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To clarify the ambiguity of the function of CMTM3 in the development of hepatocellular carcinoma (HCC) and explore its molecular mechanism.The Cmtm3-KO C57BL/6 mouse strain was established using CRISPR-Cas9. Acute liver damage and HCC models were induced by peritoneal injection of 100 or 25 mg/kg.BW N-Nitrosodiethylamine (DEN) to male mice. Liver function and histology were evaluated by blood serum levels of AST and ALT, and HE staining. Gene and protein expression in liver tissues was investigated by RNA-seq, RT-qPCR, Western blotting, immunohistochemistry, and immunofluorescence. Protein-protein interactions were studied by STRING and topological measures. The mRNA expression of CMTM3 and PPARs and patient survival were analyzed using the UALCAN database.Global knockout of Cmtm3 in KO mice was successfully confirmed. Cmtm3 knockout alleviated DEN-induced acute damage to liver histological integrity and liver function, reduced DNA damage and apoptosis, and also caused a significantly reduced number (WT: 8.7 ± 5.5 vs. KO: 2.7 ± 3.1, P = 0.0394) and total size of tumors (WT: 130.9 ± 181.8 mmThis study clarified the pro-tumorigenesis role of CMTM3 in HCC in vivo, possibly through the upregulation of PPARγ and activation of the PPAR pathway.

Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this paper, we propose a novel high-dimensional data-driven cyber physical attack detection and identification approach (HCADI). Firstly, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we construct a high dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score based attack detection and binary matrix factorization based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.

Ferredoxin (Fdx) is regarded as the main electron carrier in biological electron transfer and acts as an electron donor in metabolic pathways of many organisms. Here, we screened a self-sufficient P450-derived reductase PRF with promising production yield of 9OHAD (9α-hydroxy4-androstene-3,17-dione) from AD, and further proved the importance of [2Fe–2S] clusters of ferredoxin-oxidoreductase in transferring electrons in steroidal conversion. The results of truncated Fdx domain in all oxidoreductases and mutagenesis data elucidated the indispensable role of [2Fe–2S] clusters in the electron transfer process. By adding the independent plant-type Fdx to the reaction system, the AD (4-androstene-3,17-dione) conversion rate have been significantly improved. A novel efficient electron transfer pathway of PRF + Fdx + KshA (KshA, Rieske-type oxygenase of 3-ketosteroid-9-hydroxylase) in the reaction system rather than KshAB complex system was proposed based on analysis of protein–protein interactions and redox potential measurement. Adding free Fdx created a new conduit for electrons to travel from reductase to oxygenase. This electron transfer pathway provides new insight for the development of efficient exogenous Fdx as an electron carrier. Graphical Abstract

As a chemotherapeutic drug, cyclophosphamide (CP) has a negative impact on male fertility due to its reproductive toxicity. Melatonin (Mel) promotes the male reproductive system and increases testosterone synthesis. This study is aimed at exploring the molecular mechanism of Mel as a protector of male fertility against CP-induced cytotoxicity. A CP toxicity model was established in adult ICR male mice by intraperitoneal injection of 100 mg/kg CP every other day for a week. Protective effects of Mel on the testis from CP-induced damage were evaluated using four groups of ICR male mice that received intraperitoneal injections of normal saline, 100 mg/kg CP, 10 mg/kg Mel, or the same dosage of CP and Mel, respectively. Testis morphology was observed by hematoxylin and eosin (HE) staining. Sperm quality parameters were evaluated, and sperm function was studied by in vitro fertilization (IVF). Proliferation, meiosis, and pyroptosis markers were examined by western blot. Results showed that CP treatment induced testis toxicity in a time-dependent manner with the most severe damage to the testis at two weeks post CP treatment. CP-treated mice showed reduced testicular weight and impaired spermatogenesis by downregulating PCNA and SYCP3, reduced serum testosterone levels, decreased sperm counts and motility, increased seminiferous tubule vacuolization, and oxidative damage to spermatogenic cells. All these effects, apart from testicular weight, could be ameliorated by Mel administration. The IVF experiment revealed that CP treatment reduced the rates of sperm fertilization and blastocyst development, which were also enhanced by Mel. Mel-treated mice also showed increased expression of proliferation-associated protein PCNA and meiosis-associated proteins REC8, STRA8, and SYCP3, which were all reduced by CP. Furthermore, Mel inhibited the pyroptosis of spermatogenic cells by reducing GSDMD and IL18 expression. In conclusion, this study indicated that Mel might protect the testis from CP-induced DNA damage to germ cells through the alleviation of pyroptosis.

