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Detecting anomalies in satellites holds immense importance within the aerospace industry. Many current detection methods only focus on temporal correlations and ignore spatial correlations. To take into account both temporal and spatial features, we proposed to employ Graph Neural Networks (GNN). In fact, to develop a GNN approach that is applicable to the satellite power system anomaly detection in real-world scenarios, it is imperative to take into account the specific requirements of the aerospace industry. Our analysis and research indicate that the critical factors lie in model interpretability and data characteristics. First, as model interpretability can bring trustworthiness, traceability, and guidance, it is indispensable for the application of deep learning algorithms in satellite telemetry data anomaly detection. We created a novel graph filter: Adaptive Quadratic Approximation Graph Filter (AQAGF), which is capable of both approximation ability and response time. It is transformed into a spectral graph convolution neural network that can draw the frequency response function to provide the model interpretability. Then, to address the difficulties of telemetry data characteristics: strong noise and missing data, we introduced autoencoder architecture and adversarial training strategy to further design our anomaly detection method, called Interpretable Spatial-Temporal Graph Anomaly Detection (ISTGAD) based on AQAGF, which is robust to noise and data missing. And it is able to capture temporal and spatial correlations together within a transparent working mechanism. Finally, experiments proved that our model has better anomaly detection performance than other state-of-the-art methods, and we also provide visualizations to showcase the interpretability and working mechanism of our model. Our code and data are publicly available at: https://github.com/DiYi1999/ISTGAD. [ABSTRACT FROM AUTHOR]

Accurate state of health (SOH) estimation is basis for safe and reliable operation of lithium-ion batteries. In practice, accurate and reliable SOH estimation remains a challenge due to complex and dynamic operating conditions. In this paper, we propose a physics-guided neural network (PGNN) for SOH estimation of lithium-ion batteries. The physical knowledge is embedded into neural network from both explicit and implicit perspectives. Specifically, we extract physically meaningful features from the relaxation voltage segment of a fully charged battery based on an equivalent circuit model (ECM). These features and the limited current segment during constant voltage charging mode form the joint inputs for PGNN, both of which are less affected by the charging/discharging strategies. During the model optimization, the properties of the battery degradation are considered so that the model can learn better feature embeddings. To validate the proposed method, battery degradation experiments are performed to generate data over the entire battery life cycle. Finally, the superiority and effectiveness of the proposed method are validated on two datasets. • We propose a physics-guided neural network for SOH estimation. • A limited charging fragment is used for SOH estimation. • The physical features are extracted from relaxation voltage curve. • We consider battery degradation properties during the training. The effectiveness is verified on two datasets. [ABSTRACT FROM AUTHOR]

Breast cancer (BRCA) has severely threatened women's health worldwide. Radiotherapy is a treatment for BRCA, which applies high doses of ionizing radiation to induce cancer cell death and reduce disease recurrence. Radioresistance is one of the most important elements that affect the therapeutic efficacy of radiotherapy. Long noncoding RNAs (lncRNAs) are suggested to dominate crucial roles in regulating the biological behavior of BRCA. Currently, some studies indicate that overexpression or inhibition of lncRNAs can greatly alter the radioresistance of BRCA. In this review, we summarized the knowledge on the classification and function of lncRNAs and the molecular mechanism of BRCA radioresistance, listed lncRNAs related to the BRCA radioresistance, highlighted their underlying mechanisms, and discussed the potential application of these lncRNAs in regulating BRCA radioresistance. [ABSTRACT FROM AUTHOR]

Airy beams are a kind of non-diffracting beams and so-called accelerating beams with great potential applications in many fields recently. In this work, we investigate the self-accelerating characteristics and the self-healing abilities of Airy beams in uniformly accelerated space. It shows that the process of the Airy beams propagating in accelerated space will be speed up for the positive acceleration or slow down for the negative acceleration. The result may be useful to explore the behavior of light in different accelerated space. • The analytical expression of Airy beams in accelerated space is derived by using matrix-optics method and its evolution is revealed. • The self-accelerating characteristics and the self-healing abilities of Airy beams in uniformly accelerated space are investigated. • The propagation process of Airy beams will be accelerated in the accelerated space or suppressed in the decelerated space. [ABSTRACT FROM AUTHOR]

