Halpern-type Iterations for Strong Relatively Nonexpansive Multi-valued Mappings in Banach Spaces

In this paper, an iterative sequence for strong relatively nonexpansive multi-valued mapping by modifying Halpern’s iterations is introduced, and then some strong convergence theorems are proved. At the end of the paper some applications are given also.Key words Multi-valued mapping; Strong relatively nonexpansive; Fixed point; Iterative sequence; Normalized duality mappin

Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. RESULTS: In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients\u27 survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients\u27 survival time. CONCLUSION: The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients\u27 survival by integrating multi-omics data and clinical factors

The N-methyl-D-aspartate antagonist CNS 1102 protects cerebral gray and white matter from ischemic injury following temporary focal ischemia in rats

BACKGROUND AND PURPOSE: Cerebral white matter is as sensitive as gray matter to ischemic injury and is probably amenable to pharmacological intervention. In this study we investigated whether an N-methyl-D-aspartate (NMDA) antagonist, CNS 1102, protects not only cerebral gray matter but also white matter from ischemic injury. METHODS: Ten rats underwent 15 minutes of temporary focal ischemia and were blindly assigned to CNS 1102 intravenous bolus injection (1. 13 mg/kg) followed by intravenous infusion (0.33 mg/kg per hour) for 3.75 hours or to vehicle (n=5 per group) immediately after reperfusion. Seventy-two hours after ischemia, the animals were perfusion fixed for histology. The severity of neuronal necrosis in the cortex and striatum was semiquantitatively analyzed. The Luxol fast blue-periodic acid Schiff stain and Bielschowsky\u27s silver stain were used to measure optical densities (ODs) of myelin and axons, respectively, in the internal capsule of both hemispheres, and the OD ratio was calculated to reflect the severity of white matter damage. RESULTS: Neuronal damage in both the cortex and the striatum was significantly better in the drug-treated group than in the placebo group (P\u3c0.05). The OD ratio of both the axons (0.93+/-0.08 versus 0.61+/-0.18; P\u3c0.01) and the myelin sheath (0.95+/-0.07 versus 0.67+/-0.19; P=0.01) was significantly higher in the CNS 1102 group than in the placebo group. The neurological score was significantly improved in the drug-treated group (P\u3c0.05). CONCLUSIONS: The NMDA receptor antagonist CNS 1102 protects not only cerebral gray matter but also white matter from ischemic injury, most probably by preventing degeneration of white matter structures such as myelin and axons

Interpreting mechanism of Synergism of drug combinations using attention based hierarchical graph pooling

The synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations. The major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusion of AI models un-transparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in the real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, Self-Attention based Node and Edge pool (henceforth SANEpool), that can compute the attention score (importance) of nodes and edges based on the node features and the graph topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. We evaluate SANEpool on molecular networks formulated by genes from 46 core cancer signaling pathways and drug combinations from NCI ALMANAC drug combination screening data. The experimental results indicate that 1) SANEpool can achieve the current state-of-art performance among other popular graph neural networks; and 2) the sub-molecular network detected by SANEpool are self-explainable and salient for identifying synergistic drug combinations

Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data

Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization

Analysis of clinical and dosimetric factors associated with severe acute radiation pneumonitis in patients with locally advanced non-small cell lung cancer treated with concurrent chemotherapy and intensity-modulated radiotherapy

<p>Abstract</p> <p>Background</p> <p>To evaluate the association between the clinical, dosimetric factors and severe acute radiation pneumonitis (SARP) in patients with locally advanced non-small cell lung cancer (LANSCLC) treated with concurrent chemotherapy and intensity-modulated radiotherapy (IMRT).</p> <p>Methods</p> <p>We analyzed 94 LANSCLC patients treated with concurrent chemotherapy and IMRT between May 2005 and September 2006. SARP was defined as greater than or equal 3 side effects and graded according to Common Terminology Criteria for Adverse Events (CTCAE) version 3.0.</p> <p>The clinical and dosimetric factors were analyzed. Univariate and multivariate logistic regression analyses were performed to evaluate the relationship between clinical, dosimetric factors and SARP.</p> <p>Results</p> <p>Median follow-up was 10.5 months (range 6.5-24). Of 94 patients, 11 (11.7%) developed SARP. Univariate analyses showed that the normal tissue complication probability (NTCP), mean lung dose (MLD), relative volumes of lung receiving more than a threshold dose of 5-60 Gy at increments of 5 Gy (V5-V60), chronic obstructive pulmonary disease (COPD) and Forced Expiratory Volume in the first second (FEV1) were associated with SARP (<it>p </it>< 0.05). In multivariate analysis, NTCP value (<it>p </it>= 0.001) and V10 (<it>p </it>= 0.015) were the most significant factors associated with SARP. The incidences of SARP in the group with NTCP > 4.2% and NTCP ≤4.2% were 43.5% and 1.4%, respectively (<it>p </it>< 0.01). The incidences of SARP in the group with V10 ≤50% and V10 >50% were 5.7% and 29.2%, respectively (<it>p </it>< 0.01).</p> <p>Conclusions</p> <p>NTCP value and V10 are the useful indicators for predicting SARP in NSCLC patients treated with concurrent chemotherapy and IMRT.</p

Passive Testing of Electrically Small Antennas in Electronic Systems

In This Paper, a Measurement Scheme, Which Eliminates the Interference of the Common Mode Current, for Electrically Small Antennas is Proposed. Firstly, the Causes and Effects of Common Mode Currents Appearing in Passive Testing Are Analyzed. Then, the Influence of Different Outlet Points of Coaxial Cable on the Passive Testing of Antenna is Studied Experimentally and Numerically. According to the Distribution of the Common Mode Currents on the Ground Plane When the Coaxial Cable Feeds the Antenna, the Minimum Current Point is Selected as the Outlet Point of the Coaxial Cable to Reduce the Influence of Common Mode Current. Additionally, the Influence of the Coaxial Cable\u27s Arrangement and the Soldering Area between Coaxial Cable and Ground Plane on the Antenna under Test is Studied. Finally, Considering the Output Point, Arrangement and Soldering Area of the Coaxial Cable, a Measurement Scheme to Improve the Passive Measurement Accuracy of the Electrically Small Antenna is Proposed