Wang, Y. and Du, Y., 2024. Eutrophication evaluation assessment based on the multidimension cloud model and projection pursuit method.Lake eutrophication evaluation is challenging because the ...evaluation process is uncertain and random and monitored data are usually inaccurate within a wide range. To deal with the uncertainty and randomness in evaluation eutrophication, the integration of multidimension cloud model (MDCM) and projection pursuit (PP) method were proposed, called the MDCM-PP method. The MDCM considers each evaluation factor as a one-dimension attribute, and the weights of evaluation factors were determined by PP method. In addition, the uncertainty and fuzziness of data was processed by triangular fuzzy numbers (TFNs). The combination of the MDCM-PP model and TFNs was applied to Dongting Lake in China to evaluated eutrophication statuses. The results indicated that the eutrophication levels in the East Dongting Lake were more serious than the South Dongting Lake and West Dongting Lake, which is in accordance with other research. The proposed method can consider fuzziness and randomness with the MDCM-PP model and TFNs in the eutrophication evaluation, which can also be applied to other evaluation processes with character of fuzziness and randomness.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Background
Cholangiocarcinoma (CCA) stands as an aggressive malignancy of the biliary tract. The interplay between the tumor and immune system plays a pivotal role in disease progression and ...treatment outcomes. Hence, the present study aimed to extensively explore the immunogenomic landscape of CCA, with the objective of unveiling unique molecular and immunological signatures that could guide personalized therapeutic approaches.
Methods
The study collected data from The Cancer Genome Atlas databases, performed gene set variation analysis for the chemokine ligand 5 (CCL5) high/low expression group, conducted principal component analysis, gene set enrichment analysis enrichment and mutation pattern analysis, generated a heatmap, and performed cox regression analysis.
Results
The two discrete subpopulations were found to exhibit contrasting mutational and immunogenomic characteristics, emphasizing the heterogeneity of CCA. These subsets also showed pronounced discrepancies in the infiltration of immune cells, indicating diverse interactions with the tumor immune microenvironment. Furthermore, the dissimilarities in mutational patterns were observed within the two CCA subgroups, with PBRM1 and BAP1 emerging as the most frequently mutated genes. In addition, a prognostic framework was formulated and validated utilizing the expression profiles of COX16 and RSAD2 genes, effectively segregating patients into high‐risk and low‐risk cohorts. Furthermore, the connections between immune‐related parameters and these risk groups were identified, underscoring the potential significance of the immune microenvironment in patient prognosis. In vitro experiments have shown that COX16 promotes the proliferation and metastasis of CCA cells, whereas RSAD2 inhibits it.
Conclusions
The present study provides an intricate depiction of the immunogenomic landscape of CCA based on CCL5 expression, thereby paving the way for novel immunotherapy strategies and prognostic assessment.
The present study provides a comprehensive analysis of the immunogenomic landscape of cholangiocarcinoma (CCA) based on CCL5 expression, revealing significant heterogeneity and contrasting characteristics between different CCA subgroups. A prognostic framework utilizing COX16 and RSAD2 expression profiles was developed and verified.
RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on ...their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.
RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on ...their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.
This paper describes new configurations of digital-to-analog converters (DACs) based on number theory: (i) DACs consisting of N current sources, N-angle number weighted resistor networks, switch ...arrays and decoders (N= 3, 4, 5,...). (ii) A DAC composed of two current sources, a prime number weighted resistor network, switch array, a decoder; this is based on Goldbach's conjecture in the number theory. Their principles, configurations and operations are presented.
We study the twisted control of the near-field radiative heat transfer between two hyperbolic antiferromagnetic insulators under external magnetic fields. We show that the near-field heat flux can be ...affected by both the twist angle \(\theta\) and the magnitude of the applied magnetic field with different broken symmetries. Irrespective of twist angle, the external magnetic field causes the radiative heat flux to change nonmonotonically, and the minimum heat flux can be found with the magnetic fields of about 1.5 T. Such nonmonotonic behavior is due to the fact that the magnetic field can radically change the nature of the magnon polaritons with time reversal symmetry breaking. The field not only affects the topological structure of surface magnon polaritons, but also induces the volume magnon polaritons that progressively dominate the heat transfer as the field increases. We further propose a twist-induced thermal switch device with inversion symmetry breaking, which can severely regulate radiative heat flux through different magnetic fields. Our findings account for a characteristic modulation of radiative heat transfer with implications for applications in dynamic thermal management.
RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on ...their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.
This paper investigates multi-tone signals for short-time and high quality testing of analog circuit frequency response. First, we study three multi-tone signal generation algorithms for minimum ...crest factor using an arbitrary waveform generator, and show that they are comparable. Then we propose crest factor controlled multi-tone signal generation algorithms for effective testing of transmitters. These algorithms are verified with simulations.
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