thaliana, two highly homologous CPD genes, OsCPD1/CYP90A3 and OsCPD2/CYP90A4, are present in rice genome. There is still no genetic evidence so far about the requirement of OsCPD1 and OsCPD2 in rice BR biosynthesis. In this study, we reported the functional characterization of OsCPD genes using CRISPR/Cas9 gene editing technology. The overall growth and development of oscpd1 and oscpd2 single knock-out mutants was indistinguishable from the wild-type, whereas, the oscpd1 oscpd2 double mutant displayed multiple and obvious BR-related defects. Cytological analyses further indicated the defective cell elongation in oscpd1 oscpd2 double mutant. The oscpd double mutants had a lower endogenous BR level and could be restored by the application of the brassinolide (BL). Moreover, overexpression of OsCPD1 and OsCPD2 led to a typical BR enhanced phenotype, with enlarged leaf angle and increased grain size. Taken together, our results provide direct genetic evidence that OsCPD1 and OsCPD2 play essential and redundant roles in maintenance of plant architecture by modulating BR biosynthesis in rice.

Prediction of the Lotus Effect on Solid Surfaces by Machine Learning.

Superhydrophobic surfaces with the "lotus effect" have wide applications in daily life and industry, such as self-cleaning, anti-freezing, and anti-corrosion. However, it is difficult to reliably predict whether a designed superhydrophobic surface has the "lotus effect" by traditional theoretical models due to complex surface topographies. Here, a reliable machine learning (ML) model to accurately predict the "lotus effect" of solid surfaces by designing a set of descriptors about nano-scale roughness and micro-scale topographies in addition to the surface hydrophobic modification is demonstrated. Geometrical and mathematical descriptors combined with gray level cooccurrence matrices (GLCM) offer a feasible solution to the puzzle of accurate descriptions of complex topographies. Furthermore, the "black box" is opened by feature importance and Shapley-additive-explanations (SHAP) analysis to extract waterdrop adhesion trends on superhydrophobic surfaces. The accurate prediction on as-fabricated superhydrophobic surfaces strongly affirms the extensionality of the ML model. This approach can be easily generalized to screen solid surfaces with other properties.

High-Strength Plus Reversible Supramolecular Adhesives Achieved by Regulating Intermolecular PtII ⋠⋠⋠PtII Interactions.

Surface adhesion has a great contradiction in high strength and good reversibility given their mutually exclusive requirements of fixed crosslinked networks and dynamic chain motion. Herein, we demonstrate a supramolecular organoplatinum(II) adhesive system regulated by intermolecular PtII ⋅⋅⋅PtII interactions that can simultaneously achieve high-strength and excellent reversible adhesion to various substrates. Upon alternating temperature, the assembly of suitably substituted organoplatinum(II) molecules can switch between well-ordered and disordered states via tuning PtII ⋅⋅⋅PtII interactions, resulting in stable reversible adhesion even after 100 cycles with a robust strength of ≈1.25 MPa and a large on-off ratio of ≈25. Along with the switch of PtII ⋅⋅⋅PtII contacts, the surface adhesion of organoplatinum(II) adhesives can be monitored by their changes in electrical signals. This study will open up new inspirations for developing high-performance reversible adhesives.

GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture Model.

Recent studies on unsupervised object detection based on spatial attention have achieved promising results. Models, such as AIR and SPAIR, output "what" and "where" latent variables that represent the attributes and locations of objects in a scene, respectively. Most of the previous studies concentrate on the "where" localization performance. However, we claim that acquiring "what" object attributes is also essential for representation learning. This study presents a framework, GMAIR, for unsupervised object detection. It incorporates spatial attention and a Gaussian mixture in a unified deep generative model. GMAIR can locate objects in a scene and simultaneously cluster them without supervision. Furthermore, we analyze the "what" latent variables and clustering process. Finally, we evaluate our model on MultiMNIST and Fruit2D datasets. We show that GMAIR achieves competitive results on localization and clustering compared with state-of-the-art methods.

Thermo-Responsive Jamming of Nanoparticle Dense Suspensions towards Macroscopic Liquid-Solid Switchable Materials.