Deep neural networks have been widely used in battery health management, including state-of-health (SOH) estimation and remaining useful life (RUL) prediction, with great success. However, traditional neural networks still lack transparency in terms of explainability due to their "black-box" nature. Although a number of explanation methods have been reported, there is still a gap in research efforts towards improving the model benefiting from explanations. To bridge this gap, we propose an explainability-driven model improvement framework for lithium-ion battery SOH estimation. To be specific, the post-hoc explanation technique is used to explain the model. Beyond explaining, we feed the insights back to model to guide model training. Thus, the trained model is inherently explainable, and the performance of the model can be improved. The superiority and effectiveness of the proposed framework are validated on different datasets and different models. The experimental results show that the proposed framework can not only explain the decision of the model, but also improve the model's performance. Our code is open source and available at: https://github.com/wang-fujin/Explainability-driven_SOH. • We propose an explainability-driven model improvement framework for SOH estimation. • The explanation was used to guide model training. • The trained model is inherently explainable. • The effectiveness was verified on different datasets and backbones. [ABSTRACT FROM AUTHOR]

Online capacity estimation of lithium-ion batteries plays an important role in battery management systems. Accurate estimation of the current capacity of the battery is helpful for predictive maintenance. Data-driven capacity estimation methods have become increasingly popular in recent years. However, most data-driven approaches assume that the battery degradation data used to train and test obeys the same distribution. In practical applications, there is often a discrepancy in the distributions of the training set and the test set due to differences in the internal chemistry of batteries and various operating conditions. The existing methods for domain adaptive capacity estimation force the global features of the source and target domains to be aligned, but do not take into account the domain-specific information. Thus, models may not extract domain-invariant representation. To address this problem, We propose a feature D isentanglement and tendency R etainment network (DR-Net) for domain adaptive capacity estimation. Specifically, our DR-Net can disentangle the extracted features from raw data into domain-invariant shared features and domain-specific private features while retaining degradation trend information. Experimental results show that the proposed method achieves more competitive results in both estimation accuracy and robustness than other state-of-the-art methods. Our code is available at: https://github.com/wang-fujin/battery_capacity_estimation. • We proposed a DR-Net for domain adaptive capacity estimation of lithium-ion battery. • Our DR-Net can disentangle features into shared feature and private feature. • The domain-invariant shared features can retain degradation trend information. • Our method achieves more competitive results than other state-of-the-art methods. • Public dataset is used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Laboratory-scale plastic silos were prepared to examine bacterial factors associated with the aerobic stability of total mixed rations (TMR) preserved as a silage after mixing. In experiment 1, TMR silages containing wet brewers grains (BG), hay (lucerne or sudangrass), maize grain, wheat bran, beet pulp and liquid molasses, were ensiled for 14 and 56 days, and bacterial communities were examined by denaturing gradient gel electrophoresis (DGGE). The silages resisted aerobic deterioration for as long as 2 weeks, except that heating occurred after 5.5 days in 14-day silage prepared with sudangrass hay. The DGGE analyses indicated that, although not found in the materials, Lactobacillus buchneri and L. brevis were detectable in long-stored TMR silage. L. delbrueckii and L. fermentum were found in the materials, and appeared to survive during fermentation and after exposure to air. There were few changes in the DGGE profiles before and after heating in deteriorated silage containing sudangrass hay. In experiment 2, TMR silage with ingredients similar to those in experiment 1 was prepared, and culturable lactic acid bacteria were isolated from the plates of de Man-Rogosa-Sharpe agar. L. delbrueckii and L. fermentum were obtained from the materials but not from TMR silage. L. buchneri and L. brevis were not found in the materials, but more than one-third of the isolates were L. buchneri and L. brevis in 14- and 56-day silages, respectively. In experiment 3, whole crop maize was inoculated with L. buchneri, L. brevis, L. delbrueckii and L. fermentum isolates, and fermentation and aerobic stability were determined. No changes occurred from L. brevis, L. delbrueckii and L. fermentum inoculation, while aerobic spoilage was suppressed by L. buchneri with increases in pH and acetic acid content. Results indicate that, whilst not found in BG or the TMR mixture, L. buchneri can become detectable during fermentation, and this bacterium helps establish aerobic stability of BG-containing TMR silage. [Copyright &y& Elsevier]

