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Long non‑coding RNA expression identified by microarray analysis: Candidate biomarkers in human acral lentiginous melanoma
2019
Oncology Letters
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Melanoma is a rare but fatal form of skin cancer and acral lentiginous melanoma (ALM) is one of its most common types. Long non-coding RNA (lncRNA) has emerged as a crucial molecule in the development and progression of human cancers, and several studies have revealed that lncRNAs may be associated with the pathogenesis, progression and metastasis of melanoma. To demonstrate the association between ALM and lncRNAs, microarray analysis was performed in tumor and adjacent non-tumor tissues. A
doi:10.3892/ol.2019.11207
pmid:31966073
pmcid:PMC6956422
fatcat:3y7kv7kr4bh53oprbedyinvfti
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... l of 4,488 lncRNAs and 3,913 mRNAs were identified to be differentially expressed in these samples. Among them, 2,211 and 2,277 lncRNAs were upregulated and downregulated in the ALM samples compared with adjacent tissues, respectively. In addition, 1,191 and 2,722 mRNAs were upregulated and downregulated, respectively. Additionally, five randomly selected lncRNAs (fold-change >2; P<0.05) were validated by reverse transcription-quantitative PCR. An lncRNA and mRNA co-expression network and competing endogenous network analysis were also constructed. In summary, the results of the present study may reveal a novel mechanism associated with the pathogenesis and malignant biological processes of ALM and indicate that lncRNAs may serve as potential targets for the treatment of ALM.
Environmental Pollution in Vietnamese Urban
2019
Zenodo
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The North has a Le Hung Duong, Thi Minh Hao Dong, and Cong Tri Tran, "Environmental Pollution in Vietnamese Urban," International Journal of Multidisciplinary Research and Publications (IJMRAP), Volume ...
Le Hung Duong, Thi Minh Hao Dong, and Cong Tri Tran, "Environmental Pollution in Vietnamese Urban," International Journal of Multidisciplinary Research and Publications (IJMRAP), Volume 2, Issue 4, pp. ...
doi:10.5281/zenodo.3483930
fatcat:wv22pvd26vbppodstvhgbuouaa
Construction of Novel Nanocomposites (Cu-MOF/GOD@HA) for Chemodynamic Therapy
2021
Nanomaterials
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The emerging chemodynamic therapy (CDT) has received an extensive attention in recent years. However, the efficiency of CDT is influenced due to the limitation of H2O2 in tumor. In this study, we designed and synthesized a novel core-shell nanostructure, Cu-metal organic framework (Cu-MOF)/glucose oxidase (GOD)@hyaluronic acid (HA) (Cu-MOF/GOD@HA) for the purpose of improving CDT efficacy by increasing H2O2 concentration and cancer cell targeting. In this design, Cu-MOF act as a CDT agent and
doi:10.3390/nano11071843
fatcat:77pywiq4lngfteixnmvi22yxnq
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... D carrier. Cu(II) in Cu-MOF are reduced to Cu(I) by GSH to obtain Cu(I)-MOF while GSH is depleted. The depletion of GSH reinforces the concentration of H2O2 in tumor to improve the efficiency of CDT. The resultant Cu(I)-MOF catalyze H2O2 to generate hydroxyl radicals (·OH) for CDT. GOD can catalyze glucose (Glu) to supply H2O2 for CDT enhancement. HA act as a targeting molecule to improve the targeting ability of Cu-MOF/GOD@HA to the tumor cells. In addition, after loading with GOD and coating with HA, the proportion of Cu(I) in Cu-MOF/GOD@HA is increased compared with the proportion of Cu(I) in Cu-MOF. This phenomenon may shorten the reactive time from Cu-MOF to Cu(I)-MOF. The CDT enhancement as a result of GOD and HA effects in Cu-MOF/GOD@HA was evidenced by in vitro cell and in vivo animal studies.
