Results 251 to 260 of about 16,362,739 (323)
Molecular Oncology, EarlyView.We analyze cisplatin–DNA adducts (CDAs) and double‐strand breaks (DSBs) in a cell‐cycle‐dependent manner. We find that CDAs form similarly across all cell cycle phases. DSBs arise only in S‐phase. CDAs might not directly impair DSB repair, but S‐phase DSB lesions evolve in the presence of CDAs and disrupt repair in G2, also causing radiosensitization ...
Ye Qiu +10 more
wiley +1 more source
Hijacking emergency granulopoiesis: Neutrophil ontogeny and reprogramming in cancer
Molecular Oncology, EarlyView.Neutrophils are highly plastic innate immune cells; their functions in cancer extend beyond the tumour microenvironment. This Review summarises current understanding of neutrophil maturation and heterogeneity and highlights tumour‐induced granulopoiesis as a systemic programme that expands immature, immunosuppressive neutrophils via tumour‐derived ...
Gabriela Marinescu, Yi Feng
wiley +1 more source
NKCC1: A key regulator of glioblastoma progression
Molecular Oncology, EarlyView.Glioblastoma (GBM) progression is driven by disrupted chloride cotransporter homeostasis. NKCC1 is highly expressed in stem‐like, astrocytic, and progenitor cells, correlating with earlier recurrence, while overall survival remains unaffected. NKCC1 serves as a prognostic marker and potential therapeutic target, linking chloride transporter imbalance ...
Anja Thomsen +5 more
wiley +1 more source
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An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series
Knowledge Discovery and Data Mining, 2023Missing values, which are common in multivariate time series, is most important obstacle towards the utilization and interpretation of those data. Great efforts have been employed on how to accurately impute missing values in multivariate time series ...
Xu Wang +6 more
semanticscholar +1 more source
Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder
IEEE Transactions on Cybernetics, 2022Missing values are ubiquitous in industrial data sets because of multisampling rates, sensor faults, and transmission failures. The incomplete data obstruct the effective use of data and degrade the performance of data-driven models.
Zhuofu Pan +5 more
semanticscholar +1 more source
IEEE Transactions on Evolutionary Computation, 2022
Feature selection (FS) in data with class imbalance or missing values has received much attention from researchers due to their universality in real-world applications.
Yong Zhang +3 more
semanticscholar +1 more source
Feature selection (FS) in data with class imbalance or missing values has received much attention from researchers due to their universality in real-world applications.
Yong Zhang +3 more
semanticscholar +1 more source
Missing Value Monitoring to Address Missing Values in Quantitative Proteomics
2021Many classes of key functional proteins such as transcription factors or cell cycle proteins are present in the proteome at a very low concentration. These low-abundance proteins are almost entirely invisible to systematic quantitative analysis by classical data dependent proteomics methods (DDA).
Vittoria, Matafora, Angela, Bachi
openaire +2 more sources
IEEE Transactions on Industrial Informatics
In complex process industries, multivariate time sequences are omnipresent, whose nonlinearities and dynamics present two major challenges for soft sensing of important quality variables.
Xiaofeng Yuan +7 more
semanticscholar +1 more source
In complex process industries, multivariate time sequences are omnipresent, whose nonlinearities and dynamics present two major challenges for soft sensing of important quality variables.
Xiaofeng Yuan +7 more
semanticscholar +1 more source
The Mathematical Gazette, 1949
The following theorems are closely connected in various ways; one common thread that runs through them is that in each a critical stage of the proof consists in filling in a missing value in the range of values for which the result is known to hold. Another common feature, of no logical importance but none the less the
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The following theorems are closely connected in various ways; one common thread that runs through them is that in each a critical stage of the proof consists in filling in a missing value in the range of values for which the result is known to hold. Another common feature, of no logical importance but none the less the
openaire +1 more source