Predicting Remaining Useful Life (RUL) of lithium-ion batteries plays an important role in battery health management. The existing methods have some limitations in battery RUL prediction, including the problem of error accumulation and the inapplicability of RUL models under different working conditions. For one battery, the existing prediction methods have the problem of error accumulation; for batteries under different working conditions, the corresponding lifetime distributions are discrete, so models cannot be generalized. To address above problems, A transferable RUL prediction method for lithium-ion batteries is proposed. Firstly, an Encoding Net that can extract global degradation tendency information is constructed to map the battery degradation data into the representation subspace. Cycle-consistency learning is used to train the Encoding Net so that points with the same degradation level between different batteries are aligned with each other. Based on aligned features, a new RUL prediction method, consisting of optimal decision of alignment ratio and integrated prediction using Bayesian model averaging (BMA), is established. The experimental results show that the proposed method can achieve high prediction accuracy and also provide a new perspective for RUL prediction. • We proposed a transferable RUL prediction method based on degradation alignment. • Cycle-consistency learning is used to achieve degradation alignment. • Global degradation tendency is considered to encode. • Our method can accurately predict RUL under multiple working conditions. • Two datasets are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the major malignancies that threaten men's health all over the world. Due to the lack of specific symptoms and signs in the early stage, as well as the limitations of existing detection methods, it is difficult to achieve early diagnosis for prostate cancer. As short single-stranded oligonucleotides (DNA or RNA) with specific 3D structure which can be produced using an in vitro selection process termed systematic evolution of ligands by exponential enrichment (SELEX), aptamers can specifically bind to the corresponding targets. They have become a class of novel targeting ligand for accurate diagnosis and effective treatment of cancer. Owing to distinctive physicochemical features, and some other special properties such as easy modifiability, good biocompatibility, being easily coupled with other ligands, nanomaterials are extensively used in biological medical field research. Enlighteningly, the combination of aptamers with nanomaterials, including metal nanoparticles, nanosilica, quantum dots, and carbon nanomaterials, can enhance the ability of nanomaterials to recognize tumor cells, which is beneficial to overcome the shortcomings such as low sensitivity in early detection and lack of specificity of traditional antineoplastic drugs, thus, clinically helpful to improve the early metaphase diagnosis rate, providing a technical guarantee for the "personalized treatment" strategy for prostate cancer. Herein, we mainly review the basic and applied research of aptamer functionalized nanocomposite in prostate cancer diagnosis and treatment, including biosensing, bioimaging, and cancer therapy, hoping to provide new ideas for prostate cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Excitability is a pivotal quality in guide dogs because moderately active dogs are more trainable. Excessive activity is associated with behavioral problems and pet surrender. Excitability is a highly heritable trait, yet the relevant genetic factors and markers associated with this condition are poorly characterized. In the present study, we selected six single nucleotide polymorphisms (SNPs) of two genes that are possibly related to excitability in dogs (TH c.264G > A, TH c.1208A > T, TH c.415C > G, TH c.168C > T, TH c.180C > T and MAOB c.199 T > C). We measured the excitability of dogs using seven variables from three behavioral tests: the play test (interest in play, grabbing in throw and tug‐of‐war), the chase test (following and forward grabbing) and the passive test (moving range and moving time). These behavioral tests are part of the Dog Mentality Assessment developed by Svartberg & Forkman. The activity scores in the guide dog group were higher than in the temperament withdrawal group, and significant differences were detected in the aggregate score (p = 0.02), passive activity score (p = 0.007) and moving range score (p = 0.04). Analysis using the Kruskal–Wallis test and non‐parametric Steel–Dwass test to evaluate the relationship between these SNPs and behavioral variable scores revealed that TH c.264G > A was associated with aggregate scores of excitability‐related behavioral variables (adj. p = 0.03), object‐interaction activity scores (adj. p = 0.03), following scores (adj. p = 0.03) and forward grabbing scores (adj. p = 0.03) in Labrador dogs and MAOB c.199 T > C was associated with moving range scores in these dogs (adj. p = 0.004). However, these results had low power. To explain the behavioral traits, further genetic studies more reliable than candidate gene studies are needed. [ABSTRACT FROM AUTHOR]

