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HighlightsSynergistic effect triggers the Ostwald ripening for the downward recrystallization of perovskite to form buried submicrocavities as light output coupler.The simulation suggests the buried submicrocavities can improve the light out-coupling efficiency from 26.8% to 36.2% for near-infrared light.Light-emitting diodes yields peak external quantum efficiency increasing from 17.3% at current density of 114 mA cm−2 to 25.5% at current density of 109 mA cm−2 and a radiance increasing from 109 to 487 W sr−1 m−2 with low rolling-off.Embedding submicrocavities is an effective approach to improve the light out-coupling efficiency (LOCE) for planar perovskite light-emitting diodes (PeLEDs). In this work, we employ phenethylammonium iodide (PEAI) to trigger the Ostwald ripening for the downward recrystallization of perovskite, resulting in spontaneous formation of buried submicrocavities as light output coupler. The simulation suggests the buried submicrocavities can improve the LOCE from 26.8 to 36.2% for near-infrared light. Therefore, PeLED yields peak external quantum efficiency (EQE) increasing from 17.3% at current density of 114 mA cm−2 to 25.5% at current density of 109 mA cm−2 and a radiance increasing from 109 to 487 W sr−1 m−2 with low rolling-off. The turn-on voltage decreased from 1.25 to 1.15 V at 0.1 W sr−1 m−2. Besides, downward recrystallization process slightly reduces the trap density from 8.90 × 1015 to 7.27 × 1015 cm−3. This work provides a self-assembly method to integrate buried output coupler for boosting the performance of PeLEDs.

Even before the onset of the COVID-19 pandemic, children and adolescents were experiencing a mental health crisis, partly due to a lack of quality mental health services. The rate of suicide for Black youth has increased by 80%. By 2025, the health care system will be short of 225,000 therapists, further exacerbating the current crisis. Therefore, it is of utmost importance for providers, schools, youth mental health, and pediatric medical providers to integrate innovation in digital mental health to identify problems proactively and rapidly for effective collaboration with other health care providers. Such approaches can help identify robust, reproducible, and generalizable predictors and digital biomarkers of treatment response in psychiatry. Among the multitude of digital innovations to identify a biomarker for psychiatric diseases currently, as part of the macrolevel digital health transformation, speech stands out as an attractive candidate with features such as affordability, noninvasive, and nonintrusive. The protocol aims to develop speech-emotion recognition algorithms leveraging artificial intelligence/machine learning, which can establish a link between trauma, stress, and voice types, including disrupting speech-based characteristics, and detect clinically relevant emotional distress and functional impairments in children and adolescents. Informed by theoretical foundations (the Theory of Psychological Trauma Biomarkers and Archetypal Voice Categories), we developed our methodology to focus on 5 emotions: anger, happiness, fear, neutral, and sadness. Participants will be recruited from 2 local mental health centers that serve urban youths. Speech samples, along with responses to the Symptom and Functioning Severity Scale, Patient Health Questionnaire 9, and Adverse Childhood Experiences scales, will be collected using an Android mobile app. Our model development pipeline is informed by Gaussian mixture model (GMM), recurrent neural network, and long short-term memory. We tested our model with a public data set. The GMM with 128 clusters showed an evenly distributed accuracy across all 5 emotions. Using utterance-level features, GMM achieved an accuracy of 79.15% overall, while frame selection increased accuracy to 85.35%. This demonstrates that GMM is a robust model for emotion classification of all 5 emotions and that emotion frame selection enhances accuracy, which is significant for scientific evaluation. Recruitment and data collection for the study were initiated in August 2021 and are currently underway. The study results are likely to be available and published in 2024. This study contributes to the literature as it addresses the need for speech-focused digital health tools to detect clinically relevant emotional distress and functional impairments in children and adolescents. The preliminary results show that our algorithm has the potential to improve outcomes. The findings will contribute to the broader digital health transformation. DERR1-10.2196/46970.

Abstract In response to concerns about increasingly intense Atlantic hurricanes, new federal climate and environmental justice policies aim to mitigate the unequal impact of environmental disasters on economically and socially vulnerable communities. Recent research emphasizes that standard procedures for restoring power following extreme weather could be one significant contributor to these divergent outcomes. Our paper evaluates the hypothesis that more economically and socially vulnerable communities experience longer-duration power outages following hurricanes than less vulnerable communities do, conditional on the severity of the impact of the storm itself. Using data from eight major Atlantic hurricanes that made landfall between January 2017 and October 2020 and induced power outages for over 15 million customers in 588 counties in the Southeast, we demonstrate a significant relationship between socioeconomic vulnerability and the duration of time that elapses before power is restored for 95% of customers in a county. Specifically, a one-decile change in the socioeconomic status theme in the Social Vulnerability Index, a measure of vulnerability produced by the Centers for Disease Control and Prevention and the Agency for Toxic Substances and Disease Registry, produces a 6.1% change in expected outage duration in a focal county. This is equivalent to a 170-min average change in the period of time prior to power restoration.