Catalase-like nanosystem for interlocking trimodal cancer therapy with hypoxia relief
2020
Science China Materials
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Author contributions Luo L designed and engineered the samples; Luo L characterized the samples with support from Cong C; Luo L and Li L performed the experiments; He Y performed the data analysis; Hao ...
doi:10.1007/s40843-020-1492-3
fatcat:ufv6fozbbfhwjng6rucimxcpbu
Best-Effort FPGA Programming: A Few Steps Can Go a Long Way
[article]
2018
arXiv
pre-print
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FPGA-based heterogeneous architectures provide programmers with the ability to customize their hardware accelerators for flexible acceleration of many workloads. Nonetheless, such advantages come at the cost of sacrificing programmability. FPGA vendors and researchers attempt to improve the programmability through high-level synthesis (HLS) technologies that can directly generate hardware circuits from high-level language descriptions. However, reading through recent publications on FPGA
arXiv:1807.01340v1
fatcat:6ocpzvp2cvgkninbtyvvyk7yiu
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... using HLS, one often gets the impression that FPGA programming is still hard in that it leaves programmers to explore a very large design space with many possible combinations of HLS optimization strategies. In this paper we make two important observations and contributions. First, we demonstrate a rather surprising result: FPGA programming can be made easy by following a simple best-effort guideline of five refinement steps using HLS. We show that for a broad class of accelerator benchmarks from MachSuite, the proposed best-effort guideline improves the FPGA accelerator performance by 42-29,030x. Compared to the baseline CPU performance, the FPGA accelerator performance is improved from an average 292.5x slowdown to an average 34.4x speedup. Moreover, we show that the refinement steps in the best-effort guideline, consisting of explicit data caching, customized pipelining, processing element duplication, computation/communication overlapping and scratchpad reorganization, correspond well to the best practice guidelines for multicore CPU programming. Although our best-effort guideline may not always lead to the optimal solution, it substantially simplifies the FPGA programming effort, and will greatly support the wide adoption of FPGA-based acceleration by the software programming community.
Macroscopic dynamics of gene regulatory networks revealed by individual entropy
[article]
2021
bioRxiv
pre-print
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AbstractComplex systems are usually high-dimensional with intricate interactions among internal components, and may display complicated dynamics under different conditions. While it is difficult to measure detail dynamics of each component, proper macroscopic description of a complex system is crucial for quantitative studies. In biological systems, each cell is a complex system containing a huge number of molecular components that are interconnected with each other through intricate molecular
doi:10.1101/2021.10.01.462839
fatcat:mwd7bayx7vbuli47qpobcjop7i
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... nteraction networks. Here, we consider gene regulatory networks in a cell, and introduce individual entropy as a macroscopic variable to quantify the transcriptional dynamics in response to changes in random perturbations and/or network structures. The proposed individual entropy measures the information entropy of a system at each instant with respect to a basal reference state, and may provide temporal dynamics to characterize switches of system states. Individual entropy provides a method to quantify the stationary macroscopic dynamics of a gene set that is dependent on the gene regulation connections, and can be served as an indicator for the evolution of network structure variation. Moreover, the individual entropy with reference to a preceding state enable us to characterize different dynamic patterns generated from varying network structures. Our results show that the proposed individual entropy can be a valuable macroscopic variable of complex systems in characterizing the transition processes from order to disorder dynamics, and to identify the critical events during the transition process.
The Prognostic Value of LncRNA SLNCR1 in Cancers: A Meta-Analysis
2021
Journal of Oncology
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Objective. This meta-analysis was performed to identify the prognostic value of SLNCR1 in multiple cancer types. Methods. Electronic databases, including PubMed, EMBASE, and Web of Science, Cochrane Library, Medline, BioMed Central, Springer, Science Direct, and China National Knowledge Internet (CNKI), were searched for relevant studies up to August 2021, and the hazard ratios (HR) and 95% confidence intervals (95% CI) were calculated to assess the relationship between SLNCR1 expression and
doi:10.1155/2021/3161714
pmid:34733328
pmcid:PMC8560271
fatcat:7ugxykcjtrh7jp76wbsixl4mxq
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... rall survival (OS). Results. 12 studies with a total of 1155 patients with 9 different types of cancers were included in this meta-analysis. The pooled HR indicates that high SLNCR1 expression represented poorer prognosis of cancer (HR = 2.11, 95% CI: 1.59–2.80, I2 = 0%, P < 0.00001 ). Additionally, high SLNCR1 expression was correlated with TNM stage (odds ratio (OR): 1.72, 95% CI: 1.08–2.74, I2 = 62%, P = 0.02 ), lymph node metastasis (LNM) (OR:2.42, 95% CI: 1.61–3.64, I2 = 55%, P < 0.0001 ), and distant metastases (DM) (OR: 2.30, 95% CI: 1.50–3.55, I2 = 27%, P = 0.0002 ). However, no evidence was found for a relationship between SLNCR1 expression and clinical features such as tumor size (OR: 1.71, 95% CI: 0.93–3.14, I2 = 71%, P = 0.09 ), age (OR: 0.86, 95% CI: 0.68–1.08, I2 = 0%, P = 0.19 ), or gender (OR: 1.07, 95% CI: 0.64–1.81, I2 = 55%, P = 0.79 ). Conclusion. Our findings found that high SLNCR1 expression was associated with poor OS, advanced tumor stage, tumor size, LNM, and DM in multiple cancers, indicating that SLNCR1 may serve as a potential prognostic biomarker for cancer patients in China.
