Novel Techniques for Single-cell RNA Sequencing Data Imputation and Clustering

Advances in single-cell technologies have shifted genomics research from the analysis of bulk tissues toward a comprehensive characterization of individual cells. These cutting-edge approaches enable the in-depth analysis of individual cells, unveiling the remarkable heterogeneity and complexity of cellular systems. By unraveling the unique signatures and functions of distinct cell types, single-cell technologies have not only deepened our understanding of fundamental biological processes but also unlocked new avenues for disease diagnostics and therapeutic interventions.The applications of single-cell technologies extend beyond basic research, with significant implications for precision medicine, drug discovery, and regenerative medicine. By capturing the cellular heterogeneity within tumors, these methods have shed light on the mechanisms of tumor evolution, metastasis, and therapy resistance. Additionally, they have facilitated the identification of rare cell populations with specialized functions, such as stem cells and tissue-resident immune cells, which hold great promise for cell-based therapies.However, one of the major challenges in analyzing scRNA-seq data is the prevalence of dropouts, which are instances where gene expression is not detected despite being present in the cell. Dropouts occur due to technical limitations and can introduce excessive noise into the data, obscuring the true biological signals. As a result, imputation methods are used to estimate missing values and reduce the impact of dropouts on downstream analyses. Furthermore, the high-dimensionality of scRNA-seq data presents additional challenges in effectively partitioning cell populations. Thus, robust computational approaches are required to overcome these challenges and extract meaningful biological insights from single-cell data.There have been numerous imputation and clustering methods developed specifically to address the unique challenges associated with scRNA-seq data analysis. These methods aim to reduce the impact of dropouts and high dimensionality, allowing for accurate cell population partitioning and the discovery of meaningful biological insights. While these methods have unquestionably advanced the field of single-cell transcriptomics, they are not without limitations. Some methods may be computationally intensive, resulting in scalability issues with large datasets, whereas others may introduce biases or overfit the data, potentially affecting the accuracy of subsequent analyses. Furthermore, the performance of these methods can vary depending on the dataset's complexity and heterogeneity. As a result, ongoing research is required to improve existing methodologies and create new algorithms that address these limitations while retaining robustness and accuracy in scRNA-seq data analysis.In this work, we propose three imputation approaches which incorporate with statistical and deep learning framework. We robustly reconstruct the gene expression matrix, effectively mitigating dropout effects and reducing noise. This results in the enhanced recovery of true biological signals from scRNA-seq data and leveraging transcriptomic profiles of single cells. In addition, we introduce a clustering method, which exploits the scRNA-seq data to identify cellular subpopulations. Our method employs a combination of dimensionality reduction and network fusion algorithms to generate a cell similarity graph. This approach accounts for both local and global structure within the data, enabling the discovery of rare and previously unidentified cell populations.We plan to assess the imputation and clustering methods through rigorous benchmarking on simulated and more than 30 real scRNA-seq datasets against existing state-of-the-art techniques. We will show that the imputed data generated from our method can enhance the quality of downstream analyses. Also, we demonstrate that our clustering algorithm is efficient in accurately identifying the cells populations and capable of analyzing big datasets.In conclusion, this thesis propose an alternative approaches to advance current state of scRNA-seq data analysis by developing innovative imputation and clustering methods that enable a more comprehensive and accurate characterization of cellular subpopulations. These advancements potentially have broad applicability in diverse research fields, including developmental biology, immunology, and oncology, where understanding cellular heterogeneity is crucial

Synthesis and characterization of MCM-41 containing CeO2

MCM-41 and cerium containing MCM-41 mesoporous materials were obtained by hydrothermal method under atmospheric pressure (the molar ratio SiO2/CeO2= x, x = 160, 80, 40, 20). The characteristics of all samples were investigated by ThermoGravimetric - Differential Thermal Analysis (TG-DTA), X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM), Energy-Dispersive X-ray spectrometry (EDX) and nitrogen adsorption-desorption isotherms. The results indicated that: particles are sphere, uniform and pore size is about 50 nm; the pore systems are hexagonal structure, ordered arrangement; the samples have high surface area ( 600 m2/g) with narrow pore size distribution curve. Results of EDX showed that the SiO2/CeO2molar ratios of samples were very similar to the molar ratios in gel

