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Abstract Background Breast cancer is the most commonly diagnosed cancer worldwide. Although major treatments represented by chemotherapy have shown effectiveness at the initial period, recurrence and metastasis still occur later after treatments. The alternation of the tumor microenvironment by chemotherapy is confirmed as a trigger of the elevated proliferation and migration of the remaining tumor cells. Methods Using bioinformatic methods, differential gene expression analysis was used to determine DEGs between post-chemotherapy and pre-chemotherapy samples of breast cancer patients, followed by survival analysis and ELISA analysis of the potential key genes. An in vitro model of 2 breast cancer cells lines was used to demonstrate the role of VWF in the evasion and migration of breast cancer cells, using cell migration, evasion and wound healing assays, PCR and molecular docking analysis. Results 19 hub genes were further identified using GO and KEGG pathway analyses and WGCNA. The 5 secreted protein-coding genes with reported carcinogenesis effects (VWF, SVEP1, DPT, ADIPOQ, and LPL) were further analyzed in breast cancer patients and VWF was identified as a potential key regulator in the anthracycline-based chemotherapy-exacerbated metastasis. It was further confirmed that anthracycline-based chemotherapeutics doxorubicin exacerbated VWF upregulation and the evasion and migration of breast cancer cells. Based on molecular docking analysis and previous study, berberine was used as an inhibitor of VWF, and showed an effective inhibition of the doxorubicin-exacerbated VWF upregulation, migration and evasion in breast cancer. Conclusions Doxorubicin-exacerbated evasion and migration through VWF upregulation. Berberine as an inhibitor of VWF was able to reversed the doxorubicin-exacerbated VWF upregulation and evasion and migration in breast cancer cells.

With the opening of industrial networks in the information age, the characteristic of Industrial Control Protocols (ICPs) to transmit plaintext without encryption exposes serious security risks, threatening the safe and stable operation of Industrial Control Systems (ICSs). Exploring the work of mining vulnerabilities in ICPs can use fuzzing to mine potential vulnerabilities in protocols to ensure the safe operation of ICS. However, traditional fuzzing methods require the construction of test cases based on expert experience and the format syntax specification of ICPs. This process is time-consuming, labor-intensive, inefficient, and limited when facing unknown ICPs. In response to these issues, this paper proposes an automated fuzzing method for ICPs based on the Denoising Diffusion Probabilistic Model (DDPM). Specifically, DDPM achieves the conversion from noise to data samples, which can easily and quickly generate test cases. On this basis, we designed a universal fuzzing framework, DiffusionFuzz, that can be applied to most ICPs. The experimental results obtained on ICPs such as Modbus/TCP in the Industrial Attack-Defense Range of the Key Laboratory of Information Security for Petrochemical Industry in Liaoning Province demonstrate that the test cases generated by this method are diverse, and outperform other fuzzing methods in terms of acceptance rate and ability to trigger exceptions. Certainly, DiffusionFuzz can enhance the effectiveness of fuzzing, identify vulnerabilities in ICPs, and thereby reduce potential economic risks and impacts.

Network intrusion detection technology has always been an indispensable protection mechanism for industrial network security. The rise of new forms of network attacks has resulted in a heightened demand for these technologies. Nevertheless, the current models’ effectiveness is subpar. We propose a new Deformable Vision Transformer (DE-VIT) method to address this issue. DE-VIT introduces a new deformable attention mechanism module, where the positions of key-value pairs in the attention mechanism are selected in a data-dependent manner, allowing it to focus on relevant areas, capture more informative features, and avoid excessive memory and computational costs. In addition to using deformable convolutions instead of regular convolutions in embedding layers to enhance the receptive field of patches, a sliding window mechanism is also employed to utilize edge information fully. In Parallel, we use a layered focal loss function to improve classification performance and address data imbalance issues. In summary, DE-VIT reduces computational complexity and achieves better results. We conduct experimental simulations on the public intrusion detection datasets, and the accuracy of the enhanced intrusion detection model surpasses that of the Deep Belief Network with Improved Kernel-Based Extreme Learning (DBN-KELM). It reaches 99.5% and 97.5% on the CIC IDS2017 and UNSW-NB15 datasets, exhibiting an increase of 8.5% and 9.1%, respectively.

Intensively farmed animals such as pigs inevitably experience a certain degree of psychological stress, which leads to a reduction in production performance. Mental health traits are currently difficult to measure, resulting in a gap in understanding their genetic basis. To address this challenge, we propose a computational framework called mental health of animals (MeHA), capable of revealing genes related to animal mental health traits. Using MeHA, we identified 109 candidate genes associated with pig mental health and discovered their intricate connections with critical functions, such as memory, cognition, and neural development, which are essential components of mental health and cognitive performance. Importantly, our findings provide evidence of the potential impact of these genes on economically important traits, including meat quality and piglet survival. This research underscores the importance of genetic studies in enhancing our understanding of animal behavior and cognition, as well as promoting agricultural practices. By applying our approach to study the genetic basis of mental health in pigs as a case, we confirmed that our framework is an effective way to reveal genetic factors affecting animal mental health traits, which contributes to animal welfare and has potential implications for understanding human mental disorders.

