Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival

Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers

Building the Path to Early Alzheimer\u27s Prediction Using Machine Learning

Alzheimer’s disease (AD) is the most common form of dementia and one of the most prominent challenges of precision healthcare is early identification of AD. To combat this latency in diagnosis, integration of machine learning has been exercised for more cost efficient and powerful diagnostic tools. Specifically, we have developed a workflow for identifying AD within a given sample. Utilizing cerebral cortex proteomic data as a baseline, we were able to test two different forms of feature selection and 6 different machine learning methods. The best performing of these combinations was using a Support Vector Machine (SVM) method with features selected from Maximum Relevance Minimum Redundancy (MRMR) . This method had an average accuracy of 93.25% across and had yielded relatively good precision across 100 iterations. Furthering these types of predictions methods could allow a better quality and longevity of life for those at risk of Alzheimer\u27s Disease. Funding: Funding for this project was supplied by ND EPSCoR STEM (UND0025726), the American Society for Pharmacology & Experimental Therapeutics (ASPET) SURF Program, the Chair of the Department of Biomedical Sciences, the Division of Research & Economic Development at the University of North Dakota, an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P20GM103442, and the Dean of the University of North Dakota School of Medicine & Health Sciences.https://commons.und.edu/as-showcase/1004/thumbnail.jp

Characterization of Ba\u3csub\u3e1-x-y\u3c/sub\u3eCa\u3csub\u3ex\u3c/sub\u3eSr\u3csub\u3ey\u3c/sub\u3eTiO\u3csub\u3e3\u3c/sub\u3e Perovskites as Pb-Free Dielectric Materials

Use of lead-containing piezoelectric components in electrical and electronic devices has been banned on the EU market since July 1st, 2006. Development of lead-free high performance piezoelectric materials to meet the strong market demand is therefore imperative. In this paper, we report a systematic study on the structural, dielectric and ferroelectric properties of one class of lead-free piezoelectric materials, Ba1-x-yCaxSryTiO3 (x = 0-0.4, and y = 0-0.2) ceramics, using techniques such as XRD, SEM, impedance analyzer, and ferroelectric analyzer. It is found that with increasing Sr concentration in Ba1-ySryTiO3 and Ba0.8-ySryCa0.2TiO3, the crystal structure transforms from tetragonal to cubic along with a decreased unit-cell volume. The microstructures of all samples prepared are uniform and dense with the grain size decreasing with Sr content. The Curie temperature decreases faster with Sr and Ca co-doped BaTiO3 than that of Sr or Ca singularly-doped one. Above Curie temperature, a tunability of 31.4% can be achieved at an applied voltage of 30 kV/cm for (Ba0.6Ca0.2Sr0.2TiO3). These properties promise Ba1-x-yCaxSryTiO3 system to be applicable in Pb-free tunable devices

Genome-Wide Analysis of Methylome in the Mouse Brain using Long-Read Sequencing Technology

DNA methylation is an epigenetic modification that transfers a methyl group onto the C-5 position of the cytosine to form 5-methylcytosine. DNA methylation regulates gene expression by recruiting proteins involved in gene repression or by inhibiting the binding of transcription factor(s) to DNA, especially in regulation of Allele Specific Expression (ASE). In this study, we used Oxford Nanopore long-read sequencing technology to profile methylome in the two inbred mouse strains, C57BL/6J (B6) and DBA/2J (D2). Compared with bisulfite conversion followed by Illumina Sequencing, long-read sequencing technology allows us to achieve much longer read length of 4,653.675 base pairs on average while maintaining an average percent identity of 90.775%. We detected millions of methylation events and 1,465 differentially methylated regions (DMRs) between B6 and D2. Understanding more about how DNA methylation patterns of these mice affect neurological phenotype will further research into drug development for neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). This work was conducted in the UND Department of Biology under the advisement of Dr. Xusheng Wang and supported by The UND Center for Biomedical Research Excellence (CoBRE) for Epigenomics of Development and Disease (X.W.), the UND CoBRE for Host-Pathogen Interactions (HPI) (X.W.), the ND EPSCoR STEM program (X.W.), the UND Vice President for Research & Economic Development (VPRED) seed program (X.W.), the American Society for Pharmacology & Experimental Therapeutics (ASPET) SURF Program, the Chair of the Department of Biomedical Sciences, the Division of Research & Economic Development at the University of North Dakota , an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P20GM103442, the Dean of the University of North Dakota School of Medicine & Health Sciences. A special thanks to all my peers in the Xusheng Wang Laboratory: He Huang, Ling Li, Kincaid Rowbotham, Alyssa Erickson, and the UND Genomics Core for performing the DNA extraction and sequencing.https://commons.und.edu/as-showcase/1008/thumbnail.jp