Nanoparticle aggregation for constructing functional materials has shown enormous advantages in various applications. Most efforts focused on ordered nanoparticle aggregation for specific functions but were often limited to irreversible aggregation processes due to the thermodynamic equilibrium. Herein, we report a reversible disordered aggregation of SiO2 -PNIPAAm nanoparticles (SPNPs) through thermo-responsive jamming, obtaining smart liquid-solid switchable materials. The smart materials can display a switch between liquid-like state and solid-like state responding to a temperature change. This unique macroscopic behavior originates from the reversible disordered aggregation modulated by temperature-dependent hydrophobic interactions among the SPNPs. Notably, the materials at the solid-like state show anti-impact properties and can withstand the impact of a steel sphere with a speed of 328 cm s-1 . We envision that this finding offers inspiration to design smart liquid-solid switchable materials for impact protection.

Association Between Ischemic Stroke and COVID-19 in China: A Population-Based Retrospective Study.

Background and Purpose: To investigate the effect of prior ischemic stroke on the outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19), and to describe the incidence, clinical features, and risk factors of acute ischemic stroke (AIS) following COVID-19. Methods: In this population-based retrospective study, we included all the hospitalized positive patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020. Clinical data were extracted from administrative datasets coordinated by the Wuhan Health Commission. The propensity score matching and multivariate logistic regression analyses were used to adjust the confounding factors. Results: There are 36,358 patients in the final cohort, in which 1,160 (3.2%) had a prior stroke. After adjusting for available baseline characteristics, patients with prior stroke had a higher proportion of severe and critical illness and mortality. We found for the first time that the premorbid modified Rankin Scale (MRS) grouping (odds ratio [OR] = 1.796 [95% CI 1.334-2.435], p < 0.001) and older age (OR = 1.905 [95% CI 1.211-3.046], p = 0.006) imparted increased risk of death. AIS following COVID-19 occurred in 124 (0.34%) cases, and patients with prior stroke had a much higher incidence of AIS (3.4%). Logistic regression analyses confirmed an association between the severity of COVID-19 with the incidence of AIS. COVID-19 patients with AIS had a significantly higher mortality compared with COVID-19 patients without stroke and AIS patients without COVID-19. Conclusions: Coronavirus disease 2019 patients with prior stroke, especially those with the higher premorbid MRS or aged, have worse clinical outcomes. Furthermore, COVID-19 increases the incidence of AIS, and the incidence is positively associated with the severity of COVID-19.

Development and multicenter validation of chest X-ray radiography interpretations based on natural language processing.

Background: Artificial intelligence can assist in interpreting chest X-ray radiography (CXR) data, but large datasets require efficient image annotation. The purpose of this study is to extract CXR labels from diagnostic reports based on natural language processing, train convolutional neural networks (CNNs), and evaluate the classification performance of CNN using CXR data from multiple centers. Methods: We collected the CXR images and corresponding radiology reports of 74,082 subjects as the training dataset. The linguistic entities and relationships from unstructured radiology reports were extracted by the bidirectional encoder representations from transformers (BERT) model, and a knowledge graph was constructed to represent the association between image labels of abnormal signs and the report text of CXR. Then, a 25-label classification system were built to train and test the CNN models with weakly supervised labeling. Results: In three external test cohorts of 5,996 symptomatic patients, 2,130 screening examinees, and 1,804 community clinic patients, the mean AUC of identifying 25 abnormal signs by CNN reaches 0.866 ± 0.110, 0.891 ± 0.147, and 0.796 ± 0.157, respectively. In symptomatic patients, CNN shows no significant difference with local radiologists in identifying 21 signs (p > 0.05), but is poorer for 4 signs (p < 0.05). In screening examinees, CNN shows no significant difference for 17 signs (p > 0.05), but is poorer at classifying nodules (p = 0.013). In community clinic patients, CNN shows no significant difference for 12 signs (p > 0.05), but performs better for 6 signs (p < 0.001). Conclusion: We construct and validate an effective CXR interpretation system based on natural language processing.

Age-Related Risk Factors and Complications of Patients With COVID-19: A Population-Based Retrospective Study.

