Analytical Method for Joint Optimization of Ffe and Dfe Equalizations for Multi-Level Signals

Channel equalization is the efficient method for recovering distorted signal and correspondingly reducing bit error rate (BER). Different type of equalizations, like feed forward equalization (FFE) and decision feedback equalization (DFE) are canceling channel effect and recovering channel response. Separate optimization of tap coefficients for FFE and DFE does not give optimal result. In this case FFE and DFE tap coefficients are found separately and they are not collaborating. Therefore, the final equalization result is not global optimal. In the present paper new analytical method for finding best tap coefficients for FFE and DFE joint equalization is introduced. The proposed method can be used for both NRZ and PAM4 signals. The idea of the methodology is to combine FFE and DFE tap coefficients into one optimization problem and allow them to collaborate and lead to the global optimal solution. The proposed joint optimization method is fast, easy to implement and efficient. The method has been tested for several measured channels and the analysis of the results are discussed

Prognostic impact of CDKN2A/B deletion, TERT mutation, and EGFR amplification on histological and molecular IDH-wildtype glioblastoma

BACKGROUND: We aimed to evaluate the clinical outcomes of molecular glioblastoma (mGBM) as compared to histological GBM (hGBM) and to determine the prognostic impact of METHODS: IDH-wildtype GBM patients treated with radiation therapy (RT) between 2012 and 2019 were retrospectively analyzed. mGBM was defined as grade II-III IDH-wildtype astrocytoma without histological features of GBM but with one of the following molecular alterations: RESULTS: Of the 367 eligible patients, the median follow-up was 11.7 months. mGBM and hGBM did not have significantly different OS (median: 16.6 vs 13.5 months, respectively, CONCLUSION: Criteria for mGBM may require further refinement and validation

A Dnn-Ensemble Method for Error Reduction and Training Data Selection in Dnn based Modeling

Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators. This paper presents a powerful approach using an ensemble of DNN s to effectively reduce the training data size in DNN-based modeling problems. A batch of training data with the largest uncertainties is selected using active learning through the variance among the ensemble of DNNs. Subsequently, a greedy sampling algorithm is applied to select a data subset using diversity. Thus, the proposed method can achieve both uncertainty and diversity in data selection. By averaging the outputs of the DNN ensemble, the prediction error can be further reduced. Simple mathematical functions are used to validate the proposed method, and a high-dimensional strip line modeling problem also demonstrates the effectiveness of this DNN-ensemble approach. The proposed method is task agnostic and can be used in other surrogate modeling problems with DNN s

Prolonged response of recurrent IDH-wild-type glioblastoma to laser interstitial thermal therapy with pembrolizumab

Despite the improved understanding of the molecular and genetic heterogeneity of glioblastoma, there is still an unmet need for better therapeutics, as treatment approaches have remained unchanged in recent years. Research into the role of the immune microenvironment has generated enthusiasm for testing immunotherapy (specifically, immune checkpoint inhibitors). However, to date, trials of immunotherapy in glioblastoma have not demonstrated a survival advantage. Combination approaches aimed at optimally inducing response to immune checkpoint inhibitors with radiotherapy are currently being investigated. Herein, the authors describe their experience of the potential benefit and clinical outcomes of using combination pembrolizumab (an immune checkpoint inhibitor) and laser interstitial thermal therapy in a case series of patients with recurren

Nanoscale battery cathode materials induce DNA damage in bacteria

The increasing use of nanoscale lithium nickel manganese cobalt oxide (LixNiyMnzCo1−y−zO2, NMC) as a cathode material in lithium-ion batteries poses risk to the environment. Learning toxicity mechanisms on molecular levels is critical to promote proactive risk assessment of these complex nanomaterials and inform their sustainable development. We focused on DNA damage as a toxicity mechanism and profiled in depth chemical and biological changes linked to DNA damage in two environmentally relevant bacteria upon nano-NMC exposure. DNA damage occurred in both bacteria, characterized by double-strand breakage and increased levels of many putative chemical modifications on bacterial DNA bases related to direct oxidative stress and lipid peroxidation, measured by cutting-edge DNA adductomic techniques. Chemical probes indicated elevated intracellular reactive oxygen species and transition metal ions, in agreement with DNA adductomics and gene expression analysis. By integrating multi-dimensional datasets from chemical and biological measurements, we present rich mechanistic insights on nano-NMC-induced DNA damage in bacteria, providing targets for biomarkers in the risk assessment of reactive materials that may be extrapolated to other nano–bio interactions