Hydrogen Production Characteristic of Diesel Reforming under Ship SOFC-GT Operation Environment

SOFC/GT hybrid system is proposed as one of the advanced power systems of future ships due to its high efficiency, low emission and fuel flexibility. However, how to efficiently reform marine diesel into H2 is the key to maintaining the operation of SOFC/GT. This paper studies the influence of operating parameters such as S/C, temperature and pressure of ship SOFC/GT hybrid system on H2 production characteristics of marine diesel reforming through numerical simulation and experimental analysis. The results show that under the variable working conditions, the H2 production increases with temperature and the carbon deposition decreases. The increase of S/C promotesthe increase of H2 yield and inhibits carbon deposition. Pressure has a negative effect on H2 production. The research results can provide basic technical support for the safe and efficient operation of the SOFC-GT hybrid power system on ships and the continuous supply of fuel

A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors

On the Performance of RIS-Aided Spatial Scattering Modulation for mmWave Transmission

In this paper, we investigate a state-of-the-art reconfigurable intelligent surface (RIS)-assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) systems, where a more practical scenario that the RIS is near the transmitter while the receiver is far from RIS is considered. To this end, the line-of-sight (LoS) and non-LoS links are utilized in the transmitter-RIS and RIS-receiver channels, respectively. By employing the maximum likelihood detector at the receiver, the conditional pairwise error probability (CPEP) expression for the RIS-SSM scheme is derived under the two scenarios that the received beam demodulation is correct or not. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained based on the CPEP expression. Finally, the derivation results are exhaustively validated by the Monte Carlo simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2307.1466

Repetitive nerve stimulation on survival in amyotrophic lateral sclerosis

ObjectiveNo previous studies investigated the association between decrement of low-frequency repetitive nerve stimulation (LF-RNS) and amyotrophic lateral sclerosis (ALS) survival. We aim to study the relationship between decrement and survival in ALS.MethodsSporadic ALS patients diagnosed at the Department of Neurology, the First Medical Center, Chinese PLA General Hospital from January 2018 to December 2019 were enrolled in this study. Experienced neurologists followed up the participants regularly every 6 months until January 2022. A decremental response of 10% or greater at least in one muscle was considered positive. According to the decrement, the participants were divided into LF-RNS (+) and LF-RNS (−) groups.ResultsOne hundred and eighty-one sporadic ALS patients were recruited in our study, including 100 males and 81 females. Among them, 10 cases (5.5%) were lost to follow-up, 99 cases (54.7%) died, and 72 patients (39.8%) were still alive at the last follow-up. The median survival time of all ALS patients in this study was 42.0 months. There was no significant difference of median survival in LF-RNS(+) group and LF-RNS(−) group (p = 0.159, Kaplan–Meier method). In multivariate Cox regression analysis, age of onset, diagnostic delay, and ALS Functional Rating Scale-Revised (ALSFRS-R) score were associated with ALS survival, but the decrement was not correlated with ALS survival (p = 0.238).ConclusionThe decrement in accessory and ulnar nerves was not associated with the survival of ALS. The decrement of LF-RNS could not be an electrophysiological marker to predict ALS survival

CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model

Accurate, and effective traffic forecasting is vital for smart traffic systems, crucial in urban traffic planning and management. Current Spatio-Temporal Transformer models, despite their prediction capabilities, struggle with balancing computational efficiency and accuracy, favoring global over local information, and handling spatial and temporal data separately, limiting insight into complex interactions. We introduce the Criss-Crossed Dual-Stream Enhanced Rectified Transformer model (CCDSReFormer), which includes three innovative modules: Enhanced Rectified Spatial Self-attention (ReSSA), Enhanced Rectified Delay Aware Self-attention (ReDASA), and Enhanced Rectified Temporal Self-attention (ReTSA). These modules aim to lower computational needs via sparse attention, focus on local information for better traffic dynamics understanding, and merge spatial and temporal insights through a unique learning method. Extensive tests on six real-world datasets highlight CCDSReFormer's superior performance. An ablation study also confirms the significant impact of each component on the model's predictive accuracy, showcasing our model's ability to forecast traffic flow effectively.Comment: 18 page