BCR-ABL tyrosine kinase inhibitors (TKI) are used to treat the chronic myeloid leukemia (CML). Many TKI have been developed as the primary treatment to the CML. Imatinib, a first generation TKI, directly targets BCR-ABL with effective results. As the disease becomes more advanced, patients start to develop resistance to imatinib. Due to this effect it is necessary to generate novel treatments for advanced stage CML. Computational tools can predict new drug candidates to target BCR-ABL. We have designed two new drug candidates with different levels of modification, based on the predicted structure activity relationships with BCR-ABL. These new drug candidates are predicted to have better binding affinities with BCR-ABL than imatinib, which can be more potent treatments of the disease.

A new coronavirus (SARS-CoV-2) has been identified as the etiologic agent for the COVID-19 outbreak. Currently, effective treatment options remain very limited for this disease; therefore, there is an urgent need to identify new anti-COVID-19 agents. In this study, we screened over 6,000 compounds that included approved drugs, drug candidates in clinical trials, and pharmacologically active compounds to identify leads that target the SARSCoV-2 papain-like protease (PLpro). Together with main protease (Mpro), PLpro is responsible for processing the viral replicase polyprotein into functional units. Therefore, it is an attractive target for antiviral drug development. Here we discovered four compounds, YM155, cryptotanshinone, tanshinone I and GRL0617 that inhibit SARS-CoV-2 PLpro with IC50 values ranging from 1.39 to 5.63 μmol/L. These compounds also exhibit strong antiviral activities in cell-based assays. YM155, an anticancer drug candidate in clinical trials, has the most potent antiviral activity with an EC50 value of 170 nmol/L. In addition, we have determined the crystal structures of this enzyme and its complex with YM155, revealing a unique binding mode. YM155 simultaneously targets three \"hot\" spots on PLpro, including the substratebinding pocket, the interferon stimulating gene product 15 (ISG15) binding site and zinc finger motif. Our results demonstrate the efficacy of this screening and repurposing strategy, which has led to the discovery of new drug leads with clinical potential for COVID-19 treatments.

•We proposed a data augmentation approach CarveMix for brain lesion segmentation.•CarveMix mixes pairs of annotated images to generate synthetic training images.•The image mixing is performed according to the location and shape of the lesions.•CarveMix was validated on multiple public and private datasets.•The results show that CarveMix improves the quality of brain lesion segmentation. Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and convolutional neural networks (CNNs) have achieved unprecedented success in the segmentation task. Data augmentation is a widely used strategy to improve the training of CNNs. In particular, data augmentation approaches that mix pairs of annotated training images have been developed. These methods are easy to implement and have achieved promising results in various image processing tasks. However, existing data augmentation approaches based on image mixing are not designed for brain lesions and may not perform well for brain lesion segmentation. Thus, the design of this type of simple data augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple yet effective data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other mixing-based methods, CarveMix stochastically combines two existing annotated images (annotated for brain lesions only) to obtain new labeled samples. To make our method more suitable for brain lesion segmentation, CarveMix is lesion-aware, where the image combination is performed with a focus on the lesions and preserves the lesion information. Specifically, from one annotated image we carve a region of interest (ROI) according to the lesion location and geometry with a variable ROI size. The carved ROI then replaces the corresponding voxels in a second annotated image to synthesize new labeled images for network training, and additional harmonization steps are applied for heterogeneous data where the two annotated images can originate from different sources. Besides, we further propose to model the mass effect that is unique to whole brain tumor segmentation during image mixing. To evaluate the proposed method, experiments were performed on multiple publicly available or private datasets, and the results show that our method improves the accuracy of brain lesion segmentation. The code of the proposed method is available at https://github.com/ZhangxinruBIT/CarveMix.git.

Nonsyndromic orofacial cleft (NSOFC) is a severe birth defect that occurs early in embryonic development and includes the subtypes cleft palate only (CPO), cleft lip only (CLO) and cleft lip with cleft palate (CLP). Given a lack of specific genetic factor analysis for CPO and CLO, the present study aimed to dissect the landscape of genetic factors underlying the pathogenesis of these two subtypes using 6,986 cases and 10,165 controls. By combining a genome-wide association study (GWAS) for specific subtypes of CPO and CLO, as well as functional gene network and ontology pathway analysis, we identified 18 genes/loci that surpassed genome-wide significance (P < 5 × 10-8) responsible for NSOFC, including nine for CPO, seven for CLO, two for both conditions and four that contribute to the CLP subtype. Among these 18 genes/loci, 14 are novel and identified in this study and 12 contain developmental transcription factors (TFs), suggesting that TFs are the key factors for the pathogenesis of NSOFC subtypes. Interestingly, we observed an opposite effect of the genetic variants in the IRF6 gene for CPO and CLO. Moreover, the gene expression dosage effect of IRF6 with two different alleles at the same single-nucleotide polymorphism (SNP) plays important roles in driving CPO or CLO. In addition, PAX9 is a key TF for CPO. Our findings define subtypes of NSOFC using genetic factors and their functional ontologies and provide a clue to improve their diagnosis and treatment in the future.