Background: JianPiYiFei (JPYF) II granules are a Chinese medicine for the treatment of chronic obstructive pulmonary disease (COPD). However, the main components and underlying mechanisms of JPYF II granules are not well understood. This study aimed to elucidate the potential mechanism of JPYF II granules in the treatment of COPD using network pharmacology, molecular docking, and molecular dynamics simulation techniques. Methods: The active compounds and corresponding protein targets of the JPYF II granules were found using the TCMSP, ETCM, and Uniport databases, and a compound–target network was constructed using Cytoscape3.9.1. The COPD targets were searched for in GEO datasets and the OMIM and GeneCards databases. The intersection between the effective compound-related targets and disease-related targets was obtained, PPI networks were constructed, and GO and KEGG enrichment analyses were performed. Then, molecular docking analysis verified the results obtained using network pharmacology. Finally, the protein–compound complexes obtained from the molecular docking analysis were simulated using molecular dynamics (MD) simulations. Results: The network pharmacological results showed that quercetin, kaempferol, and stigmasterol are the main active compounds in JPYF II granules, and AKT1, IL-6, and TNF are key target proteins. The PI3K/AKT signaling pathway is a potential pathway through which the JPYF II granules affect COPD. The results of the molecular docking analysis suggested that quercetin, kaempferol, and stigmasterol have a good binding affinity with AKT1, IL-6, and TNF. The MD simulation results showed that TNF has a good binding affinity with the compounds. Conclusions: This study identified the effective compounds, targets, and related underlying molecular mechanisms of JPYF II granules in the treatment of COPD through network pharmacology, molecular docking, and MD simulation techniques, which provides a reference for subsequent research on the treatment of COPD.

Curcumin and exercise have been reported to show good anti-tumour effects. However, relevant research on the combined effects of physical exercise and curcumin supplementation on cancer and the underlying mechanisms is still lacking. The current study aimed to construct an anti-breast tumour mouse model using the combined effects of curcumin treatment and swimming exercise. Transcriptomic and metabolomic techniques were used to screen for differentially expressed genes and metabolites, evaluate the anticancer effects, and analyse the molecular regulatory mechanisms related to metabolism. Observation of the mouse phenotypes, including tumour appearance, in-vivo tumour imaging, and HE staining results of pathological sections, suggested a more obvious inhibitory effect of the combination of curcumin administration and exercise intervention on breast cancer than that of a single treatment. The combination treatment group had a total of 445 differentially expressed (154 upregulated and 291 downregulated) genes. Functional enrichment analysis showed the calcium signalling pathway, Wnt signalling pathway, PI3K Akt signalling pathway, and IL-17 signalling pathway to significantly participate in the anti-breast cancer process of curcumin-exercise combination treatment. Results of the intergroup differential metabolite analysis showed that the combined effect of curcumin and exercise involves two unique pathways, namely the amino sugar and nucleotide sugar metabolism, which includes chitosan, d-glucosamine 6-phosphate, l-fucose, and N-acetyl beta-mannosamine, and the amino acid biosynthesis, which includes dl-isoleucine, dl-tyrosine, and homocysteine. Collectively, the top-ranked genes and metabolites with the highest degree of associations were further revealed by O2PLS analysis. Overall, the study helped reveal the mechanism of action of curcumin-exercise combination treatment on breast cancer at multi-omics level.

The therapeutic mechanism of oleanolic acid (OA) in breast cancer has been widely reported, but little has been known about the combined effects of transcriptome and gut microbiome. In this study, the phenotypic effect of oleanolic acid on mice was tested at the end of the administration cycle, and RNA sequencing on murine tumor tissue and 16S-rRNA sequencing on intestinal contents were conducted to analyze gene expression profiles and microbial diversity between the control group and OA treated group using 4T1-induced mice breast cancer model. As a result, it has been confirmed that oleanolic acid would play a significant inhibitory effect on the development of breast tumors in mice. Based on the integrative analysis of the transcriptomic and metagenomic data, it was found that the abundance of Lactobacillus in the intestinal flora of mice significantly increased in the OA group. Moreover, the up-regulation of Il10 had a significant effect on inhibiting the tumor progression, which played a role through cytokine-cytokine receptor interaction pathway.

