Racial Justice and Black Lives in Research: Consider these areas of inquiry

Developed by the Black Matter in Research Working Group 2021 -- Researchers, Ethics & IRB Professionals

Characteristics of phonon transmission across epitaxial interfaces: a lattice dynamic study

Phonon transmission across epitaxial interfaces is studied within the lattice dynamic approach. The transmission shows weak dependence on frequency for the lattice wave with a fixed angle of incidence. The dependence on azimuth angle is found to be related to the symmetry of the boundary interface. The transmission varies smoothly with the change of the incident angle. A critical angle of incidence exists when the phonon is incident from the side with large group velocities to the side with low ones. No significant mode conversion is observed among different acoustic wave branches at the interface, except when the incident angle is near the critical value. Our theoretical result of the Kapitza conductance $G_{K}$ across the Si-Ge (100) interface at temperature $T=200$K is 4.6\times10^{8} {\rm WK}^{-1}{\rmm}^{-2}. A scaling law $G_K \propto T^{2.87}$ at low temperature is also reported. Based on the features of transmission obtained within lattice dynamic approach, we propose a simplified formula for thermal conductanceacross the epitaxial interface. A reasonable consistency is found between the calculated values and the experimentally measured ones.Comment: 8 figure

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding technique

Registration of 'Ok102' wheat

Peer reviewedPlant and Soil SciencesEntomology and Plant PathologyBiochemistry and Molecular Biolog

An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems

Conjunctival Reconstruction with Progenitor Cell-Derived Autologous Epidermal Sheets in Rhesus Monkey

Severe ocular surface diseases are some of the most challenging problems that the clinician faces today. Conventional management is generally unsatisfactory, and the long-term ocular consequences of these conditions are devastating. It is significantly important to find a substitute for conjunctival epithelial cells. This study was to explore the possibility of progenitor cell-derived epidermal sheets on denuded amniotic membrane to reconstruct ocular surface of conjunctiva damaged monkeys. We isolated epidermal progenitor cells of rhesus monkeys by type IV collagen adhesion, and then expanded progenitor cell-derived epidermal sheets on denuded amniotic membrane ex vivo. At 3 weeks after the conjunctiva injury, the damaged ocular surface of four monkeys was surgically reconstructed by transplanting the autologous cultivated epidermal progenitor cells. At 2 weeks after surgery, transplants were removed and examined with Hematoxylin-eosin staining, Periodic acid Schiff staining, immunofluorescent staining, scanning and transmission electron microscopy. Histological examination of transplanted sheets revealed that the cell sheets were healthy alive, adhered well to the denuded amniotic membrane, and had several layers of epithelial cells. Electron microscopy showed that the epithelial cells were very similar in appearance to those of normal conjunctival epithelium, even without goblet cell detected. Epithelial cells of transplants had numerous desmosomal junctions and were attached to the amniotic membrane with hemidesmosomes. Immunohistochemistry confirmed the presence of the conjunctival specific markers, mucin 4 and keratin 4, in the transplanted epidermal progenitor cells. In conclusion, our present study successfully reconstructed conjunctiva with autologous transplantation of progenitor cell-derived epidermal sheets on denuded AM in conjunctival damaged monkeys, which is the first step toward assessing the use of autologous transplantation of progenitor cells of nonocular surface origin. Epidermal progenitor cells could be provided as a new substitute for conjunctival epithelial cells to overcome the problems of autologous conjunctiva shortage

Kallikrein-related peptidase 6 regulates epithelial-to-mesenchymal transition and serves as prognostic biomarker for head and neck squamous cell carcinoma patients

Background: Dysregulated expression of Kallikrein-related peptidase 6 (KLK6) is a common feature for many human malignancies and numerous studies evaluated KLK6 as a promising biomarker for early diagnosis or unfavorable prognosis. However, the expression of KLK6 in carcinomas derived from mucosal epithelia, including head and neck squamous cell carcinoma (HNSCC), and its mode of action has not been addressed so far. Methods: Stable clones of human mucosal tumor cell lines were generated with shRNA-mediated silencing or ectopic overexpression to characterize the impact of KLK6 on tumor relevant processes in vitro. Tissue microarrays with primary HNSCC samples from a retrospective patient cohort (n = 162) were stained by immunohistochemistry and the correlation between KLK6 staining and survival was addressed by univariate Kaplan-Meier and multivariate Cox proportional hazard model analysis. Results: KLK6 expression was detected in head and neck tumor cell lines (FaDu, Cal27 and SCC25), but not in HeLa cervix carcinoma cells. Silencing in FaDu cells and ectopic expression in HeLa cells unraveled an inhibitory function of KLK6 on tumor cell proliferation and mobility. FaDu clones with silenced KLK6 expression displayed molecular features resembling epithelial-to-mesenchymal transition, nuclear β-catenin accumulation and higher resistance against irradiation. Low KLK6 protein expression in primary tumors from oropharyngeal and laryngeal SCC patients was significantly correlated with poor progression-free (p = 0.001) and overall survival (p < 0.0005), and served as an independent risk factor for unfavorable clinical outcome. Conclusions: In summary, detection of low KLK6 expression in primary tumors represents a promising tool to stratify HNSCC patients with high risk for treatment failure. These patients might benefit from restoration of KLK6 expression or pharmacological targeting of signaling pathways implicated in EMT