Novel and simple patterning process of quantum dots via transfer printing for active matrix qd-led

© 2020 SID. The next generation of a self-emitting display requires precise and stable patterning techniques to shape Red, Green, and Blue pixels using quantum dots. In this study, we propose the novel and simple transfer printing process for the active matrix QD-LEDs

Waterproof Flexible InP@ZnSeS Quantum Dot Light-Emitting Diode

The development of flexible displays for wearable electronics applications has created demand for high-performance quantum dot (QD) light-emitting diodes (QLEDs) based on QD core@shell structures. Emerging indium phosphide (InP)-based core@shell QDs show promise as lighting material in the field of optoelectronics because they are environmentally friendly material, can be produced in a cost-effective manner, and are capable of tunable emission. While efforts have been made to enhance the performance of InP-based QLED, the stabilities of InP@ZnSeS QDs film and InP@ZnSeS-based QLED in water/air are not yet fully understood, limiting their practical applications. Herein, a highly durable, flexible InP@ZnSeS QLED encapsulated in an ultrathin film of CYTOP, a solution-based amorphous fluoropolymer, is demonstrated. The CYTOP-encapsulated green flexible QLED shows an external quantum efficiency (EQE) of 0.904% and a high luminescence of 1593 cd/m2 as well as outstanding waterproof performance. The flexible device emits strong luminescence after being immersed in water for ~20 minutes. Even when subjected to continuous tensile stress with a 5 mm bending radius, the high luminescence is preserved. This waterproof architecture can be a promising strategy for wearable electronics applications

Engineering Core Size of InP Quantum Dot with Incipient ZnS for Blue Emission

Abstract: Blue indium phosphide quantum dot (InP QD) is an emerging colloidal semiconductor nanocrystal, considered as a promising next‐generation photoactive material for light‐emitting purposes. Despite the tremendous progress in blue InP QDs, the synthetic method for tailoring InP core size to realize the blue‐emissive QDs still lags behind. This work suggests a synthetic method for blue‐emitting InP QDs by engineering the core size with an incipient ZnS (i‐ZnS) shell. The formation of i‐ZnS complexes, before the tris(trimethylsilyl)phosphine injection (e.g., before core growth process), restrains the overgrowth of InP nuclei by rapidly forming a ZnS shell on its surface, thereby resulting in further dwarfed InP cores. With additional ZnS shell coating, the blue QDs exhibit a photoluminescence quantum yield of ≈52% at 483 nm. The origin of bandgap diminution with the increase of shell thickness, or with the utilization of ZnSe shell is unraveled via the first‐principles density functional theory simulations. Simulational evidence on InP‐core densification with the shell coating, along with accompanying changes in chemical and structural properties, is presented. The blue‐emitting InP QD device shows a maximum luminance of 1162 cd m−2 and external quantum efficiency of 1.4%

Early metabolic response using FDG PET/CT and molecular phenotypes of breast cancer treated with neoadjuvant chemotherapy

Background: This study was aimed 1) to investigate the predictive value of FDG PET/CT (fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography) for histopathologic response and 2) to explore the results of FDG PET/CT by molecular phenotypes of breast cancer patients who received neoadjuvant chemotherapy. Methods: Seventy-eight stage II or III breast cancer patients who received neoadjuvant docetaxel/doxorubicin chemotherapy were enrolled in this study. FDG PET/CTs were acquired before chemotherapy and after the first cycle of chemotherapy for evaluating early metabolic response. Results: The mean pre- and post-chemotherapy standard uptake value (SUV) were 7.5 and 3.9, respectively. The early metabolic response provided by FDG PET/CT after one cycle of neoadjuvant chemotherapy was correlated with the histopathologic response after completion of neoadjuvant chemotherapy (P = 0.002). Sensitivity and negative predictive value were 85.7% and 95.1%, respectively. The estrogen receptor negative phenotype had a higher pre-chemotherapy SUV (8.6 vs. 6.4, P = 0.047) and percent change in SUV (48% vs. 30%, P = 0.038). In triple negative breast cancer (TNBC), the pre-chemotherapy SUV was higher than in non-TNBC (9.8 vs. 6.4, P = 0.008). Conclusions: The early metabolic response using FDG PET/CT could have a predictive value for the assessment of histopathologic non-response of stage II/III breast cancer treated with neoadjuvant chemotherapy. Our findings suggest that the initial SUV and the decline in SUV differed based on the molecular phenotype

Polygenic risk scores have high diagnostic capacity in ankylosing spondylitis

We would like to thank all participating subjects with AS and healthy individuals who provided the DNA and clinical information necessary for this study. The TASC study was funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) grants P01-052915, R01-AR046208. Funding was also received from the University of Texas Health Science Center at Houston CTSA grant UL1RR02418, Cedars-Sinai GCRC grant MO1-RR00425, Intramural Research Program, NIAMS/NIH, and Rebecca Cooper Foundation (Australia). This study was funded, in part, by Arthritis Research UK (Grants 19536 and 18797), by the Wellcome Trust (grant number 076113), and by the Oxford Comprehensive Biomedical Research Centre ankylosing spondylitis chronic disease cohort (Theme Code: A91202). JZB was funded by a grant from the Zhejiang Provincial Natural Science Foundation of China (LD18H120001LD). The New Zealand data was derived from participants in the Spondyloarthritis Genetics and the Environment Study (SAGE) and was funded by The Health Research Council, New Zealand. HX was funded by the National Natural Science Foundation of China (Grant 81430031) and China Ministry of Science and Technology (973 Program of China 2014CB541800). We acknowledge the Understanding Society: The UK Household Longitudinal Study. This is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. The survey was conducted by NatCen and the genome-wide scan data were analysed and deposited by the Wellcome Trust Sanger Institute. Information on how to access the data can be found on the Understanding Society website https: www.understandingsociety.ac.uk/. French sample collection was performed by the Groupe Française d’Etude Génétique des Spondylarthrites, coordinated by Professor Maxime Breban and funded by the Agence Nationale de Recherche GEMISA grant reference ANR-10-MIDI-0002. We acknowledge and thank the TCRI AS Group for their support in recruiting patients for the study (see below). The authors acknowledge the sharing of data and samples by the BSRBR-AS Register in Aberdeen. Chief Investigator, Prof Gary Macfarlane and Dr. Gareth Jones, Deputy Chief Investigator created the BSRBR-AS study which was commissioned by the British Society for Rheumatology, funded in part by Abbvie, Pfizer and UCB. We are grateful to every patient, past and present staff of the BSRBR-AS register team and to all clinical staff who recruited patients, followed them up and entered data – details here: https://www.abdn.ac.uk/iahs/research/epidemiology/spondyloarthritis.php#panel1011. The QIMR control samples were from parents of adolescent twins collected in the context of the Brisbane Longitudinal Twin Study 1992–2016, support by grants from NHMRC (NGM) and ARC (MJW). We thank Anjali Henders, Lisa Bowdler, Tabatha Goncales for biobank collection and Kerrie McAloney and Scott Gordon for curating samples for this study. MAB is funded by a National Health and Medical Research Council (Australia) Senior Principal Research Fellowship (1024879), and support for this study was received from a National Health and Medical Research Council (Australia) program grant (566938) and project grant (569829), and from the Australian Cancer Research Foundation and Rebecca Cooper Medical Research Foundation. We are also very grateful for the invaluable support received from the National Ankylosing Spondylitis Society (UK) and Spondyloarthritis Association of America in case recruitment. Additional financial and technical support for patient recruitment was provided by the National Institute for Health Research Oxford Musculoskeletal Biomedical Research Unit and NIHR Thames Valley Comprehensive Local Research and an unrestricted educational grant from Abbott Laboratories. This research was funded/supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London and/or the NIHR Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.Peer reviewedPublisher PD

Disability, Home Physical Environment and Non-Fatal Injuries among Young Children in China