We propose a quantification CνGn of n-mode Gaussian coherence. The value of CνGn only depends on the covariance matrices and displacement vectors of continuous-variable states without any optimization procedures, and thus is easily calculated. For n=1, CνG1 is a proper Gaussian coherence measure of single-mode CV system. For n≥2, CνGn is invariant under any permutation of submodes, nonincreasing under any n-mode local incoherent Gaussian channels, vanishes at incoherent Gaussian states, and, satisfies the unification condition and the hierarchy condition that a multi-partite quantum correlation measure should obey. Thus CνGn is a multi-partite Gaussian correlation measure, which reveals, though the quantum coherence lives in single-partite systems, that the multi-mode coherence for continuous-variable systems can be regarded as a multipartite Gaussian correlation between modes, and such multi-partite Gaussian correlation is also a quantum resource. Moreover, we show that CνGn is completely monogamous as a multipartite Gaussian correlation measure. This means that the n-mode Gaussian coherence subjects to the principles of resource allocation. In addition, CνGn is an upper bound of the geometric-based single-mode Gaussian coherence measure CBu by the Bures distance at pure Gaussian states of mode ≤2 and can be used to detect coherence in any n-mode Gaussian states more efficiently.

This study aimed to analyze the effect of lipid peroxidation on the allergenicity and functional properties of soybean β-conglycinin (7S) and glycinin (11S). Oxidation complexes were determined using the lipid peroxidation method. Functional properties were analyzed based on emulsifying and foaming properties. The potential allergenicity was evaluated by in vitro and in vivo methods. The results found that oxidation altered structures of the proteins and resulted in the formation of cross-linked protein polymers. The emulsion and foaming properties of the proteins were improved after oxidation. The IgE-binding capacity of 7S and 11S reduced after oxidation. KU812 cell assays showed that both histamine and IL-4 release decreased after oxidation treatment. A mouse model showed that oxidation reduced the IgE, IgG, and IgG1 levels, as well as reduced histamine and mMCP-1 release in serum, which might suppress the allergic reaction. In conclusion, the lipid peroxidation treatment likely causes changes to the functional properties of soybean, decreasing the potential allergenicity of 7S and 11S.

Patient-derived tumor xenografts (PDXs) are a powerful tool for drug discovery and screening in cancer. However, current studies have led to little understanding of genotype mismatches in PDXs, leading to massive economic losses. Here, we established PDX models from 53 lung cancer patients with a genotype matching rate of 79.2% (42/53). Furthermore, 17 clinicopathological features were examined and input in stepwise logistic regression (LR) models based on the lowest Akaike information criterion (AIC), least absolute shrinkage and selection operator (LASSO)-LR, support vector machine (SVM) recursive feature elimination (SVM-RFE), extreme gradient boosting (XGBoost), gradient boosting and categorical features (CatBoost), and the synthetic minority oversampling technique (SMOTE). Finally, the performance of all models was evaluated by the accuracy, area under the receiver operating characteristic curve (AUC), and F1 score in 100 testing groups. Two multivariable LR models revealed that age, number of driver gene mutations, epidermal growth factor receptor (EGFR) gene mutations, type of prior chemotherapy, prior tyrosine kinase inhibitor (TKI) therapy, and the source of the sample were powerful predictors. Moreover, CatBoost (mean accuracy = 0.960; mean AUC = 0.939; mean F1 score = 0.908) and the eight-feature SVM-RFE (mean accuracy = 0.950; mean AUC = 0.934; mean F1 score = 0.903) showed the best performance among the algorithms. Meanwhile, application of the SMOTE improved the predictive capability of most models, except CatBoost. Based on the SMOTE, the ensemble classifier of single models achieved the highest accuracy (mean = 0.975), AUC (mean = 0.949), and F1 score (mean = 0.938). In conclusion, we established an optimal predictive model to screen lung cancer patients for non-obese diabetic (NOD)/Shi-scid, interleukin-2 receptor (IL-2R) γnull (NOG)/PDX models and offer a general approach for building predictive models.

In this paper, we establish an aggregate class distribution neural network (AGGNN) structure to determine whether an arbitrary two-qubit quantum state is steerable. Compared to the classification results obtained using a support vector machine (SVM) and a backpropagation neural network (BPNN), we obtain higher-accuracy quantum-steering classification models via the AGGNN, as well as steerability bounds of generalized Werner states, which are more similar to the theoretical bounds. In particular, when we only know partial information about the quantum states, higher-performance quantum-steering classifiers are obtained compared to those via SVM and BPNN.

