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Table S1 showed the IC50 values for metformin in cells and GPD1 overexpression cells.

Metformin is an oral drug widely used for the treatment of type 2 diabetes mellitus. Numerous studies have demonstrated the value of metformin in cancer treatment. However, for metformin to elicit effects on cancer often requires a high dosage, and any underlying mechanism for how to improve its inhibitory effects remains unknown. Here, we found that low mRNA expression of glycerol-3-phosphate dehydrogenase 1 (GPD1) may predict a poor response to metformin treatment in 15 cancer cell lines. In vitro and in vivo, metformin treatment alone significantly suppressed cancer cell proliferation, a phenotype enhanced by GPD1 overexpression. Total cellular glycerol-3-phosphate concentration was significantly increased by the combination of GPD1 overexpression and metformin treatment, which suppressed cancer growth via inhibition of mitochondrial function. Eventually, increased reactive oxygen species and mitochondrial structural damage was observed in GPD1-overexpressing cell lines treated with metformin, which may contribute to cell death. In summary, this study demonstrates that GPD1 overexpression enhances the anticancer activity of metformin and that patients with increased GPD1 expression in tumor cells may respond better to metformin therapy.Significance:GPD1 overexpression enhances the anticancer effect of metformin through synergistic inhibition of mitochondrial function, thereby providing new insight into metformin-mediated cancer therapy.

Supplementary Table from Comprehensive Evaluation of Machine Learning Models and Gene Expression Signatures for Prostate Cancer Prognosis Using Large Population Cohorts

Supplementary Figure from Comprehensive Evaluation of Machine Learning Models and Gene Expression Signatures for Prostate Cancer Prognosis Using Large Population Cohorts

Overtreatment remains a pervasive problem in prostate cancer management due to the highly variable and often indolent course of disease. Molecular signatures derived from gene expression profiling have played critical roles in guiding prostate cancer treatment decisions. Many gene expression signatures have been developed to improve the risk stratification of prostate cancer and some of them have already been applied to clinical practice. However, no comprehensive evaluation has been performed to compare the performance of these signatures. In this study, we conducted a systematic and unbiased evaluation of 15 machine learning (ML) algorithms and 30 published prostate cancer gene expression–based prognostic signatures leveraging 10 transcriptomics datasets with 1,558 primary patients with prostate cancer from public data repositories. This analysis revealed that survival analysis models outperformed binary classification models for risk assessment, and the performance of the survival analysis methods—Cox model regularized with ridge penalty (Cox-Ridge) and partial least squares (PLS) regression for Cox model (Cox-PLS)—were generally more robust than the other methods. Based on the Cox-Ridge algorithm, several top prognostic signatures displayed comparable or even better performance than commercial panels. These findings will facilitate the identification of existing prognostic signatures that are promising for further validation in prospective studies and promote the development of robust prognostic models to guide clinical decision-making. Moreover, this study provides a valuable data resource from large primary prostate cancer cohorts, which can be used to develop, validate, and evaluate novel statistical methodologies and molecular signatures to improve prostate cancer management.Significance:This systematic evaluation of 15 machine learning algorithms and 30 published gene expression signatures for the prognosis of prostate cancer will assist clinical decision-making.

Peer reviewed: True, Over time, high-temperature superconductor (HTS)-coated conductors (CCs) have proven to be promising candidates for future high-efficiency and high-power density electrical machines. However, their commercialization is handicapped due to the AC dissipative loss that occurs upon exposure to external magnetic fields. In rotating electromagnetic devices, the external magnetic field is a combination of alternating and rotating magnetic fields. Most of the research is devoted to the effect of exposure of the superconductors to alternating magnetic fields only. This article presents an investigation to observe the behavior of HTSCCs under rotating magnetic fields, particularly the AC loss, using a finite-element-based homogeneous H-formulation technique. Our investigation shows that the AC loss could be considerably high when HTSCCs are exposed to rotating magnetic fields and, ultimately, could affect the cooling efficiency of future high-efficiency and high-power density electrical machines.

Electrical machine optimization is normally a high-dimensional nonlinear multi-objective optimization problem. To improve the efficiency, multi-level optimization strategy is currently used, where sensitivity analysis is required for dividing design parameters into different groups. However, the conventional multi-level optimization strategy cannot handle ultra-high-dimensional optimization problems. To attempt this challenge, this paper proposes a sensitivity analysis method with variable weighted intervals to calculate the sensitivity coefficient in the parameter design range. Three improved multi-level optimization strategies based on different optimization algorithms and sequential sensitivity strategy are proposed, analyzed, and compared with the conventional multi-level optimization strategy. Through a case study of a synchronous reluctance machine, it can be seen that the proposed optimization strategies can improve the optimization results and efficiency of ultra-high-dimensional optimization of electrical machines.

In this paper, an improved deadbeat predictive stator flux control (DPSFC) based on disturbance observer is proposed to improve the control performance of in-wheel permanent magnet synchronous motors (PMSMs) with parameter mismatch and disturbance. First, the sensitivity of conventional deadbeat predictive current control to the parameter variation, including flux linkage, stator resistance and stator inductance, is analyzed. Then, a reduced-order observer based on additional disturbance state variables is designed to predict the future stator flux and observe the system disturbance caused by parameter mismatch. The proposed DPSFC method is able to enhance the robustness of the drive performance effectively via the compensations of one-step delay and stator voltage. Finally, the performance of the proposed control method is validated by simulations and experiments on a prototype of an in-wheel PMSM drive.

This paper presents a model predictive sliding control (MPSC) strategy with the dynamic reference tracking for cascaded H-bridge multilevel converters. The nonlinear predicted sliding mode control (PSMC) and linear model predictive control (MPC) are combined as a two-level cascaded control structure. The proposed approach aims to get rid of time-consuming tuning to reach the good performance in the presence of uncertainties and disturbances, such as proportional-integral (PI). The MPC scheme is designed to predict the future optimal current and voltage vectors with a designed cost function. The high level PSMC algorithm is proposed for the dynamic current reference with less chattering problems. The stability of the proposed strategy is analyzed with the Lyapunov direct method. The design principle of the system parameters is introduced with a step-by-step tuning procedure. With the proposed method, the response time can be reduced from 60 to 30 ms during the start-up conditions in the simulation tests. The overshoot of the cell voltages can be eliminated with low harmonics current and dc-link voltage targets tracking. The validity and effectiveness of the proposed method are implemented by the experimental tests on a laboratory two-cell CHB converter. Compared with the PI-based MPC approach, the current total harmonic distortion can be reduced and the DC side performance, including overshoot/undershoot and response speed, can be improved.

For accurate position tracking and robust control of permanent magnet linear synchronous motor (PMLSM) servo systems against various uncertainties, such as parameter change, load disturbance, friction force, and so on, this article proposes a novel super-twisting nonsingular terminal sliding mode control method based on a high-order sliding mode observer. First, a dynamic model of PMLSM with uncertainties is established. A novel nonsingular terminal sliding mode function combined with the super-twisting algorithm is then proposed to design a robust super-twisting nonsingular terminal sliding mode controller, which ensures the system convergence to the equilibrium state within a finite time, effectively eliminating the chattering phenomenon and improving the system robustness. To further improve the system tracking performance and anti-interference ability, a high-order sliding mode observer is used to estimate the system uncertainties. The effective tracking performance and robustness of the proposed control method are validated by the experimental results.

This paper proposes a model predictive current control (MPCC) compensation method based on virtual voltage vectors for single-phase open-circuit faults of an asymmetric six-phase permanent magnet synchronous hub motor (PMHSM). The proposed strategy adopts the normal vector space to decompose the transformation matrix without reconfiguring the controller topology. By analyzing the difference between the open-circuit phase voltage and the state of health, the disturbance term of the prediction vector in the α-β and x-y subspaces is obtained. 64 voltage vectors are obtained from the switching states of the 6-phase two-level inverter, and then these 64 voltage vectors are appropriately compensated and synthesized into 24 new virtual voltage vectors. The new virtual vectors avoid the adjustment of the MPCC weighting factors and the synthesized virtual vectors can suppress current harmonics. Finally, the experimental results show the effective performance of the method before and after the fault-tolerant operation.

Plenty of works have done to enhance the piezoelectricity of potassium-sodium niobate (KNN), aiming to replace lead-zirconate titanate (PZT) in the consideration of eco-friendly requirement. However so far, KNN ceramics with high piezoelectric performances tend to have a low mechanical quality factor (Qm), which could result in excessive dielectric loss, especially when working in high frequencies. Thus, increasing Qm is a crucial task in KNN-based ceramics. By constructing phase boundaries together with inducing oxygen vacancies, a new KNN ceramic system is built by using conventional solid-state method with high Qm (>250), high piezoelectric performance as well as outstanding temperature stability. Optimum overall properties of KNN-based ceramics can be as large as d33 = 231 pC/N, Qm = 355, TC = 366 °C. This work provides a deeper insight to KNN-based ceramics with high mechanical quality factor and makes a progress on the high frequency application of lead-free ceramics.

Porcine epidemic diarrhea virus (PEDV) is the causative agent of porcine epidemic diarrhea, which can cause death in suckling piglets. Vaccines confer only partial protection against new mutant strains, whereas antibodies targeting virus-encoded proteins may be effective prophylactics. In this study, we constructed a recombinant single chain variable fragment (scFv) library from the spleens of two pigs immunized with a recombinant PEDV nucleocapsid (N) protein. Among the positive clones directed against PEDV N protein isolated from the library, four scFvs that showed higher affinity for N were functionally analyzed. These scFvs specifically bound to the PEDV N protein, but not to the transmissible gastroenteritis virus (TGEV) N protein. Their framework regions were highly conserved, whereas their complementarity-determining regions displayed clear diversity. An immunofluorescence assay showed the co-localization of the four scFvs with PEDV N protein in cells. They significantly suppressed PEDV replication, detected with reverse transcription (RT)-quantitative PCR (qPCR; P

Understanding customers’ energy consumption at the individual appliances level is crucial for the planning and im-plementation of demand response (DR) programs. The appliances’ usage profiles can be disaggregated from whole-house energy consumption data using non-intrusive load monitoring (NILM) methods. The appliance load patterns of each customer are considera-bly different, which make it challenging to train a model with strong generalization ability. In this paper, a novel methodology using transfer knowledge between domains for NILM is proposed. A temporal convolutional network is developed to learn the dynamic features of individual appliance load. A domain adaption loss is used to quantify the domain distribution discrepancy between source and target domain representation. By jointly optimizing domain adaptation and energy disaggregation, an invariant representation across domains for the individual appliance states can be learned. Data experiments on ground truth data validate the accuracy and the robustness of the proposed model, and demonstrate its superior transferability and application potential under those scenarios of data shortage.

Modern electronics and electrical power systems require a high energy storage density (Wrec) and a large efficiency (η) to deliver high performances.

Single sheet testers (SSTs) with asymmetrical yokes were developed for convenient measurement of one and two-dimensional (1&2D) magnetic properties of Fe-Si sheets. An inherent drawback of such SSTs is that the magnetic properties in the central region of the specimen are affected by the planar eddy currents induced in the specimen plane facing the pole faces of the yokes, where the magnetic flux enters the specimen in the normal direction. Most relevant studies reported in the literature use large symmetrical double yokes to mitigate the influence of planar eddy currents on the magnetic properties. However, such configuration significantly increases the system complexity. This paper proposes a compact cross-yoke SST with the novel arm-slotted specimen and the integrated 2-D B-H senor to minimize the influence of the planar eddy currents and simplify the structure of the SST. The magnetic properties of non-oriented Fe-Si sheets of 50JN470 under 1&2D magnetizations are measured and analyzed. The experimental results from the proposed cross-yoke SST show the agreement with those measured by using the international standard SST.

Accurate estimation of residential solar photovoltaic (PV) generation is crucial for the power distribution and demand response program implementation. Currently, most distributed PVs are installed behind-the-meters (BTMs), and are thus invisi-ble to the utilities. The existing methods separate the BTM solar generation from the available net load in a centralized manner as-suming that all data are accessible to utilities. However, this can cause privacy issues, since the data are owned by different utilities and they may be unwilling to share their data. To this end, a novel method is proposed for disaggregating community-level BTM so-lar generation using a federated learning-based Bayesian neural network (FL-BNN), which can preserve the privacy of utilities. Specifically, a Bayesian neural network (BNN) is designed as the probabilistic energy disaggregation model with the ability to cap-ture uncertainties. The BNN training process is extended into a decentralized manner based on the federated learning framework. To enable the model customized for each community, the layers of BNN are categorized into shallow and deep layers, and a layerwise parameter aggregation strategy is proposed to update the model. Both community-specific features and community-invariant fea-tures can be learned. The effectiveness of the proposed method is validated on a publicly available dataset.

Bifidobacterium longum subsp. longum BL21 exerts conspicuous hypoglycemic effects via reshaping the gut microbiota and glucose metabolism, and it could be a potential functional food for controlling type 2 diabetes.

Oxy-fuel combustion with low NOx emission has been received more attention in recent years. In this work, a series of oxygen-staged experimental investigations of Shenmu bituminous coal with the preheated combustion technology was carried out under O2/CO2 atmosphere to obtain the effect of oxygen-staged on combustion and NOx emission. The results showed that the increase of excess oxygen coefficient of the primary air(λPr) decreased the temperature difference at each measuring point in the circulating fluidized bed, promoted the production of coal gas, facilitated the release of elements, and improved the mass conversion ratio of volatile matter up to 92.3%. Besides, the increase of λPr inhibited the conversion process of N-5 to N-6. At the same time, the release of nitrogen-containing volatile matter during the partial gasification process was also reduced. As the λPr increases, reducing the excess oxygen coefficient of the secondary air(λSe) or increasing the oxygen partial pressure of primary air were beneficial to reduce the emissions of NOx to a certain extent.

As an efficient control strategy, model predictive current control (MPCC) has rapid response and simple calculation. This article proposes an improved MPCC scheme for permanent-magnet synchronous hub motor (PMSHM) drives. The mentioned control scheme uses the parameter values at the last moment to obtain the back electromotive force (EMF) and utilizes the obtained back EMF to obtain the predicted current value at the next moment. In the actual application of the motor, to enhance the robustness of the control system, a sliding mode controller is used to replace the conventional proportional-integral (PI) speed loop, and a finite position phase-locked loop based on the dichotomy is added to achieve sensorless speed control and provide an accurate rotor position angle. To improve the steady-state performance, the method of duty cycle is introduced, and the null vector and the actual vector are used together in the same control cycle. The simulation and experimental results both show the effectiveness of the proposed MPCC scheme, and the steady-state performance of MPCC is greatly improved compared with traditional MPCC.

In this paper, a new system-level sequential Taguchi method (SLSTM) is proposed to achieve the optimal solution with high robustness for switched reluctance motor (SRM) drive systems. An SRM drive system consisting of a segmented-rotor SRM and the angle position controller is investigated as a case study. In the implementation, the optimization function contains torque, loss, and torque ripple. The control factors of the system are selected according to the sensitivity analysis results. Manufacturing tolerances are considered to guarantee that the optimal solution features low sensitiveness to uncertainties. The process of defining the design levels of all the control factors and noise factor is illustrated and the orthogonal array is established. The optimization of the SLSTM is carried out sequentially until the certain convergence condition is satisfied. Finally, the component-level sequential Taguchi method (CLSTM) is carried out for comparison. It appears that the proposed SLSTM is efficient in searching for the robust optimal solution for the SRM drive system. Besides, it can achieve better output performance, such as higher average torque and lower torque ripple, and a higher level of robustness compared with the initial design and CLSTM.

This paper presents a novel single-source three-phase multilevel converter with voltage boosting capability for medium-voltage photovoltaic applications. This converter consists of different switched-capacitor networks, formed by 1 capacitor and 3 active switches, as a basic switching cell to generate variable dc-link voltage that boosts the input dc-source voltage, producing 2n+1 (n2) voltage levels at the output. It can reduce the dc-link voltage requirements by 50% in comparison to the traditional neutral point clamped (NPC), flying capacitors, active NPC (ANPC), hybrid and hybrid clamped ANPC and cascaded H-bridge topologies, and 75% to advanced ANPC topologies. It can also reduce the number of required switches and capacitors as well as their voltage stresses compared to these state-of-the-art topologies reported in the literature so far. A finite control set model predictive control algorithm is derived to control the converter, which has the capability to control the active and reactive power without distorting the generated grid current quality. The capacitor voltage balancing is inherent in the proposed topology, and thus, there is no need for any additional voltage balancing circuit, which reduces the control complexity. As an example, a 7-level version of the proposed topology is compared to other existing 7-level inverter topologies.

Switched reluctance motors (SRMs) have attracted much attention in industry due to the advantages of low cost, robust structure, high fault tolerance and high torque density. However, several disadvantages like high torque ripples and coil temperatures hinder their industrialization for some applications requiring high dynamic performance, like electric vehicles (EVs). In this paper, a multiobjective and multiphysics design optimization method considering both thermal and electromagnetic performance is presented for a 12/10 SRM. First, the topology of the SRM is introduced and the optimal parameters are defined. Then, the electromagnetic finite element model (FEM) is introduced and the improved transient lumped-parameter thermal model (TLPTM), considering both axial and radial heat transfer for the SRM, is proposed. Second, the objectives and constraints of the optimization are determined. To improve the optimization efficiency, the sequential subspace optimization strategy is employed to find the optimal solution of this high-dimensional design optimization problem. Finally, to validate the effectiveness of the proposed method, both simulation and experimental results are given and discussed. Compared with the initial design, the optimal solution exhibits lower temperature, higher torque, lower torque ripple and less loss.

Perovskite flat-panel X-ray detectors are promising products for realizing low-dose medical imaging, a nondestructive test, and security inspection. However, the perovskite X-ray imager still faces intractable problems such as severe baseline drift, a low signal-to-noise ratio, and rapid performance degradation, which were involved by the notorious intrinsic ion migration of the perovskite functional layer. In this work, sensitive, stable, and portable pixel quasi-two-dimensional (2D) Ruddlesden-Popper (RP) perovskite X-ray imagers were obtained by an advanced solvent-free laminated fabrication approach. A-Site cation engineering of RP perovskites provides a hint for solving the trade-off between stability and detection performance, resulting in a stable pixel X-ray imager that shows a sensitivity of ∼7000 μC Gy

This paper proposes an axial-radial flux hybrid excitation permanent magnet eddy current coupling (HE-PMEC), where the ferrite magnet is employed for producing the radial flux and the DC excitation current is adopted for producing the axial flux. Moreover, a shielding layer is developed to regulate the radial magnetic field. Its magnetic field distribution, electromagnetic performance, and temperature distribution are calculated based on the 3D finite element method (FEM). By adjusting the applied DC excitation current density and the insertion depth of the shielding layer, the variable current starting process, the variable current braking process, and the speed regulation characteristic of the proposed HE-PMEC are obtained. It can be seen that the starting process, braking process, and speed regulation ability of the HE-PMEC have been enhanced greatly. Furthermore, the copper ring shape of the HE-PMEC is optimized by using the proposed improved multilevel optimization method. It can be seen that the performance of the HE-PMEC can be improved by using the new optimized copper ring shape. Lastly, compared with the conventional PMECs, the proposed HE-PMEC has shown its superior electromagnetic performance.

Our recent algorithm,Hapi, infers chromosome-scale haplotypes using genomic data of a small number of single gametes. Its advanced version,IIIandMe, is proposed here to achieve comparable phasing accuracy with as few as three gametes, pushing the analysis to its limit. The new method is validated with simulation and a citrus gamete dataset. The rapid advances in genotyping technologies promise a broad application ofIIIandMein disclosing important genetic information.

As the most promising lead-free piezoelectric ceramics to replace lead zirconate titanate (PZT) ceramics, potassium sodium niobate (KNN) ceramics have been widely studied for their application prospects in various electronic devices. Increasing