We compared the patterns of medically attended injuries between children with and without disabilities and explored the residential environment risks in five counties of Hubei Province in the People's Republic of China by a 1:1 matched case-control study based on the biopsychosocial model of the International Classification of Functioning, Disability and Health--ICF.1201 children aged 1-14 with disabilities and 1201 their healthy counterparts matched as having the same gender, same age, and lived in the same neighborhood were recruited in our study. Characteristics of injuries in the past 12 months were compared between children with and without disabilities. The associations among disability status, home environment factors and injuries were examined in logistic regression analysis taking into account sociodemographic factors.Children with disabilities had a significantly higher prevalence of injury than children without disabilities (10.2% vs. 4.4%; P<.001). The two groups differed significantly in terms of number of injury episodes, injury place and activity at time of injury. Falls were the leading mechanism of injury regardless of disability status. Most of the injury events happened inside the home and leisure activities were the most reported activity when injured for both groups. The univariate OR for injury was 4.46 (2.57-7.74) for the disabled children compared with the non-disabled children. Disabled children whose family raised cat/dog(s) were 76% more likely to be injured during the last 12 months (OR = 1.76; 95% CI = 1.02, 3.02), comparing with those whose family did not have any cat/dog. And for children without disabilities, those whose family had cat/dog(s) were over 3 times more likely to having injuries comparing with those whose family did not have any cat/dog.Children with disabilities had a significantly increased risk for injury. Interventions to prevent residential injury are an important public health priority in children with disabilities

Comparative Transcriptome Analysis of Bacillus subtilis Responding to Dissolved Oxygen in Adenosine Fermentation

Dissolved oxygen (DO) is an important factor for adenosine fermentation. Our previous experiments have shown that low oxygen supply in the growth period was optimal for high adenosine yield. Herein, to better understand the link between oxygen supply and adenosine productivity in B. subtilis (ATCC21616), we sought to systematically explore the effect of DO on genetic regulation and metabolism through transcriptome analysis. The microarrays representing 4,106 genes were used to study temporal transcript profiles of B. subtilis fermentation in response to high oxygen supply (agitation 700 r/min) and low oxygen supply (agitation 450 r/min). The transcriptome data analysis revealed that low oxygen supply has three major effects on metabolism: enhance carbon metabolism (glucose metabolism, pyruvate metabolism and carbon overflow), inhibit degradation of nitrogen sources (glutamate family amino acids and xanthine) and purine synthesis. Inhibition of xanthine degradation was the reason that low oxygen supply enhanced adenosine production. These provide us with potential targets, which can be modified to achieve higher adenosine yield. Expression of genes involved in energy, cell type differentiation, protein synthesis was also influenced by oxygen supply. These results provided new insights into the relationship between oxygen supply and metabolism

Ki-67 can be used for further classification of triple negative breast cancer into two subtypes with different response and prognosis

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Introduction: Triple negative breast cancer (TNBC) has a poorer survival, despite a higher response rate to neoadjuvant chemotherapy. The purpose of this study was to identify the predictive or prognostic value of Ki-67 among patients with TNBC treated with neoadjuvant chemotherapy, and the role of Ki-67 in further classification of TNBC. Methods: A total of 105 TNBC patients who received neoadjuvant docetaxel/doxorubicin chemotherapy were included in the present study. Pathologic complete response (pCR) rate, relapse-free survival (RFS), and overall survival (OS) were compared according to the level of Ki-67. Results: pCR was observed in 13.3% of patients. TNBC with high Ki-67 expression (>= 10%) showed a higher pCR rate to neoadjuvant chemotherapy than TNBC with low Ki-67 expression. None of the low Ki-67 group achieved pCR (18.2% in the high Ki-67 group vs. 0.0% in the low Ki-67 group, P = 0.019). However, a high Ki-67 expression was significantly associated with poor RFS and OS in TNBC, despite a higher pCR rate (P = 0.005, P = 0.019, respectively). In multivariate analysis, high Ki-67 was an independent prognostic factor for RFS in TNBC (hazard ratio = 7.82, P = 0.002). The high Ki-67 group showed a similar pattern of recurrence with overall TNBC, whereas the low Ki-67 group demonstrated a relatively constant hazard rate for relapse. Conclusions: TNBC with high Ki-67 was associated with a more aggressive clinical feature despite a higher pCR rate. High proliferation index Ki-67 can be used for further classification of TNBC into two subtypes with different responses and prognosis.