The security of industrial control systems relies on the communication and data exchange capabilities provided by industrial control protocols, which can be complex, and may even use encryption. Reverse engineering these protocols has become an important topic in industrial security research. In this paper, we present PREIUD, a reverse engineering tool for industrial control protocols, based on unsupervised learning and deep neural network methods. The reverse process is divided into stages. First, we use the bootstrap voting expert algorithm to infer the keyword segment boundaries of the protocols, considering the symmetry properties. Then, we employ a bidirectional long short-term memory conditional random field with an attention mechanism to classify the protocols and extract their format and semantic features. We manually constructed data sample sets for six commonly used industrial protocols, and used them to train and test our model, comparing its performance to two advanced protocol reverse tools, MSERA and Discoverer. Our results showed that PREIUD achieved an average accuracy improvement of 7.4% compared to MSERA, and 15.4% compared to Discoverer, while also maintaining a balance between computational conciseness and efficiency. Our approach represents a significant advancement in the field of industrial control protocol reverse engineering, and we believe it has practical implications for securing industrial control systems.

Abstract Background The detection of Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network (ResNet) to estimate the KRAS mutation status in CRC patients based on pre-treatment contrast-enhanced CT imaging. Methods We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were divided into a training cohort (n = 117) and a testing cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and evaluated the model in the testing cohort. Several groups of expended region of interest (ROI) patches were generated for the ResNet model, to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. Results The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the testing cohort and peaked at 0.93 with an input of ’ROI and 20-pixel’ surrounding area. AUC of radiomics model in testing cohorts were 0.818. In comparison, the ResNet model showed better predictive ability. Conclusions Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation.

As part of the United States Pharmacopeia’s ongoing review of dietary supplement safety data, a new comprehensive systematic review on green tea extracts (GTE) has been completed. GTEs may contain hepatotoxic solvent residues, pesticide residues, pyrrolizidine alkaloids and elemental impurities, but no evidence of their involvement in GTE-induced liver injury was found during this review. GTE catechin profiles vary significantly with manufacturing processes. Animal and human data indicate that repeated oral administration of bolus doses of GTE during fasting significantly increases bioavailability of catechins, specifically EGCG, possibly involving saturation of first-pass elimination mechanisms. Toxicological studies show a hepatocellular pattern of liver injury. Published adverse event case reports associate hepatotoxicity with EGCG intake amounts from 140 mg to ∼1000 mg/day and substantial inter-individual variability in susceptibility, possibly due to genetic factors. Based on these findings, USP included a cautionary labeling requirement in its Powdered Decaffeinated Green Tea Extract monograph that reads as follows: “Do not take on an empty stomach. Take with food. Do not use if you have a liver problem and discontinue use and consult a healthcare practitioner if you develop symptoms of liver trouble, such as abdominal pain, dark urine, or jaundice (yellowing of the skin or eyes).”

Due to the irregularity of colorectal tumor contours, it is a challenging task for clinicians to segment colorectal tumors manually in CT scans. To solve this problem, a novel algorithm based on a convolutional neural network with an attention mechanism is proposed to automatically achieve tumor segmentation. The proposed network consists of three major modules: an encoder module, which is fed CT scans to attain the feature map; a dual attention module, which includes a channel attention module and a position attention module to obtain more contextual information in the deep layer of the network; and a decoder module, which restores the feature map to the original size of the input images. We used 1131 CT slices of colorectal tumors to train and test the proposed network. Compared with U-Net and CE-Net, the Dice coefficient increased by 1.46% and 0.66% respectively, for our model. The comprehensive results show that the proposed network performs more effectively in colorectal tumor segmentation than the other methods.

Ovalbumin (OVA) is one of the major allergenic proteins in egg that contain numerous functional properties. Here, oxidation-induced changes to the structure, functional properties, and allergenicity of OVA were evaluated after 2,2′-azobis (2-amidinopropane) dihydrochloride (AAPH) and acrolein treatment. Oxidation complexes were prepared by the lipid peroxidation, which caused structural changes, and increased digestibility, foaming, and emulsifying properties. The IgE-binding capacity of OVA decreased after oxidation. KU812 cell assay showed that the histamine and IL-4 levels decreased upon oxidation with AAPH and acrolein. A mouse model study showed that oxidation caused a reduction in the allergenicity by reducing IgE and IgG1 levels and the production of Th2 cytokines after acrolein (1 mmol/L) and AAPH (5 mmol/L) treatment. Thus, OVA could cause changes to structural and functional properties, modulating Th1/Th2 immunobalance, and reducing potential allergenicity after oxidation by lipid peroxidation, which might provide an effective and potential method to process hypoallergenic OVA.

Background: Diabetes is a complex and widespread disease, and type 2 diabetes mellitus (T2DM) diagnoses continue to climb throughout the world. Traditional herbal medicine is used for many health conditions, and Momordica charantia is the most commonly used plant around the world in treating diabetes. Hypothesis: We hypothesized that the well-established hypoglycemic activity of M. charantia is due to the saponin-induced stimulation of insulin secretion, resulting in normalization of glucose metabolism. In addition, we investigate whether saponins support the proliferation of pancreatic β-cells.Study Design and Methods: To test this, C57BLK/6 mice were fed a low or high fat diet. Either a crude extract or saponin rich fraction (SRF) of M. charantia or vehicle was orally administered daily for 4 weeks. Results: The SRF lowered fasting glucose concentrations, and both the crude extract and SRF improved glucose tolerance. The SRF did not impact β-cell mass, when compared to other treatment groups. Conclusions: In conclusion, our study suggests that saponins are responsible for all, or part, of the hypoglycemic effect and insulin secretory activity reported in M. charantia fruit.