• Arbitrary order alternated optical vortex (OV) lattices with positive and negative topological charges, which are named as the n th-order alternated OV lattices (NAOVLs), are proposed theoretically and realized experimentally. • The propagation properties of NAOVLs are explored both theoretically and experimentally in a focusing system or at far-field Fraunhofer region. • Such NAOVLs can generate finite square optical arrays with interesting patterns or defects, and the phase dislocation lines contained in square optical arrays are confirmed by interference experiments. Recently optical lattices containing optical vortices (OVs), which carry phase singularities and possess exotic optical properties, have attracted increasing attention due to their potential applications in optical trapping or light-matter interaction systems. Here, we propose a kind of arbitrary order alternated OV lattices with positive and negative topological charges ± n , which are named as the n th-order alternated OV lattices (NAOVLs), and we experimentally demonstrate their propagation properties. We find that such NAOVLs can generate finite square optical arrays with interesting patterns or defects near the focal plane or far-field Fraunhofer region. We further find a general law (although with some exceptions) about the number N of bright spots in finite optical arrays as the value n increases. The interference experiments confirm optical properties of such finite arrays close to the focal plane. Our results may have potential applications in particle trapping and experimental realizations of novel optical arrays for the interaction between special optical lattices and atoms (or molecules, ions, micro- or nano-particles). [ABSTRACT FROM AUTHOR]

Magnetic nanomaterials have recently emerged, playing an increasingly essential role in life science and biomedicine fields, and they exhibited promising potentials in cancer diagnosis and therapy. Also, their use as contrast agents improved various cancer diagnostic imaging sensitivities and accuracy. Magnetic nanomaterials are also exploited as targeted drug carriers to increase the sensitivity and reduce the side effects of chemotherapeutic drugs. Herein, we reviewed the preparation, characterization, and surface modification of various magnetic nanomaterials and their cancer diagnosis and therapy applications. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the most common liver cancer and is highly malignant. Male prevalence and frequent activation of the Ras signaling pathway are distinct characteristics of HCC. However, the underlying mechanisms remain to be elucidated. By exploring Hras12V transgenic mice showing male-biased hepatocarcinogenesis, we performed a high-throughput comparative proteomic analysis based on tandem-mass-tag (TMT) labeling combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) on the tissue samples obtained from HCC (T) and their paired adjacent precancerous (P) of Hras12V transgenic male and female mice (Ras-Tg) and normal liver (W) of wild-type male and female mice (Non-Tg). The further validation and investigation were performed using quantitative real-time PCR and western blot. Totally, 5193 proteins were quantified, originating from 5733 identified proteins. Finally, 1344 differentially expressed proteins (DEPs) (quantified in all examined samples; |ratios| ≥ 1.5, p < 0.05) were selected for further analysis. Comparison within W, P, and T of males and females indicated that the number of DEPs in males was much higher than that in females. Bioinformatics analyses showed the common and unique cluster-enriched items between sexes, indicating the common and gender-disparate pathways towards HCC. Expression change pattern analysis revealed HCC positive/negative-correlated and ras oncogene positive/negative-correlated DEPs and pathways. In addition, it showed that the ras oncogene gradually and significantly reduced the responses to sex hormones from hepatocytes to hepatoma cells and therefore shrunk the gender disparity between males and females, which may contribute to the cause of the loss of HCC clinical responses to the therapeutic approaches targeting sex hormone pathways. Additionally, gender disparity in the expression levels of key enzymes involved in retinol metabolism and terpenoid backbone/steroid biosynthesis pathways may contribute to male prevalence in hepatocarcinogenesis. Further, the biomarkers, SAA2, Orm2, and Serpina1e, may be sex differences. In conclusion, common and unique DEPs and pathways toward HCC initiated by ras oncogene from sexually dimorphic hepatocytes provide valuable and novel insights into clinical investigation and practice. [ABSTRACT FROM AUTHOR]