Ferroptosis, a novel type of iron-dependent cell death, plays a vital role in breast cancer progression. However, the function of ferroptosis-induced cancer cell-derived exosomes in breast cancer remains unclear. In this study, we attempted to investigate the impact of breast cancer cells-derived exosomes induced by ferroptosis on the polarization of macrophages and the progression of breast cancer. Erastin was used to induce ferroptosis and breast cancer cell-derived exosomes were identified by transmission electron microscopy. Western blot, quantitative reverse transcription PCR, immunofluorescence, flow cytometry, and ELISA were used to determine the role of exosomes in macrophage polarization. Transwell assays were used to detect breast cancer cell migration, and invasion. Our results showed that erastin promoted ferroptosis in breast cancer cells with increased Fe2+ level and ROS production. Breast cancer cell-derived exosomes induced by ferroptosis were successfully isolated and verified to be internalized by macrophages. In addition, ferroptosis-induced breast cancer cell-derived exosomes (Fe-exo) remarkably diminished M2 marker, Arg-1 expression. The ratio of CD206 macrophages was significantly decreased after Fe-exo treatment. CD206 protein expression and Arg-1 level were dramatically reduced in M2 macrophages incubated by Fe-exo. Moreover, autophagy PCR array showed that the expression of 84 autophagy-related genes were altered after macrophages were incubated by Fe-exo. Furthermore, macrophages incubated by Fe-exo repressed the migration and invasion of breast cancer cells. Ferroptosis-dependent cancer cell-derived exosomes inhibited M2 polarization of macrophages, which in turn inhibited migration and invasion of breast cancer cells. This study provides novel therapeutic strategies for patients with breast cancer.

Since 2019, SARS-CoV-2 has evolved rapidly and gained resistance to multiple therapeutics targeting the virus. Development of host-directed antivirals offers broad-spectrum intervention against different variants of concern. Host proteases, TMPRSS2 and CTSL/CTSB cleave the SARS-CoV-2 spike to play a crucial role in the two alternative pathways of viral entry and are characterized as promising pharmacological targets. Here, we identify compounds that show potent inhibition of these proteases and determine their complex structures with their respective targets. Furthermore, we show that applying inhibitors simultaneously that block both entry pathways has a synergistic antiviral effect. Notably, we devise a bispecific compound, 212-148 , exhibiting the dual-inhibition ability of both TMPRSS2 and CTSL/CTSB, and demonstrate antiviral activity against various SARS-CoV-2 variants with different viral entry profiles. Our findings offer an alternative approach for the discovery of SARS-CoV-2 antivirals, as well as application for broad-spectrum treatment of viral pathogenic infections with similar entry pathways. TMPRSS2 and CTSL/CTSB, host proteases that facilitate SARS-CoV-2 entry, are promising drug targets. Here the authors simultaneously inhibit these host proteases and see synergistic antiviral effects, offering a broad-spectrum intervention against SARS-CoV-2 variants.

The color pattern of insects is one of the most diverse adaptive evolutionary phenotypes. However, the molecular regulation of this color pattern is not fully understood. In this study, we found that the transcription factor Bm-mamo is responsible for black dilute ( bd ) allele mutations in the silkworm. Bm-mamo belongs to the BTB zinc finger family and is orthologous to mamo in Drosophila melanogaster . This gene has a conserved function in gamete production in Drosophila and silkworms and has evolved a pleiotropic function in the regulation of color patterns in caterpillars. Using RNAi and clustered regularly interspaced short palindromic repeats (CRISPR) technology, we showed that Bm-mamo is a repressor of dark melanin patterns in the larval epidermis. Using in vitro binding assays and gene expression profiling in wild-type and mutant larvae, we also showed that Bm-mamo likely regulates the expression of related pigment synthesis and cuticular protein genes in a coordinated manner to mediate its role in color pattern formation. This mechanism is consistent with the dual role of this transcription factor in regulating both the structure and shape of the cuticle and the pigments that are embedded within it. This study provides new insight into the regulation of color patterns as well as into the construction of more complex epidermal features in some insects.