Background: Comorbid somatic diseases increase the death risk and affect the condition, treatment, and prognosis of older psychiatric patients. We investigated the comorbidity and drug treatment in older patients with psychosis.Methods: This retrospective study used data from 3,115 older psychiatric in-patients hospitalized at the Shanghai Mental Health Center Affiliated to Shanghai Jiaotong University School of Medicine, China discharged from 2005 to 2015. Descriptive analyses of patients' age, sex, treatment drugs, diagnoses (based on ICD-10), and time trend were performed.Results: Patients' median age was 56 (range, 50-98) years; 1,824 (58.6%) were female. The top five first-level diagnoses were schizophrenia (F20) (n = 1,818, 58.3%), depressive episode (F32) (n = 457, 14.6%), bipolar affective disorder (F31) (n = 151, 4.8%), manic episode (F30), (n = 143, 4.6%), and vascular dementia (F01) (n = 136, 4.4%). Mental (99.9%), central nervous system (85.2%), digestive system (83.5%), cardiovascular system (72.5%), and anti-infective (59.6%) drugs had the highest prescription rates. The combined use of antidepressants, anti-anxiety, anti-arrhythmic, hormones and endocrine system drugs were significantly higher in female than in male patients, while mood stabilizers and genitourinary system drugs significantly more frequent in men. With increasing age, the F20-F29 patients decreased, while F00-F09 patients increased, with the corresponding changes to prescription in those patients. In comparison to that in 2005-2010, the combined prescriptions for genitourinary and cardiovascular drugs increased between 2011 and 2015, and F00-F09 and F40-F48 older patients doubled, accordingly anti-Alzheimer's disease drugs and antidepressants more than doubled. F30-F39 patients increased by 49.1%, and anti-anxiety drugs, mood stabilizers, etc. increased by ≥50%; F20-F29 older patients decreased by 26.7%, while antipsychotics only increased by 4.4%.Conclusions: This study found the combined drug treatment of somatic diseases, particularly for central nervous, digestive, cardiovascular, respiratory and genitourinary drugs were extremely common among older psychiatric in-patients in China. With increasing age, the F20-F29 patients decreased, while F00-F09 patients increased; the antipsychotics prescriptions decreased, and almost all comorbidity drugs increased. Compared with that in 2005-2010, the older patients with all diagnosis except F20-F29 increased in 2011-2015, and the prescriptions for psychotropic, genitourinary, and cardiovascular drugs increased.

DNA damage response and repair (DDR) gene alterations increase tumor-infiltrating lymphocytes, genomic instability, and tumor mutational burden (TMB). Whether DDR-related alterations relate to therapeutic response and prognosis in lung cancer lacking oncogenic drivers remains unknown. Pretherapeutic cancer samples of 122 patients [86 non-small cell lung cancer and 36 small cell lung cancer (SCLC)] harboring no EGFR/ALK alterations were collected. Through whole-exome sequencing, we outlined DDR mutational landscape and determined relationships between DDR gene alterations and TMB or intratumoral heterogeneity. Then, we evaluated the impacts of DDR gene alterations on therapeutic response and prognosis and established a DDR-based model for prognosis prediction. In addition, we investigated somatic interactions of DDR genes and immunomodulatory genes, immune expression patterns, immune microenvironment, and immune infiltration characteristics between DDR-deficient and DDR-proficient samples. Samples from cBioportal datasets were utilized for verification. We found that deleterious DDR gene alterations were closely associated with higher TMB than proficient-types (p < 0.001). DDR mechanisms attach great importance to the determination of patients’ prognosis after chemotherapy, and alterations of base excision repair pathway in adenocarcinoma, nucleotide excision repair in squamous carcinoma, and homologous recombination pathway in SCLC tend to associate with worse progression-free survival to first-line chemotherapy (all p < 0.05). A predictive nomogram model was constructed incorporating DDR-related alterations, clinical stage, and smoking status, with the area under curve values of 0.692–0.789 for 1- and 2-year receiver operating characteristic curves in training and testing cohorts. Furthermore, DDR-altered tumors contained enhanced frequencies of alterations in various genes of human leukocyte antigen (HLA) class I pathway including TAP1 and TAP2 than DDR-proficient samples. DDR-deficient types had lower expressions of STING1 (p = 0.01), CD28 (p = 0.020), HLA-DRB6 (p = 0.014) in adenocarcinoma, lower TNFRSF4 (p = 0.017), and TGFB1 expressions (p = 0.033) in squamous carcinoma, and higher CD40 (p = 0.012) and TNFRSF14 expressions (p = 0.022) in SCLC. DDR alteration enhanced activated mast cells in adenocarcinoma (p = 0.044) and M2 macrophage in squamous carcinoma (p = 0.004) than DDR-proficient types. Collectively, DDR gene alterations in lung cancer without oncogenic drivers are positively associated with high TMB. Specific DDR gene alterations tend to associate with worse progression-free survival to initial chemotherapy.