MOESM4 of Boosting the biosynthesis of betulinic acid and related triterpenoids in Yarrowia lipolytica via multimodular metabolic engineering
2019
Figshare
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Additional file 4: Figure S3. GCâ MS profiles and mass spectra of chemical standards and fermentation extracts from YLJCC6. (a) GCâ MS profiles of the (1) lupeol standard, (2) betulin standard, (3) betulinic acid standard, and (4) metabolite extracts of YLJCC6. (b) Mass spectra of lupeol and betulinic acid. Red represents the mass spectrum of the metabolite extracts of YLJCC6, and gray represents the mass spectrum of the standards.
doi:10.6084/m9.figshare.8080238.v1
fatcat:gfxfbulvareiriam5hsqwqjmnm
Bioluminescence tomography with Gaussian prior
2010
Biomedical Optics Express
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The association between keloid and osteoporosis: real-world evidence
2021
BMC Musculoskeletal Disorders
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Background Keloids are characterized by disturbance of fibroblast proliferation and apoptosis, deposition of collagen, and upregulation of dermal inflammation cells. This benign dermal fibro-proliferative scarring condition is a recognized skin inflammation disorder. Chronic inflammation is a well-known contributor to bone loss and its sequelae, osteoporosis. They both shared a similar pathogenesis through chronic inflammation. We assessed whether keloids increase osteoporosis risk through
doi:10.1186/s12891-020-03898-8
pmid:33413286
fatcat:v6dt5kts4fc6tizeec2nfo3iti
more »
... National Health Insurance Research Database. Methods The 42,985 enrolled patients included 8597 patients with keloids but no history of osteoporosis; 34,388 controls without keloids were identified from the general population and matched at a one-to-four ratio by age, gender. Kaplan-Meier method was applied to determine cumulative incidence of osteoporosis. Cox proportional hazard regression analysis was performed after adjustment of covariates to estimate the effect of keloids on osteoporosis risk. Results Of the 8597 patients with keloids, 178 (2.07%) patients were diagnosed with osteoporosis while in the 34,388 controls, 587 (1.71%) were diagnosed with osteoporosis. That is, the keloids patients had 2.64-fold higher risk of osteoporosis compared to controls after adjustment for age, gender, Charlson Comorbidity Index and related comorbidities. The association between keloids and osteoporosis was strongest in patients younger than 50 years (hazard ratio = 7.06%) and in patients without comorbidities (hazard ratio = 4.98%). In the keloids patients, a high incidence of osteoporosis was also associated with advanced age, high Charlson Comorbidity Index score, hyperlipidemia, chronic liver disease, stroke, and depression. Conclusions Osteoporosis risk was higher in patients with keloids compared to controls, especially in young subjects and subjects without comorbidities.
The cosmological constant from space-time discreteness
[article]
2020
arXiv
pre-print
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We regard the background of space-time as a physical system composed of discrete volume elements at the Planck scale and get the internal energy of space-time by Debye model. A temperature-dependent minimum energy limit of the particles is proposed from the thermal motion part of the internal energy. As decreases of the temperature caused by the expansion of the universe, more and more particles would be"released" because of the change of the energy limit, we regard these new particles as a
arXiv:2007.01111v1
fatcat:n5xp3nustzeyraboxn5ddmc4ye
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... ce of dark energy. The minimum energy limit also leads to a corrected number of particles in universe and a modified conservation equation. According to the modified conservation equation, an effective cosmological constant consistent with its observed value is obtained.
Hybrid Graph Models for Logic Optimization via Spatio-Temporal Information
[article]
2022
arXiv
pre-print
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Despite the stride made by machine learning (ML) based performance modeling, two major concerns that may impede production-ready ML applications in EDA are stringent accuracy requirements and generalization capability. To this end, we propose hybrid graph neural network (GNN) based approaches towards highly accurate quality-of-result (QoR) estimations with great generalization capability, specifically targeting logic synthesis optimization. The key idea is to simultaneously leverage
arXiv:2201.08455v1
fatcat:63h7zpdmffgaxgms3i3gdryewu
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... ral information from hardware designs and logic synthesis flows to forecast performance (i.e., delay/area) of various synthesis flows on different designs. The structural characteristics inside hardware designs are distilled and represented by GNNs; the temporal knowledge (i.e., relative ordering of logic transformations) in synthesis flows can be imposed on hardware designs by combining a virtually added supernode or a sequence processing model with conventional GNN models. Evaluation on 3.3 million data points shows that the testing mean absolute percentage error (MAPE) on designs seen and unseen during training are no more than 1.2% and 3.1%, respectively, which are 7-15X lower than existing studies.
Circumferential Material Flow in the Hydroforming of Overlapping Blanks
2020
Metals
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The hydroforming of the overlapping blanks is a forming process where overlapping tubular blanks are used instead of tubes to enhance the forming limit and improve the thickness distribution. A distinguishing characteristic of the hydroforming of overlapping blanks is that the material can flow along the circumferential direction easily. In this research, the circumferential material flow was investigated using overlapping blanks with axial constraints to study the circumferential material flow
doi:10.3390/met10070864
fatcat:v7wgcna5zncyzkmwou2omahbs4
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... in the hydroforming of a variable-diameter part. AISI 304 stainless steel blanks were selected for numerical simulation and experimental research. The circumferential material flow distribution was obtained from the profile at the edge of the overlap. The peak value located at the middle cross-section. In addition, the circumferential material flow could be also reflected in the variation of the overlap angle. The variation of the overlap angle kept increasing as the initial overlap angle increased but the improvement of the thickness distribution did not. There was an optimal initial overlap angle to minimize the thinning ratio. An optimal thickness distribution was obtained when the initial angle was 120° for the hydroforming of the variable-diameter part with an expansion of 31.6%.
Delving into Deep Imbalanced Regression
[article]
2021
arXiv
pre-print
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Real-world data often exhibit imbalanced distributions, where certain target values have significantly fewer observations. Existing techniques for dealing with imbalanced data focus on targets with categorical indices, i.e., different classes. However, many tasks involve continuous targets, where hard boundaries between classes do not exist. We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain
arXiv:2102.09554v2
fatcat:h24bcqkurjadrki4zoeaiqnapi
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... rget values, and generalizing to the entire target range. Motivated by the intrinsic difference between categorical and continuous label space, we propose distribution smoothing for both labels and features, which explicitly acknowledges the effects of nearby targets, and calibrates both label and learned feature distributions. We curate and benchmark large-scale DIR datasets from common real-world tasks in computer vision, natural language processing, and healthcare domains. Extensive experiments verify the superior performance of our strategies. Our work fills the gap in benchmarks and techniques for practical imbalanced regression problems. Code and data are available at https://github.com/YyzHarry/imbalanced-regression.
KINH NGHIỆM ÁP DỤNG 5S TỪ THỰC TẾ TRƯỜNG ĐẠI HỌC CÔNG NGHIỆP THÀNH PHỐ HỒ CHÍ MINH
2021
Journal of Science and Technology - IUH
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học Công nghiệp Thành phố Hồ Chí Minh. ...
Mục tiêu của 5S là tạo ra môi trường làm việc sạch đẹp, gọn gàng ngăn nắp, khoa học, đem lại hiệu quả công việc mức độ cao. ...
TẾ TRƯỜNG ĐẠI HỌC CÔNG NGHIỆP
THÀNH PHỐ HỒ CHÍ MINH
© 2020 Trường Đại học Công nghiệp Thành phố Hồ Chí Minh
Thời gian qua, 5S được xem là hoạt động đáng tự hào của trường Đại học Công nghiệp Thành ...
doi:10.46242/jst-iuh.v48i6.1308
fatcat:kust6cpd5fh65lvjjvtv5viulm
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