Objective: To study the differences in clinical characteristics, risk factors, and complications across age-groups among the inpatients with the coronavirus disease 2019 (COVID-19). Methods: In this population-based retrospective study, we included all the positive hospitalized patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020, during the first pandemic wave. Multivariate logistic regression analyses were used to explore the risk factors for death from COVID-19. Canonical correlation analysis (CCA) was performed to study the associations between comorbidities and complications. Results: There are 36,358 patients in the final cohort, of whom 2,492 (6.85%) died. Greater age (odds ration [OR] = 1.061 [95% CI 1.057-1.065], p < 0.001), male gender (OR = 1.726 [95% CI 1.582-1.885], p < 0.001), alcohol consumption (OR = 1.558 [95% CI 1.355-1.786], p < 0.001), smoking (OR = 1.326 [95% CI 1.055-1.652], p = 0.014), hypertension (OR = 1.175 [95% CI 1.067-1.293], p = 0.001), diabetes (OR = 1.258 [95% CI 1.118-1.413], p < 0.001), cancer (OR = 1.86 [95% CI 1.507-2.279], p < 0.001), chronic kidney disease (CKD) (OR = 1.745 [95% CI 1.427-2.12], p < 0.001), and intracerebral hemorrhage (ICH) (OR = 1.96 [95% CI 1.323-2.846], p = 0.001) were independent risk factors for death from COVID-19. Patients aged 40-80 years make up the majority of the whole patients, and them had similar risk factors with the whole patients. For patients aged <40 years, only cancer (OR = 17.112 [95% CI 6.264-39.73], p < 0.001) and ICH (OR = 31.538 [95% CI 5.213-158.787], p < 0.001) were significantly associated with higher odds of death. For patients aged >80 years, only age (OR = 1.033 [95% CI 1.008-1.059], p = 0.01) and male gender (OR = 1.585 [95% CI 1.301-1.933], p < 0.001) were associated with higher odds of death. The incidence of most complications increases with age, but arrhythmias, gastrointestinal bleeding, and sepsis were more common in younger deceased patients with COVID-19, with only arrhythmia reaching statistical difference (p = 0.039). We found a relatively poor correlation between preexisting risk factors and complications. Conclusions: Coronavirus disease 2019 are disproportionally affected by age for its clinical manifestations, risk factors, complications, and outcomes. Prior complications have little effect on the incidence of extrapulmonary complications.

Ectopic expression of an AGAMOUS homologue gene in Jatropha curcas causes early flowering and heterostylous phenotypes.

Jatropha curcasseeds are abundant in biodiesel, and low seed yields are linked to poor quality female flowers, which creates a bottleneck for Jatropha seed utilization. Therefore, identifying the genes associated with flowering is crucial for the genetic enrichment of seed yields. Here, we identified an AGAMOUS homologue gene (JcAG) from J. curcas. We found that reproductive organs had higher JcAG expression than vegetative organs, particularly the carpel. Rosette leaves were small and misshapen in 35S:JcAG transgenic lines in comparison with those in wild-type plants. JcAG overexpression caused an extremely early flowering, delayed perianth and stamen filament development, small flowers, and significantly shorter Arabidopsis plants with little fruit. In the JcAG-overexpressing line, the homeotic transformation of sepals into pistillate organs was observed, and floral meristem and organ identity genes were regulated. This study provides insights into the JcAG's function and benefits to our knowledge of the underlying the genetic mechanisms related to floral sex differentiation in Jatropha.

Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple.

Sparse signal processing theory has been applied to synthetic aperture radar (SAR) imaging. In compressive sensing (CS), the sparsity is usually considered as a known parameter. However, it is unknown practically. For many functions of CS, we need to know this parameter. Therefore, the estimation of sparsity is crucial for sparse SAR imaging. The sparsity is determined by the size of regularization parameter. Several methods have been presented for automatically estimating the regularization parameter, and have been applied to sparse SAR imaging. However, these methods are deduced based on an observation matrix, which will entail huge computational and memory costs. In this paper, to enhance the computational efficiency, an efficient adaptive parameter estimation method for sparse SAR imaging is proposed. The complex image-based sparse SAR imaging method only considers the threshold operation of the complex image, which can reduce the computational costs significantly. By utilizing this feature, the parameter is pre-estimated based on a complex image. In order to estimate the sparsity accurately, adaptive parameter estimation is then processed in the raw data domain, combining with the pre-estimated parameter and azimuth-range decouple operators. The proposed method can reduce the computational complexity from a quadratic square order to a linear logarithm order, which can be used in the large-scale scene. Simulated and Gaofen-3 SAR data processing results demonstrate the validity of the proposed method.

Dual C-H functionalization of N-aryl tetrahydroisoquinolines: a highly diastereoselective synthesis of dibenzo[a,f]quinolizines via visible-light induced oxidation and inverse electron-demand aza-Diels-Alder reaction.

Described herein is the first example of the application of an iminium intermediate generated by visible-light photocatalyzed oxidation in an inverse electron-demand aza-Diels-Alder reaction. This dual functionalization of both C(sp(3))-H and C(sp(2))-H bonds of N-aryl tetrahydroisoquinolines represents a valuable example for access to polycycles with high diastereoselectivity.