In this article, we introduce a framework for two-qutrit Werner states tomography with Gaussian noise. The measurement scheme is based on the symmetric, informationally complete positive operator-valued measure. To make the framework realistic, we impose the Gaussian noise on the measured states numbers. Through numerical simulation, we successfully reconstructed the two-qutrit Werner states in various experimental scenarios and analyzed the optimal scenario from four aspects: fidelity, purity, entanglement, and coherence.

ObjectiveLiver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment.Materials and Methods252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation.ResultsOn the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors >0.5cm3 performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89.ConclusionThe proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

Abstract Evaluating the therapeutic response and survival of lung cancer patients receiving first‐line chemotherapy has always been difficult. Limited biomarkers for evaluation exist and as a result histology represents an empiric tool to guide therapeutic decision making. In this study, molecular signatures associated with response and long‐term survival of lung cancer patients receiving first‐line chemotherapy are discovered. Whole‐exome sequencing is performed on pretherapeutic tissue samples of 186 patients [145 non‐small cell lung cancer (NSCLC) and 41 small cell lung cancer (SCLC)]. On the basis of genomic alteration characteristics, NSCLC patients can be classified into four subtypes (C1–C4). The long‐term survival is similar among different subtypes. SCLC patients are also divided into four subtypes and significant difference in their progression free survival is revealed (P < 0.001). NSCLC patients can be divided into three subtypes (S1–S3) based on TMB. A trend of worse survival associated with higher TMB in subtype S3 than in S1+S2 is found. In contrast, no significant correlations between molecular subtype and therapeutic response are observed. In conclusion, this study identifies several molecular signatures associated with response and survival to first‐line chemotherapy in lung cancer.

ObjectiveTo develop and validate a multiregional-based magnetic resonance imaging (MRI) radiomics model and combine it with clinical data for individual preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.Methods186 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 123) and testing cohorts (n = 63). Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Five support vector machine (SVM) classification models were built using selected clinical and semantic variables, single-regional radiomics features, multiregional radiomics features, and combinations, for predicting LN metastasis in rectal cancer. The performance of the five SVM models was evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing cohort. Differences in the AUCs among the five models were compared using DeLong’s test.ResultsThe clinical, single-regional radiomics and multiregional radiomics models showed moderate predictive performance and diagnostic accuracy in predicting LN metastasis with an AUC of 0.725, 0.702, and 0.736, respectively. A model with improved performance was created by combining clinical data with single-regional radiomics features (AUC = 0.827, (95% CI, 0.711–0.911), P = 0.016). Incorporating clinical data with multiregional radiomics features also improved the performance (AUC = 0.832 (95% CI, 0.717–0.915), P = 0.015).ConclusionMultiregional-based MRI radiomics combined with clinical data can improve efficacy in predicting LN metastasis and could be a useful tool to guide surgical decision-making in patients with rectal cancer.

Objective: To explore a new predictive model of lymphatic vascular infiltration (LVI) in rectal cancer based on magnetic resonance (MR) and computed tomography (CT).Methods: A retrospective study was conducted on 94 patients with histologically confirmed rectal cancer, they were randomly divided into training cohort (n = 65) and validation cohort (n = 29). All patients underwent MR and CT examination within 2 weeks before treatment. On each slice of the tumor, we delineated the volume of interest on T2-weighted imaging, diffusion weighted imaging, and enhanced CT images, respectively. A total of 1,188 radiological features were extracted from each patient. Then, we used the student t-test or Mann–Whitney U-test, Spearman's rank correlation and least absolute shrinkage and selection operator (LASSO) algorithm to select the strongest features to establish a single and multimodal logic model for predicting LVI. Receiver operating characteristic (ROC) curves and calibration curves were plotted to determine how well they explored LVI prediction performance in the training and validation cohorts.Results: An optimal multi-mode radiology nomogram for LVI estimation was established, which had significant predictive power in training (AUC, 0.884; 95% CI, 0.803–0.964) and validation (AUC, 0.876; 95% CI, 0.721–1.000). Calibration curve and decision curve analysis showed that the multimodal radiomics model provides greater clinical benefits.Conclusion: Multimodal (MR/CT) radiomics models can serve as an effective visual prognostic tool for predicting LVI in rectal cancer. It demonstrated great potential of preoperative prediction to improve treatment decisions.