Deficiency of γ-glutamyl carboxylation of coagulation factors, as evidenced by the elevated level of Des-γ-carboxyl prothrombin (DCP), is a common feature in hepatocellular carcinoma patients. Additionally, treatment of cancer patients with mTOR inhibitors significantly increases hemorrhagic events. However, the underlying mechanisms remain unknown. In the present study, Vitamin K epoxide reductase complex subunit 1 (VKORC1) was found to be significantly down-regulated in clinical hepatoma tissues and most tested hepatoma cell lines. In vitro investigations showed that VKORC1 expression was promoted by p-mTOR at the translational level and repressed by p-ERK at the transcriptional level. By exploring Hras12V transgenic mice, a hepatic tumor model, VKROC1 was significantly down-regulated in hepatic tumors and showed prolonged activated partial prothrombin time (APTT). In vivo investigations further showed that VKORC1 expression was promoted by p-mTOR and repressed by p-ERK in both hepatoma and hepatocytes. Consistently, APTT and prothrombin time were significantly prolonged under the mTOR inhibitor treatment and significantly shortened under the ERK inhibitor treatment. Conclusively, these findings indicate that mTOR and ERK play crucial roles in controlling VKORC1 expression in both hepatoma and hepatocytes, which provides a valuable molecular basis for preventing hemorrhage in clinical therapies. [ABSTRACT FROM AUTHOR]

Activation of the Ras/ MAPK pathway is prevalently involved in the occurrence and development of hepatocellular carcinoma ( HCC). However, its effects on the deregulated cellular metabolic processes involved in HCC in vivo remain unknown. In this study, a mouse model of HCC induced by hepatocyte-specific expression of the Hras12V oncogene was investigated using an integrative analysis of metabolomics and transcriptomics data. Consistent with the phenotype of abundant lipid droplets in HCC, the lipid biosynthesis in HCC was significantly enhanced by (1) a sufficient supply of acetyl-CoA from enhanced glycolysis and citrate shuttle activity; (2) a sufficient supply of NADPH from enhanced pentose phosphate pathway ( PPP) activity; (3) upregulation of key enzymes associated with lipid biosynthesis; and (4) downregulation of key enzymes associated with bile acid biosynthesis. In addition, glutathione ( GSH) was significantly elevated, which may result from a sufficient supply of 5-oxoproline and L-glutamate as well as an enhanced reduction in the process of GSSG being turned into GSH by NADPH. The high level of GSH along with elevated Bcl2 and Ucp2 expression may contribute to a normal level of reactive oxygen species ( ROS) in HCC. In conclusion, our results suggest that the lipid metabolism, glycolysis, PPP, tricarboxylic acid ( TCA) cycle, citrate shuttle activity, bile acid synthesis, and redox homeostasis in the HCC induced by ras oncogene are significantly perturbed, and these altered metabolic processes may play crucial roles in the carcinogenesis, development, and pathological characteristics of HCC. [ABSTRACT FROM AUTHOR]