When nanozymes are used in biological analysis, higher activity can improve the detection sensitivity, and better selectivity can eliminate other interference. To improve the specificity and sensitivity, we fabricated an innovative bioconjugated nanozyme with natural enzyme (BNNZ), in which natural ChOx was immobilized onto histidine-modified Fe

Power electronics systems have become increasingly vulnerable to cyber–physical threats due to their growing penetration in the Internet-of-Things (IoT)-enabled applications, including connected electric vehicles (EVs). In response to this emerging need, a cyber–physical-security initiative was recently launched by the IEEE Power Electronics Society (PELS). With increasing connectivity due to vehicle-to-everything (V2X) and the number of electronic control units, connected EVs are facing greater cyber–physical security challenges. However, existing research extensively focuses on the network security of internal combustion engine vehicles and fails to address the cyber–physical security of EVs specifically. In this article, the challenges and future visions of cyber–physical security are discussed for connected EVs from the perspective of firmware security, vehicle charging safety, and powertrain control security. The vulnerabilities of EVs are investigated under a variety of cyberattacks, ranging from energy-efficiency-motivated attacks to safety-motivated attacks. Simulation results, including hardware-in-the-loop (HIL) results, are provided to further analyze the cyberattack impacts on both converter (device) and vehicle (system) levels. More importantly, an architecture for the next-generation power electronics systems is proposed to address the cyber–physical security challenges of EVs. Finally, potential research opportunities are discussed in detail, including detection and migration for firmware security, model-based, and data-driven detection and mitigation. To the best of our knowledge, this is the first comprehensive study on cyber–physical security of powertrain systems in modern EVs.

Breast cancer (BC) is a kind of malignant cancer in women, and it has become the most diagnosed cancer worldwide since 2020. Histone methylation is a common biological epigenetic modification mediating varieties of physiological and pathological processes. Lysine-specific demethylase 1 (LSD1), a first identified histone demethylase, mediates the removal of methyl groups from histones H3K4me1/2 and H3K9me1/2 and plays a crucial role in varieties of cancer progression. It is also specifically amplified in breast cancer and contributes to BC tumorigenesis and drug resistance via both demethylase and non-demethylase manners. This review will provide insight into the overview structure of LSD1, summarize its action mechanisms in BC, describe the therapeutic potential of LSD1 inhibitors in BC, and prospect the current opportunities and challenges of targeting LSD1 for BC therapy.

In this paper, the challenges and a future vision of the cyber-physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyber-attacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyber-attacks in software and cyber networks is described. Simulation and experimental results that show the impact of cyber-attacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cyber-secure power electronics systems. These opportunities include multi-scale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

Image-guided therapies have been on the rise in recent years as they can achieve higher accuracy and are less invasive than traditional methods. By combining augmented reality technology with image-guided therapy, more organs, and tissues can be observed by surgeons to improve surgical accuracy. In this review, 233 publications (dated from 2015 to 2020) on the design and application of augmented reality-based systems for image-guided therapy, including both research prototypes and commercial products, were considered for review. Based on their functions and applications. Sixteen studies were selected. The engineering specifications and applications were analyzed and summarized for each study. Finally, future directions and existing challenges in the field were summarized and discussed.

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. This letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. For comparison, we have evaluated classic data-driven methods, including $K$ -nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

Despite the marked progress in recent years, structured light-based three-dimensional (3D) measurement techniques still have difficulty in capturing mirror surface reflection. The accuracy of 3D reconstruction for mirror objects should be further improved to adapt to the high reflectivity and curvature of such objects. To improve the stripe definition and reconstruction accuracy of highly reflective mirror objects, this paper analyzes the local blur of defocus stripes in phase measuring deflectometry (PMD) system, and presents a method to analyze the spatially varying defocusing and de-blurring, with the aid of a 3D block matching algorithm, thereby focusing on defocus stripes. Experimental results show that the proposed method can achieve micron-level reconstruction accuracy of standard flat mirrors, and detect the defects on highly reflective mirror objects at a high precision.

9α-Hydroxy-4-androstene-3,17-dione (9OHAD), formed by the 9α-hydroxylation reaction catalyzed by 3-ketosteroid-9-hydroxylase (KSH), is an important precursor for the synthesis of adrenocortical hor...

With the dramatic growth of seismic data volume, efficient and accurate seismic source location has become a significant challenge to seismologists. Recently, time reversal imaging (TRI) has been widely applied in automatic seismic source location for its robustness and accuracy, but its wave-equation-based implementation is usually computationally expensive. To achieve an efficient in situ and real-time source location, the emerging sensor network is a good option. In this article, we propose a simplified Gaussian beam TRI (SGTRI) method to implement the seismic source location in a distributed sensor network. Gaussian beam (GB) is a high-frequency asymptotic solution of the wave equation, which can help reduce the computation costs of the wavefield extrapolation in conventional TRI. Traditionally, the GB construction for reflection seismic imaging covers the entire subsurface space. However, for certain source localization, only limited areas contribute. Thus, we propose a beamforming-technique-based simplified GB construction to further boost efficiency. Then, we propose an imaging condition for the SGTRI to construct the final source location map. Using synthetic experiments, we demonstrate the accuracy, robustness, and efficiency of the proposed method compared with conventional TRI. In the end, a field application also shows promising results.