Hepatocellular carcinoma (HCC) is one of the most common human cancers, its prevalence and severity need us to discover novel early diagnostic biomarkers and new therapeutic strategies. MicroRNA-122 is the most abundant microRNA in the liver, and acts as a tumor suppressor and represses HCC development. In our study we showed that HNF-4α and MiR-122 were down-regulated significantly in hepatocellular carcinoma. Over-expression of HNF-4α inhibit hepatocellular carcinoma cells proliferation. And miR-122 is one of the downstream effector of HNF-4α. Up-regulated miR-122 inhibited hepatocellular carcinoma cells proliferation through regulating ADAM17. Collectively, our results suggested that HNF-4α could inhibit hepatocellular carcinoma proliferation with miR-122 being a downstream target of it. And miR-122 would inhibit hepatocellular carcinoma proliferation by regulating ADAM17 signal pathway.

Osteoarthritis is a common multifactorial chronic disease that occurs in articular cartilage, subchondral bone, and periarticular tissue. The pathogenesis of OA is still unclear. To investigate the differences in serum metabolites between OA and the control group, liquid chromatography/mass spectrometry (LC/MS)-based metabolomics was used. To reveal the pathogenesis of OA, 12 SD male rats were randomly divided into control and OA groups using collagenase to induce OA for modeling, and serum was collected 7 days after modeling for testing. The OA group was distinguished from the control group by principal component analysis and orthogonal partial least squares-discriminant analysis, and six biomarkers were finally identified. These biomarkers were metabolized through tryptophan metabolism, glutamate metabolism, nitrogen metabolism, spermidine metabolism, and fatty acid metabolism pathways. The study identified metabolites that may be altered in OA, suggesting a role in OA through relevant metabolic pathways. Metabolomics, as an important tool for studying disease mechanisms, provides useful information for studying the metabolic mechanisms of OA.

Mouse and rat embryonic stem cell (ESC) self-renewal can be maintained by dual inhibition of glycogen synthase kinase 3 (GSK3) and mitogen-activated protein kinase kinase (MEK). Inhibition of GSK3 promotes ESC self-renewal by abrogating T-cell factor 3 (TCF3)-mediated repression of the pluripotency network. How inhibition of MEK mediates ESC self-renewal, however, remains largely unknown. Here, we show that inhibition of MEK can significantly suppress lymphoid enhancer factor 1 (LEF1) expression in mouse ESCs. Knockdown or knockout of Lef1 partially mimics the self-renewal-promoting effect of MEK inhibitors. Moreover, depletion of both Tcf3 and Lef1 enables maintenance of undifferentiated mouse ESCs without exogenous factors, cytokines or inhibitors. Transcriptome resequencing analysis reveals that LEF1 is closely associated with endoderm specification in ESCs. Thus, our study adds support to the notion that the key to maintaining the ESC ground state is to shield ESCs from differentiative cues.

The aerodynamic performance of a high-speed train passing through tunnel junctions under severe crosswind condition was numerically investigated using improved delayed detached-eddy simulations (IDDES). Three ground scenarios connected with entrances and exits of tunnels were considered. In particular a flat ground, an embankment, and a bridge configuration were used. The numerical method was first validated against experimental data, showing good agreement. The results show that the ground scenario has a large effect on the train’s aerodynamic performance. The bridge case resulted in generally smaller drag and lift, as well as a lower pressure coefficient on both the train body and the inner tunnel wall, as compared to the tunnel junctions with flat ground and embankment. Furthermore, the bridge configuration contributed to the smallest pressure variation in time in the tunnel. Overall, the study gives important insights on complicated tunnel junction scenarios coupled with severe flow conditions, that, to the knowledge of the authors, were not studied before. Beside this, the results can be used for further improvements in the design of tunnels where such crosswind conditions may occur.

Abstract Coordination of cell division timing is crucial for proper cell fate specification and tissue growth. However, the differential regulation of cell division timing across or within cell types during metazoan development remains poorly understood. To elucidate the systems‐level genetic architecture coordinating division timing, we performed a high‐content screening for genes whose depletion produced a significant reduction in the asynchrony of division between sister cells (ADS) compared to that of wild‐type during Caenorhabditis elegans embryogenesis. We quantified division timing using 3D time‐lapse imaging followed by computer‐aided lineage analysis. A total of 822 genes were selected for perturbation based on their conservation and known roles in development. Surprisingly, we find that cell fate determinants are not only essential for establishing fate asymmetry, but also are imperative for setting the ADS regardless of cellular context, indicating a common genetic architecture used by both cellular processes. The fate determinants demonstrate either coupled or separate regulation between the two processes. The temporal coordination appears to facilitate cell migration during fate specification or tissue growth. Our quantitative dataset with cellular resolution provides a resource for future analyses of the genetic control of spatial and temporal coordination during metazoan development.

