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In biomedical data mining, the gene dimension is often much larger than the sample size. To solve this problem, we need to use a feature selection algorithm to select feature gene subsets with a strong correlation with phenotype to ensure the accuracy of subsequent analysis. This paper presents a new three-stage hybrid feature gene selection method, that combines a variance filter, extremely randomized tree, and whale optimization algorithm. First, a variance filter is used to reduce the dimension of the feature gene space, and an extremely randomized tree is used to further reduce the feature gene set. Finally, the whale optimization algorithm is used to select the optimal feature gene subset. We evaluate the proposed method with three different classifiers in seven published gene expression profile datasets and compare it with other advanced feature selection algorithms. The results show that the proposed method has significant advantages in a variety of evaluation indicators. [ABSTRACT FROM AUTHOR]

RNA modification is a key regulatory mechanism involved in tumorigenesis, tumor progression, and the immune response. However, the potential role of RNA modification "writer" genes in the immune microenvironment of gliomas and their effect on the response to immunotherapy remains unclear. The purpose of this study was to evaluate the role of RNA modification "writer" gene in the prognosis and immunotherapy response of low-grade glioma (LGG). The consensus non-negative matrix factorization (CNMF) method was used to identify different RNA modification subtypes. We used a novel eigengene screening method, the variable neighborhood learning Harris Hawks optimizer (VNLHHO), to screen for eigengenes among the RNA modification subtypes. We constructed a principal components analysis score(PCA_score)-based prognostic prediction model and validated it using an independent cohort. We also analyzed the association between PCA_score and the immune and molecular features of LGG. The results suggested that LGG can be divided into two different RNA modification-based subtypes with distinct prognostic and molecular features. High PCA_score was significantly associated with a poor prognosis in LGG and was an independent prognostic factor. A nomogram containing PCA_score and clinical features was constructed, and it showed a significant predictive value. PCA_score was negatively correlated with tumor purity and the abundance of CD4+ T cells in LGG patients. LGG patients with high PCA_score had lower Tumor Immune Dysfunction and Exclusion scores and showed an immunotherapy response. In conclusion, we report a novel RNA modification-based prognostic model for LGG that lays the foundation for evaluating LGG prognosis and developing more effective therapeutic strategies for these tumors. [ABSTRACT FROM AUTHOR]

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discriminative feature genes, and existing methods fail to deal with this problem efficiently. This article proposes a novel metaheuristic approach for gene feature extraction, called variable neighborhood learning Harris Hawks optimizer (VNLHHO). First, the F -score is used for a primary selection of the genes in gene expression data to narrow down the selection range of the feature genes. Subsequently, a variable neighborhood learning strategy is constructed to balance the global exploration and local exploitation of the Harris Hawks optimization. Finally, mutation operations are employed to increase the diversity of the population, so as to prevent the algorithm from falling into a local optimum. In addition, a novel activation function is used to convert the continuous solution of the VNLHHO into binary values, and a naive Bayesian classifier is utilized as a fitness function to select feature genes that can help classify biological tissues of binary and multi-class cancers. An experiment is conducted on gene expression profile data of eight types of tumors. The results show that the classification accuracy of the VNLHHO is greater than 96.128% for tumors in the colon, nervous system and lungs and 100% for the rest. We compare seven other algorithms and demonstrate the superiority of the VNLHHO in terms of the classification accuracy, fitness value and AUC value in feature selection for gene expression data. [ABSTRACT FROM AUTHOR]

For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy. [ABSTRACT FROM AUTHOR]

A non-resonant magnetoelectric (ME) energy converter is presented for scavenging power-frequency magnetic field energy from two-wire power cords. The device is composed of a Terfenol-D/Pb(Mg1/3Nb2/3)O3-PbTiO3(PMNT)/Terfenol-D ME transducer, a NdFeB manget, and E-shaped magnetic yokes. The ME transducer, the magnet, and the magnetic yokes constitute a magnetic circuit, and the bias magnetic field of the ME transducer can be tuned by adjusting the contact area between the magnet and the magnetic yokes to obtain optimal power output. Meanwhile, the power-frequency magnetic field of the power cord is concentrated to the ME transducer by the magnetic yokes, which can potentially improve the power output of the device. Experiments were undertaken to evaluate the potential of energy scavenging from nearby power cords. When coupled to an appliance power cord carrying a current of 16 A, the device generated a rms voltage of 2.46 V and a maximum power of 6.05 \times 10^\mathrm -6 W with a matching load resistance of \sim 1~\textM\Omega . At a duration of 205 ms, the device can supply power for a temperature-humidity wireless sensor through an up-conversion management circuit with the power consumption of 90 mW at transmitting and 18 mW at receiving. [ABSTRACT FROM PUBLISHER]

• A magnetoelectric (ME) generator based on magnetic levitation is presented for vibration energy harvesting. • The generator uses a Halbach array assembled on the suspending magnet bar of the magnetic levitation system. • The Halbach array produces concentrated magnetic field on the ME array which can sense more magnetic field variations. • The proposed mechanism can generate a larger electrical power than that of the conventional design. • An analytical model is established and experimentally validated. This paper proposes a magnetoelectric (ME) vibration generator based on magnetic levitation. The generator uses a Halbach array and a magnet bar to constitute the suspending magnet structure of the magnetic levitation system. The Halbach array employs three NdFeB magnet rings with a particular magnetization pattern, which produces concentrated magnetic field on the outer side where the ME transducer array is placed. Consequently, the ME transducer array undergoes larger magnetic field variations and more vibration energy is converted into electrical energy. An analytical model is developed based on the solution of the kinetic equation and the equivalent circuit of the ME transducer array. Experiments have been conducted and the analytical model is validated by the experimental results. The generator produces a maximal power of 957.8μW with an optimal load resistance of 1.45M Ω at the acceleration of 0.6 g. The proposed device using a Halbach array exhibits a ∼2.5 times higher output power than that of the harvester employing a conventional suspending magnet bar. [ABSTRACT FROM AUTHOR]

Atom search optimization algorithm has good searching ability and has been successfully applied to calculate hydrogeological parameters and groundwater dispersion coefficient. Since the atom search optimization algorithm is only based on the atom force motion model in molecular dynamics, it has some shortcomings such as slow search speed and low precision during the later stage of iteration. A modified atom search optimization based on the immunologic mechanism and reinforcement learning is proposed to overcome the abovementioned shortcomings in this paper. The proposed algorithm introduces a vaccine operator to better utilize the dominant position in the current atom population so that the speed, accuracy, and domain search ability of the atom search optimization algorithm can be strengthened. The reinforcement learning operator is applied to dynamically adjust the vaccination probability to balance the global exploration ability and local exploitation ability. The test results of 21 benchmark functions confirm that the performance of the proposed algorithm is superior to seven contrast algorithms in search accuracy, convergence speed, and robustness. The proposed algorithm is used to optimize the permutation flow shop scheduling problem. The experimental results indicate that the proposed algorithm can achieve better optimization results than the seven comparative algorithms, so the proposed algorithm has good practical application value. [ABSTRACT FROM AUTHOR]

Abundant evidence has indicated that the prognosis of cutaneous melanoma (CM) patients is highly complicated by the tumour immune microenvironment. We retrieved the clinical data and gene expression data of CM patients in The Cancer Genome Atlas (TCGA) database for modelling and validation analysis. Based on single-sample gene set enrichment analysis (ssGSEA) and consensus clustering analysis, CM patients were classified into three immune level groups, and the differences in the tumour immune microenvironment and clinical characteristics were evaluated. Seven immune-related CM prognostic molecules, including three mRNAs (SUCO, BTN3A1 and TBC1D2), three lncRNAs (HLA-DQB1-AS1, C9orf139 and C22orf34) and one miRNA (hsa-miR-17-5p), were screened by differential expression analysis, ceRNA network analysis, LASSO Cox regression analysis and univariate Cox regression analysis. Their biological functions were mainly concentrated in the phospholipid metabolic process, transcription regulator complex, protein serine/threonine kinase activity and MAPK signalling pathway. We established a novel prognostic model for CM integrating clinical variables and immune molecules that showed promising predictive performance demonstrated by receiver operating characteristic curves (AUC ≥ 0.74), providing a scientific basis for predicting the prognosis and improving the clinical outcomes of CM patients. [ABSTRACT FROM AUTHOR]

Abundant evidence has indicated that the prognosis of cutaneous melanoma (CM) patients is highly complicated by the tumour immune microenvironment. We retrieved the clinical data and gene expression data of CM patients in The Cancer Genome Atlas (TCGA) database for modelling and validation analysis. Based on single-sample gene set enrichment analysis (ssGSEA) and consensus clustering analysis, CM patients were classified into three immune level groups, and the differences in the tumour immune microenvironment and clinical characteristics were evaluated. Seven immune-related CM prognostic molecules, including three mRNAs (SUCO, BTN3A1 and TBC1D2), three lncRNAs (HLA-DQB1-AS1, C9orf139 and C22orf34) and one miRNA (hsa-miR-17-5p), were screened by differential expression analysis, ceRNA network analysis, LASSO Cox regression analysis and univariate Cox regression analysis. Their biological functions were mainly concentrated in the phospholipid metabolic process, transcription regulator complex, protein serine/threonine kinase activity and MAPK signalling pathway. We established a novel prognostic model for CM integrating clinical variables and immune molecules that showed promising predictive performance demonstrated by receiver operating characteristic curves (AUC ≥ 0.74), providing a scientific basis for predicting the prognosis and improving the clinical outcomes of CM patients. [ABSTRACT FROM AUTHOR]

This paper presented a passive current sensor in an electric power grid. The sensor consists of an iron–nickel-based ferromagnetic invariant elastic alloy (FIEA) beam with high quality factor ( $Q$ -factor), piezoelectric ceramic Pb(Zr,Ti)O3 (PZT8), and a Permalloy yoke. The beam bends in the magnetic field of a current-carrying wire as a result of the magnetostriction of the FIEA, and the PZT8 produces a voltage proportional to the electric current in the wire. The electric current sensitivity is potentially improved due to the high $Q$ -factor of the FIEA/PZT8 beam and the concentration effect of the Permalloy yoke. Experimental data are taken to evaluate the feasibility of the current sensor. At the power frequency of 50 Hz, the sensor shows the linear sensitivities of 300.5, 177.9, 119.2, and 88.8 mV/A from 0.2 to 5 A when the separating distances are 6, 10, 15, and 20 mm, respectively. The performances indicate that the proposed device is promising for power-frequency current sensing applications. [ABSTRACT FROM PUBLISHER]

This paper presents a non-invasive sensor for electric current monitoring in electric power grid. The sensor consists of a modified shear-mode cymbal transducer and a Halbach array attached on the transducer. The Halbach array generates concentrated magnetic flux density on the side where the electric wire is placed and lowers the magnetic field on the other side. The wire experiences an enhanced Ampere force when energized, and the resulting reaction force of the Ampere force is further amplified by the cymbal transducer. The transducer works in shear mode and produces a voltage proportional to the applied electric current. At low frequencies, the sensor exhibits a flat voltage response. The non-resonant sensitivity of the sensor reaches ∼6.98 mV/A over the frequency range of 10–80 Hz with a separating distance of d = 3.0 mm. The average sensitivity is 6.97 mV/A with high linearity at the power-frequency of 50 Hz, and a small current step change of 0.05 A can be clearly distinguished. [ABSTRACT FROM AUTHOR]