Structured light is a widely employed approach for profile measurement in active stereo vision. Recent methods have developed learning-based single pattern structured light systems and unstructured light stereo vision systems. However, their methods require additional cameras or a diffractive optical element. In addition, the calibration methods of the learning-based structured light systems require a large number of images, while the stereo vision method cannot detect the blocked region. Classically, a digital projector is treated as a pseudocamera, and the stripe pattern is employed to measure the depth. However, an additional pattern is employed to calibrate the camera-projector system in the stripe-based method. In this paper, 24 reliable features and a novel calibration algorithm are proposed. With the proposed calibration algorithm, the stripe patterns are calibrated without additional patterns, and with the proposed RGB features, the number of encoded columns is enlarged significantly. Experiments demonstrate that the proposed method is accurate and robust.

Islanded nanogrids (NGs) are autonomous systems consisting of small-scale generation units including renewable energy sources and traditional fuel generators and energy storage systems (ESS) that typically serve few buildings or loads. This work aims at developing and validating a new optimal energy management (EM) algorithm for an islanded NG. To minimize the generator's operating cost and maximize battery availability at each operating cycle, dynamic programming (DP) framework is employed to solve the underlying optimization problem. The goal of the proposed approach is to ensure the use of maximum available solar power and to achieve optimal battery state of charge. To meet that goal, the management of the ESS is formulated as a stochastic optimal control problem, where nonlinearities in the battery discharging process are considered. A Markov model is constructed for predicting the probability distribution of the solar production used in the stochastic DP formulation. Simulation results are given to illustrate the efficacy of the proposed DP-based approach compared to a rule-based algorithm. Finally, a hardware-in-the-loop system is used to evaluate the real-time operation of the proposed EM algorithm.

To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, and depth. However, multimodal data often suffer from the position shift problem, i.e., the image pair is not strictly aligned, making one object has different positions in different modalities. For the deep learning method, this problem makes it difficult to fuse multimodal features and puzzles the convolutional neural network (CNN) training. In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem. First, a region feature (RF) alignment module with adjacent similarity constraint is designed to consistently predict the position shift between two modalities and adaptively align the cross-modal RFs. Second, we propose a novel region of interest (RoI) jitter strategy to improve the robustness to unexpected shift patterns. Third, we present a new multimodal feature fusion method that selects the more reliable feature and suppresses the less useful one via feature reweighting. In addition, by locating bounding boxes in both modalities and building their relationships, we provide novel multimodal labeling named KAIST-Paired. Extensive experiments on 2-D and 3-D object detection, RGB-T, and RGB-D datasets demonstrate the effectiveness and robustness of our method., Journal extension of the previous conference paper arXiv:1901.02645, see IEEE page https://ieeexplore.ieee.org/document/9523596

Geophysics-focused missions and improved geophysical data sets are critical for the future exploration of icy ocean worlds. Of particular interest is the exploration of the Galilean moon, Europa, and the Saturnian moons, Titan and Enceladus. These bodies likely have geologically active surfaces and may harbor habitable subsurface environments. Placing any candidate signatures for life in context requires further knowledge of the interior of these worlds. While the surfaces of these bodies have been mapped, their interiors remain poorly understood. Geophysical approaches such as geodesy, seismology, ice-penetrating radar and altimetry measurements, and electromagnetometry would provide critical information regarding the interior of these icy ocean worlds. The approaches described below would answer key science questions regarding ice shell and ocean dynamics, internal structure and interior layer thicknesses, near-surface structure, and how material from the deep interior might be exchanged with the surface. Here we outline the geophysical environments of Enceladus, Europa, and Titan; some outstanding science questions that remain to be addressed; and examples of the geophysical approaches that can provide the context to better understand icy ocean worlds.

The Internet of Things (IoT) refers to a network of connected devices that collects and exchanges data through the Internet. These things can be artificial or natural and interact as autonomous agents that form a complex system. In turn, business process management (BPM) was established to analyze, discover, design, implement, execute, monitor, and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and in practice, we strongly believe that, on the one hand, the management of IoT applications will greatly benefit from BPM concepts, methods, and technologies. On the other hand, the IoT poses challenges that will require enhancements and extensions of the current state of the art in the BPM field. In this article, we question the extent to which these two paradigms can be combined, and we discuss emerging challenges and intersections from a research and practitioner's point of view in terms of complex software systems development.