Cimicifuga foetida, an Asian Cimicifuga species, has been employed as a cooling and detoxification agent in traditional Chinese medicine since ancient times. For this herb, two cycloartane triterpene glycosides isolated from the rhizomes have demonstrated cytotoxicity on rat tumor and human cancer cell lines. Since human Hsp27 is increased in various human cancers and exhibits cytoprotective activity that affects tumorigenesis and the susceptibility of tumours to cancer treatment, the purpose of this research was to study the expression of this protein in MCF-7 breast cancer cells. To accomplish this aim, MCF-7 cells were exposed to different concentrations of Cimicifuga foetida extract showing a reduction in cell number measured by the sulforhodamine assay. In addition, the expression of Hsp-27 mRNA detected by RT-PCR and Hsp-27 protein detected by immnofluorescence was present in all conditions, except when using the highest concentration of Cimicifuga foetida extract (2,000 jig /L). We conclude that Hsp 27 expression at 2,000 jig /L Cimicifuga foetida extract is diminished. This is the first report showing the Hsp-27 expression after exposure to Cimicifuga foetida extract in MCF-7 cells.

Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and 'vesselness values' from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.

Let ℰ(H) be the Hilbert space effect algebra on a Hilbert space H with dim⁡H≥3, α,β two positive numbers with 2α+β≠1 and Φ:ℰ(H)→ℰ(H) a bijective map. We show that if Φ(AαBβAα)=Φ(A)αΦ(B)βΦ(A)α holds for all A,B∈ℰ(H), then there exists a unitary or an antiunitary operator U on H such that Φ(A)=UAU* for every A∈ℰ(H).

Let H be a complex Hilbert space; denote by Alg 𝒩 and 𝒞p(H) the atomic nest algebra associated with the atomic nest 𝒩 on H and the space of Schatten-p class operators on, H respectively. Let 𝒞p(H)∩Alg 𝒩 be the space of Schatten-p class operators in Alg 𝒩. When 1≤p

We give a characterization of trace-preserving and positive linear maps preserving trace distance partially, that is, preservers of trace distance of quantum states or pure states rather than all matrices. Applying such results, we give a characterization of quantum channels leaving Helstrom's measure of distinguishability of quantum states or pure states invariant and show that such quantum channels are fully reversible, which are unitary transformations.

Cyclin A-Cdk2, a cell cycle regulated Ser/Thr kinase, plays important roles in a variety of apoptoticprocesses. However, the mechanism of cyclin A-Cdk2 regulated apoptosis remains unclear. Here, we demonstrated that Rad9, a member of the BH3-only subfamily of Bcl-2 proteins, could be phosphorylated by cyclin A-Cdk2 in vitro and in vivo. Cyclin A-Cdk2 catalyzed the phosphorylation of Rad9 at serine 328 in HeLa cells during apoptosis induced by etoposide, an inhibitor of topoisomeraseII. The phosphorylation of Rad9 resulted in its translocation from the nucleus to the mitochondria and its interaction with Bcl-xL. The forced activation of cyclin A-Cdk2 in these cells by the overexpression of cyclin A,triggered Rad9 phosphorylation at serine 328 and thereby promoted the interaction of Rad9 with Bcl-xL and the subsequent initiation of the apoptotic program. The pro-apoptotic effects regulated by the cyclin A-Cdk2 complex were significantly lower in cells transfected with Rad9S328A, an expression vector that encodes a Rad9 mutant that is resistant to cyclin A-Cdk2 phosphorylation. These findings suggest that cyclin A-Cdk2 regulates apoptosis through a mechanism that involves Rad9phosphorylation.

Gene expression profiling of PPARα has been used in several studies, but fewer studies went further to identify the tissue-specific pathways or genes involved in PPARα activation in genome-wide. Here, we employed and applied gene set enrichment analysis to two microarray datasets both PPARα related respectively in mouse liver and intestine. We suggested that the regulatory mechanism of PPARα activation by WY14643 in mouse small intestine is more complicated than in liver due to more involved pathways. Several pathways were cancer-related such as pancreatic cancer and small cell lung cancer, which indicated that PPARα may have an important role in prevention of cancer development. 12 PPARα dependent pathways and 4 PPARα independent pathways were identified highly common in both liver and intestine of mice. Most of them were metabolism related, such as fatty acid metabolism, tryptophan metabolism, pyruvate metabolism with regard to PPARα regulation but gluconeogenesis and propanoate metabolism independent of PPARα regulation. Keratan sulfate biosynthesis, the pathway of regulation of actin cytoskeleton, the pathways associated with prostate cancer and small cell lung cancer were not identified as hepatic PPARα independent but as WY14643 dependent ones in intestinal study. We also provided some novel hepatic tissue-specific marker genes.