Near-surface imaging with distributed sensor networks (DSN) is promising for planet exploration, which affordably generates a near-surface velocity model. Recently, an Eikonal tomography-based ambient noise seismic imaging (ANSI) algorithm was implemented in a DSN to realize real-time and in-situ near-surface imaging. However, only using data from neighbors to generate a velocity map cannot have enough stacking samples to generate high-quality results. Also, the neighbor range increase will result in the exponential rise of communication costs. To overcome this problem, we propose a new decentralized Eikonal tomography algorithm in the DSN. The main idea is to change the source-based algorithm to a receiver-based one, which we call common receiver decentralized Eikonal tomography (CR-TomoEK). With CR-TomoEK, nodes fully utilize signals from neighbors to generate partial velocity maps, when combined, lead to the final output. When compared with the original Eikonal algorithm, the stacking number is significantly increased, output quality is higher than before, and there is a significant reduction in communication cost. We performed experiments on both synthetic data and real data from the USArray Transportable Array. Both imaging quality and communication cost are considered in the algorithm validation. The result shows that our algorithm significantly increases the output quality while keeping the communication cost safe to generate a real-time result.

3D facial expression dataset plays an essential role in computer vision and computer graphics, especially to the data-driven machine learning algorithms like deep-learning etc. In contrast with traditional low-resolution and low-accuracy 3D face related datasets, an accurate and dense facial expression database was introduced in this paper. During the period from January to June 2019, we have collected 8,000 3D facial expression models and 32,000 texture images from 500 subjects. The 3D facial capture device works based on structured light principle. The procedure of 3D face capture and property of 3D samples are introduced in details. The proposed high-accuracy 3D face dataset can be used in many 3D applications, like recognition, animation, and 3D facial expression simulations.

Distributed sensor networks can be used as passive seismic sensors to image and monitor subsurface and underground activities. Passive seismic surface-wave imaging adopts background ambient sounds from a far-field energy source. Because high frequency components decay a lot between the neighboring stations, conventional sparse sensor networks cannot image small-scale and shallow objects. In this article, we propose to use local seismic spatial autocorrelation coefficients, obtained by the combinations of independent dense sensor network measurements and pre-processed readings of its neighbor(s), to perform real-time collaborative imaging of the shallow subsurface objects. First, we derive the high-frequency spectral coefficient based shallow subsurface imaging method. Then, we apply the proposed approach to image a shallowly buried pipeline and obtain promising results. Furthermore, based on a time-lapse manner, the water leakage from the buried pipeline can also be detected using distributed computations between sensors. Comparisons and analysis of field deployments are made to validate the effectiveness and performance of the proposed method.

Shallow velocity variations can be caused by different reasons, which could be related to infrastructure security. Among seismic-based temporal velocity analysis methods, ambient-noise-based spatial autocorrelation (SPAC) provides the finest shallow imaging resolution. We present a continuous time-lapse SPAC (tSPAC) approach to retrieve the shallow velocity variations. In our field application, due to soil moisture changes caused by water leakage, the seismic velocity changes over the time. Based on the SPAC method, we use the extracted surface wave (Rayleigh wave) to estimate the shear wave velocity as a function of depth. Using a time-lapse manner, we demonstrate that velocity variations due to water leakage can be detected from passive ambient seismic noise. The field deployment results agree well with the ground truth of experiment setups. The success of our study demonstrates that the in situ near-surface seismic velocity can be accurately imaged by tSPAC. This technique can be used to monitor seismic velocity change and further investigate not only the fluid saturation, but also other associated changing conditions, such as stress and temperature.

Cyber attacks and malfunctions challenge the wide applications of Internet of Things (IoT). Since they are generally designed as embedded systems, typical auto-sustainable IoT devices usually have a limited capacity and a low processing power. Because of the limited computation resources, it is difficult to apply the traditional techniques designed for personal computers or super computers, like traffic analyzers and antivirus software. In this paper, we propose to leverage statistical learning methods to characterize the device behavior and flag deviations as anomalies. Because the system statistics, such as CPU usage cycles, disk usage, etc., can be obtained by IoT application program interfaces, the proposed framework is platform and deviceindependent. Considering IoT applications, we train multiple machine learning models to evaluate their feasibility and suitability. For the target auto-sustainable IoT devices, which operate well-planned processes, the normal system performances can be modeled accurately. Based on time series analysis methods, such as local outlier factor, cumulative sum, and the proposed adaptive online thresholding, the anomalous behaviors can be effectively detected. Comparing their performances on detecting anomalies as well as the computation sources required, we conclude that relatively simple machine learning models are more suitable for IoT security, and a data-driven anomaly detection method is preferred.

Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyber attacks. This paper proposes the first energy auditing and analytics-based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregation-aggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances.