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Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.

Abstract Background This study aimed to explore the efficacy of hookwire for computed tomography (CT)-guided pulmonary nodule (PN) localization before video-assisted thoracoscopic surgery (VATS) resection and determine the risk factors for localization-related complications. Methods We enrolled 193 patients who underwent preoperative CT-guided PN hookwire localization. The patients were categorized into groups A (103 patients had no complications) and B (90 patients had complications) according to CT and VATS. Uni- and multivariate logistic regression analyses were used to identify risk factors for localization-related complications. A numerical rating scale was used to evaluate hookwire localization-induced pain. Results We successfully performed localization in 173 (89.6%) patients. Pneumothorax was the main complication in 82 patients (42.5%). Patient gender, age, body mass index, tumor diameter, consolidation tumor ratio, pathologic diagnosis, position adjustment during location, lesion location, waiting time for surgery, and pleural adhesions were not significantly different between the two groups. The number of nodules, number of punctures, scapular rest position, and depth of insertion within the lung parenchyma were significant factors for successful localization. Multivariate regression analysis further validated the number of nodules, scapular rest position, and depth of insertion within the lung parenchyma as risk factors for hookwire-localization-related complications. Hookwire localization-induced pain is mainly mild or moderate pre- and postoperatively, and some patients still experience pain 7 days postoperatively. Conclusions Hookwire preoperative PN localization has a high success rate, but some complications remain. Thus, clinicians should be vigilant and look forward to further improvement.

This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners. Despite the promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, and interdisciplinary knowledge integration impede the interpretability, reliability, and applicability of AI-based DED detection methods. The research conducts a comprehensive review of datasets, diagnostic evidence, and standards, as well as advanced algorithms in AI-based DED detection over the past five years. The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques: (1) those with ground truth and/or comparable standards, (2) potential AI-based methods with significant advantages, and (3) supplementary methods for AI-based DED detection. The study proposes suggested DED detection standards, the combination of multiple diagnostic evidence, and future research directions to guide further investigations. Ultimately, the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations, advanced methods, challenges, and potential future perspectives, emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.

Plasma-Enhanced Chemical Vapor Deposition (PECVD) of amorphous silicon nitride (SiNx) thin films is a critical procedure in microelectronics serving as a surface passivation layer and dielectric barrier. However, intrinsic film stress continuously builds up along with PECVD growth, leading to film cracking. How to achieve crack-free PECVD amorphous SiNx film within a large thickness range remains a critical unresolved challenge in semiconductor industry. In this study, we revealed that high residual NH ligands from the NH3 precursor could induce excessive tensile strain at the SiNx/Si wafer interface and consequently aggravate SiNx film crack formation. With a heating pretreatment on the wafer, residual H ligands were effectively reduced to achieve homogenous chemical composition in SiNx film. As a result, the crack number declined ∼42% and the remaining crack length was substantially shorter in contrast to the original SiNx film. This work demonstrates the crucial role of residual ligands on internal strain regulation and points out a pathway to achieve crack-free PECVD SiNx films in industrial manufacturing.

BackgroundOpioid anesthesia can modulate the impaired immune response and opioid-sparing anesthesia may preserve immune functions. This study was performed to assess the effects of opioid-free anesthesia (OFA) and opioid-based anesthesia (OA) on perioperative macrophages differentiation, cytokine changes, and perioperative complications in locally advanced GC (LAGC) patients.MethodsWe used quality of recovery-15 (QoR-15) questionnaire scores and visual analog scale (VAS) scores to compare postoperative quality of recovery and pain level. In addition, the adverse reactions of patients in the two groups were compared. The perioperative serum level of inflammatory cytokines and the ratio of macrophage subtypes were detected.ResultsThe OFA group had significantly longer extubation time and PACU stay, whereas the OA group had significantly higher rate of hypotension, higher doses of norepinephrine, higher PONV and dizziness rate, and delayed flatus passage time. The QoR-15 score on postoperative 24 h was significantly higher in OFA group than in OA group. At the end of or after the surgery, the OFA group had higher levels of interleukin (IL)-12, IL-1β, tumor necrosis factor (TNF)-α, CD68+CD163− macrophage rate, but lower levels of IL-10, transforming growth factor (TGF)-β, and CD68+CD163+ macrophage rate, indicating OFA attenuated perioperative immunosuppression by diminishing M2 and promoting M1 macrophage polarization. And the reversal tendency is more obvious in LAGC patients with neoadjuvant PD-1 inhibitor.ConclusionsThe OFA may attenuate perioperative immunosuppression by diminishing M2 and promoting M1 macrophage polarization in LAGC patients with neoadjuvant PD-1 inhibitor.Clinical trial registrationhttp://gcpgl.sysucc.org.cn, identifier 2022-FXY-001.

Laryngeal cancer is the most common malignant tumor in the head and neck region. The larynx, also known as the voice box, plays a crucial role in voice production and ventilation. Enhancing the diagnosis and treatment of laryngeal cancer can significantly improve patients’ prognosis and quality of life. Artificial intelligence (AI) technology shows promise as a valuable tool for diagnosing laryngeal cancer. It not only reduces the burden on endoscopists in interpreting images but also performs screening and diagnosis efficiently and accurately. However, due to the hidden and diverse nature of laryngeal cancer lesions, achieving accuracy and efficiency in AI-based diagnosis presents poses challenges. This study introduces an improved YOLOv8 algorithm named MSEC-YOLO, specifically designed for the detection and classification tasks of laryngeal cancer in endoscopic images. A novel multiscale enhanced convolution module has been introduced to improve the model’s feature extraction capabilities for small-sized targets. Additionally, a tiny fully convolutional network architecture has been employed, reducing the number of model parameters and computational costs while maintaining or enhancing performance, which is crucial for real-time medical imaging analysis. The experiments utilized a real-world endoscopic image dataset from the hospital, and the results indicated that MSEC-YOLO outperformed the original YOLOv8 model and its multi-kernel versions across multiple evaluation metrics, especially in critical categories such as malignant tumors, polyps, and papillomas, demonstrating extremely high precision and recall rates.

K subgroup of Avian leukemia virus (ALV-K), as the latest reported exogenous ALV subgroup, can cause severe immunosuppression and tumorigenesis in infected chickens and bring serious threats to the poultry. Culling the positive chickens from breeder flocks is the most effective measurement for controling its infection at present. To setup a specific and rapid method for detecting ALV-K, an immunochromatographic strip (ICS) based on colloidal gold nanoparticles and double monoclonal antibodies (MAbs) immunochemical reaction was successfully developed. The solution containing colloidal gold particles (colloidal 20 nm) was prepared by sodium citrate reduction, and the ascites MAbs against ALV-K were purified and identified. The antibody concentration of the colloidal gold-MAb complex reaction system was 9.6 μg/mL, and the optimum pH was 7.5. The optimum coating concentrations of the captured antibody and quality control antibody on the NC membrane were 1.2 mg/mL and 0.6 mg/mL, respectively. The results showed that the prepared ICS could specifically detect ALV-K and had no cross reaction with exogenous ALV-A/B/J strains. Its sensitivity was 64 TCID50/mL against ALV-K and 0.25 μg/mL against ALV-K gp85 protein. The ICS stored at 4 °C for 180 d, at 25 °C for 45 d, and at 37 °C for 15 d could stably detect ALV-K in the samples. In comparision detection of 128 clinical samples, the coincidence rates in cloacal swab samples, serum samples, egg albumen samples and tissue homogenate samples between the ICS and the commercial ALV p27 antigen ELISA kit were 100 %, 100 %, 96.7 % and 96.9 %, respectively; moreover, the prepared ICS in operations was simpler and more convenient. This study supplies a new specific and rapid method for detecting ALV-K in clinics. This is the first report on the application of nano-colloidal gold ICS in ALV-K differential detection.

Abstract As global temperature rises, the frequency of extreme climate events, e.g., severe droughts and floods, has increased significantly and caused severe damage over the past years. To this regard, precipitation efficiency, a crucial meteorological parameter, could provide valuable insights for a better understanding of the patterns and characteristics of these extreme events. In this study, taking Guangdong province as an exemplary region, we first obtained long-term and high-resolution historical records of precipitation efficiency by integrating the observations from a dense network of Global Navigation Satellite System (GNSS) stations with precipitation data, and then characterized the extreme drought and wetness through climate indices. We found a distinct seasonal trend in precipitation efficiency in Guangdong, with annual fluctuations ranging from 10 to 25%. Notably, precipitation efficiency is higher in proximity to the Pearl River Delta Plain and gradually decreases towards the east and west. The occurrence of anomalous peaks and valleys in precipitation efficiency generally corresponds to dry and wet conditions, respectively. A total of 9 extreme wet events and 6 dry events occurred from January 2007 to May 2022, with durations from 3 to 6 months. Our results also demonstrated that both wet and dry frequencies exhibit an increasing trend with the expansion of the time scale, and the frequency of extreme events near the Pearl River Delta Plain surpasses that of other regions. Furthermore, the propagation time from meteorological anomalies to agricultural and hydrological anomalies is about 3 months. The periodic characteristics of meteorological anomalies are identified as the primary driver for other anomalous periodic patterns. Our work unveils the long-term dynamic behavior of precipitation efficiency, as well as the characteristics of extreme drought and wetness events in the regions characterized by intricate land–atmosphere interactions.

Recent stealth attacks cleverly disguise malicious activities, masquerading as ordinary connections to popular online services through seemingly innocuous applications. These methods often evade detection by traditional network monitoring or signature-based techniques, as attackers frequently hide Command and Control (C&C) servers within well-known cloud service providers, making the traffic anomalies appear normal. In this paper, we introduce an application-level monitoring system, Anteater. Anteater constructs a detailed profile for each legitimate software’s network traffic behavior, outlining the expected traffic patterns. By scrutinizing a program’s network traffic configuration, Anteater efficiently pinpoints and intercepts the IP addresses associated with abnormal program access. Implemented in a real-world enterprise environment, Anteater was tested on a dataset containing over 400 million real-world network traffic sessions. The evaluation results demonstrate that Anteater achieves a high detection rate for malware injections, boasting a true positive rate of 94.5% and a false positive rate of less than 0.1%.

Abstract The proteasome inhibitor bortezomib (BTZ) is the first‐line therapy for multiple myeloma (MM). BTZ resistance largely limits its clinical application in MM. Interleukin‐33 (IL‐33) exerts antitumor effects through various mechanisms, including enhancing antitumor immunity and promoting the apoptosis of cancer cells. Here, the synergistic anti‐MM effect of IL‐33 and BTZ was verified, and the underlying mechanisms were elucidated. Bioinformatic analysis indicated that IL‐33 expression levels were downregulated in MM, and that BTZ‐treated MM patients with high IL‐33 levels had better prognosis than those with low IL‐33 levels. Moreover, the patients with high IL‐33 levels had a better treatment response to BTZ. Further immune analysis suggested that IL‐33 can enhance the anti‐MM immunity. IL‐33 and BTZ synergistically inhibited proliferation and induced apoptosis of MM cells, which was mediated by the excessive accumulation of cellular reactive oxygen species (ROS). Furthermore, increased ROS hindered the nuclear translocation of NF‐κB‐p65, thereby decreasing the transcription of target stemness‐related genes (SOX2, MYC, and OCT3/4). These effects induced by the combination therapy could be reversed by eliminating ROS by N‐acetylcysteine. In conclusion, our results indicated that IL‐33 enhanced the sensitivity of MM to BTZ through ROS‐mediated inhibition of nuclear factor kappa‐B (NF‐κB) signal and stemness properties.

Open-shell molecules rarely fluoresce, due to their typically faster non-radiative relaxation rates compared to closed-shell ones. Even rarer is the fluorescence from states that have two more unpaired electrons than the open-shell ground state, since they involve excitations from closed-shell orbitals to vacant-shell orbitals, which are typically higher in energy compared to excitations from or out of open-shell orbitals. States that are dominated by the former type of excitations are known as tripdoublet states when they can be described as a triplet excitation antiferromagnetically coupled to a doublet state, and their description by unrestricted single-reference methods (e.g., U-TDDFT) is notoriously inaccurate due to large spin contamination. In this work, we applied our spin-adapted TDDFT method, X-TDDFT, and the efficient and accurate static-dynamic-static second order perturbation theory (SDSPT2), to the study of the excited states as well as their relaxation pathways of copper(II) porphyrin; previous experimental works suggested that the photoluminescence of some substituted copper(II) porphyrins originate from a tripdoublet state, formed by a triplet ligand π → π* excitation antiferromagnetically coupled with the unpaired d electron. Our results demonstrated favorable agreement between the X-TDDFT, SDSPT2 and experimental excitation energies, and revealed noticeable improvements of X-TDDFT compared to U-TDDFT, not only for vertical excitation energies but also for adiabatic energy differences. These suggest that X-TDDFT is a reliable tool for the study of tripdoublet state fluorescence. Intriguingly, we showed that the aforementioned tripdoublet state is only slightly above the lowest doublet excited state and lies only slightly higher than the lowest quartet state, which suggests that the tripdoublet of copper(II) porphyrin is long-lived enough to fluoresce due to a lack of efficient non-radiative relaxation pathways; an explanation for this unusual state ordering is given. Indeed, thermal vibration correlation function (TVCF)-based calculations of internal conversion, intersystem crossing, and radiative transition rates confirm that copper(II) porphyrin emits thermally activated delayed fluorescence (TADF) and a small amount of phosphorescence at low temperature (83 K), in accordance with experiment. The present contribution is concluded by a few possible approaches of designing new molecules that fluoresce from tripdoublet states.

Blockchain technology, initially developed as a decentralized and transparent mechanism for recording transactions, faces significant privacy challenges due to its inherent transparency, exposing sensitive transaction data to all network participants. This study proposes a blockchain privacy protection algorithm that employs a digital mutual trust mechanism integrated with advanced cryptographic techniques to enhance privacy and security in blockchain transactions. The contribution includes the development of a new dynamic Byzantine consensus algorithm within the Practical Byzantine Fault Tolerance framework, incorporating an authorization mechanism from the reputation model and a proof consensus algorithm for robust digital mutual trust. Additionally, the refinement of homomorphic cryptography using the approximate greatest common divisor technique optimizes the encryption process to support complex operations securely. The integration of a smart contract system facilitates automatic and private transaction execution across the blockchain network. Experimental evidence demonstrates the superior performance of the algorithm in handling privacy requests and transaction receipts with reduced delays and increased accuracy, marking a significant improvement over existing methods.

The photocatalyst (PC) zinc tetraphenylporphyrin (ZnTPP) is highly efficient for photoinduced electron/energy transfer reversible addition-fragmentation chain transfer (PET-RAFT) polymerization. However, ZnTPP suffers from poor absorbance of orange light by the so-called Q-band of the absorption spectrum (maximum absorption wavelength λmax = 600 nm, at which molar extinction coefficient εmax = 1.0×104 L/(mol·cm)), hindering photo-curing applications that entail long light penetration paths. Over the past decade, there has not been any competing candidate in terms of efficiency, despite a myriad of efforts in PC design. By theoretical evaluation, here we rationally introduce a peripheral benzo moiety on each of the pyrrole rings of ZnTPP, giving zinc tetraphenyl tetrabenzoporphyrin (ZnTPTBP). This modification not only enlarges the conjugation length of the system, but also alters the a1u occupied π molecular orbital energy level and breaks the accidental degeneracy between the a1u and a2u orbitals, which is responsible for the low absorption intensity of the Q-band. As a consequence, not only is there a pronounced hyperchromic and bathochromic effect (λmax = 655 nm and εmax = 5.2×104 L/(mol·cm)) of the Q-band, but the hyperchromic effect is achieved without increasing the intensity of the less useful, low wavelength absorption peaks of the PC. Remarkably, this strong 655 nm absorption takes advantage of deep-red (650–700 nm) light, a major component of solar light exhibiting good atmosphere penetration, exploited by the natural PC chlorophyll a as well. Compared with ZnTPP, ZnTPTBP displayed a 49% increase in PET-RAFT polymerization rate with good control, marking a significant leap in the area of photo-controlled polymerization.

Hepatitis-hydropericardium syndrome (HHS), caused by fowl adenovirus serotype 4 (FAdV-4), has been widely spread across China, resulting in great financial losses in the poultry industry. Therefore, efficient vaccines against this disease urgently need to be developed. In our study, the fiber-2 and penton base proteins derived from the FAdV-4 JS strain were expressed in a prokaryotic system (E. coli) in a soluble form. Then, the efficacy of the two recombinant proteins formulated with cheap and widely used adjuvants (Marcol™ 52 white oil) were respectively tested, and the minimum immune doses and safety of the above proteins were also determined. It was indicated that the fiber-2 (20 µg/bird, 200 µg/bird) and penton base (200 µg/bird) could provide complete protection against the highly pathogenic FAdV-4 and suppress its replication and shedding. Unfortunately, only the fiber-2 protein could induce complete protection (10/10) at a low dose (10 µg/bird). In addition, we confirmed that the fiber-2 subunit vaccine formulated with oil adjuvants was safe for vaccinated chickens. Conclusively, all of our results suggest that we successfully prepared an efficient and cheap fiber-2 subunit vaccine with few side effects.

Abstract Background Bloodstream infection (BSI) is a life-threatening condition with high morbidity and mortality rates worldwide. Early diagnosis of BSI is critical to avoid the unnecessary application of antimicrobial agents and for proper treatment. However, the current standard methods based on blood culture are time-consuming, thus failing to provide a timely etiological diagnosis of BSI, and common PCR-based detection might be inhibited by matrix components. Methods The current study explored an integrated pre-analytical treatment protocol for whole blood samples, wherein pathogens are enriched and purified by incubation and concentration, and inhibitors are inactivated and removed. Further, this study developed and evaluated a novel high-throughput multiplex genetic detection system (HMGS) to detect 24 of the most clinically prevalent BSI pathogens in blood culture samples and pre-treated whole blood samples. The specificity and sensitivity were evaluated using related reference strains and quantified bacterial/fungal suspensions. The clinical utility of BSI-HMGS combined with the pre-analytical treatment protocol was verified using blood cultures and whole blood samples. Results The combined pre-treatment protocol and BSI-HMGS was highly specific for target pathogens and possessed a low detection limit for clinical whole blood samples. The pre-treatment protocol could deplete the PCR inhibitors effectively. For blood culture samples, the current method showed 100.0% negative percent agreements and > 87.5% positive percent agreements compared to the reference results based on blood culture findings. For whole blood samples, the current method showed 100.0% negative percent agreements and > 80.0% positive percent agreements compared to the reference results for most pathogens. The turnaround time was ≤ 8 h, and all the procedures could be conducted in a general clinical laboratory. Conclusion The BSI-HMGS combined with the pre-treatment protocol was a practical and promising method for early and precise detection of BSIs, especially for areas without access to advanced medical facilities.

Structural rearrangements of chromosome 4p gives rise to a group of rare genomic disorders that mainly result in two different clinical entities: Wolf-Hirschhorn syndrome (WHS) and partial 4p trisomy. The severity of the phenotype depends on the size of the deletion or locus duplication. Here, we present two unrelated individuals with a copy number variation of chromosome 4p. Inverted duplication deletions (inv dup-del) in 4p are particularly rare. Case 1 describes a 15-year-old girl with a 1.055 Mb deletion of terminal 4p, distal to the recognized critical region of WHS, and a large duplication of 9.6 Mb in size from 4p16.3 to p16.1. She had postnatal development delay, intellectual disability (especially pronounced in speech), seizure/electroencephalogram anomalies, and facial dysmorphic features. This unusual chromosomal imbalance resulted in the WHS phenotype rather than the 4p trisomy syndrome phenotype. Case 2 describes a 21-month-old boy with a 1.386 Mb terminal 4p deletion who presented with slight developmental delay, borderline intellectual disability, and seizures. Combined with previous reported cases of 4 pter del-dup or pure 4p terminal deletions, our observations suggest that terminal chromosome 4p deletion is more pathogenic than the concomitant partial 4p duplication, and some regions of the 4p terminal may have regulatory effects on the remaining region of 4p. About nine cases have been reported thus far to date, and our study delineates further genotype-phenotype correlations about terminal 4p duplication-deletions for predicting disease prognosis and patient counseling.

Clinical radiotherapy (RT) is severely limited by hypoxic tumor microenvironment and a lack of targeting precision. Therefore, it is crucial to develop highly efficient radiosensitizers to enhance RT efficacy. Herein, a novel kind of epidermal growth factor receptor (EGFR)-antagonistic affibody-functionalized Pt-based nanozyme for RT sensitization to EGFR-positive tumors was developed. In this study, porous platinum nanoparticles (pPt NPs) featuring catalase (CAT)-like activity and strong radiation energy absorption ability were first synthesized. Then, a thin biomimetic polydopamine (PDA) layer was coated on pPt NPs to optimize its biocompatibility as well as provide a reactive surface. Finally, a dimeric EGFR-antagonistic affibody called ZEGFR expressed by the Escherichia coli (E. coli), was conjugated to pPt@PDA NPs (termed pPt@PDA-ZEGFR NPs) to precisely recognize EGFR-positive A431 tumors. Under the navigation of ZEGFR, superior tumor homing was achieved with these Pt-based nanozymes, which was ascribed to EGFR receptor-mediated endocytosis. As high-Z metal NPs exhibit an inherently strong ability to absorb radiation energy and catalyze endogenous H2O2 in tumors, the pPt@PDA-ZEGFR NPs demonstrated superb therapeutic efficacy; specifically, the NPs significantly inhibited HIF-1α expression and increased RT-induced DNA damage. Furthermore, the biosafety of these Pt-based nanozymes was good during short-term treatment. In summary, EGFR-antagonistic affibody-functionalized Pt-based nanozymes are promising radiosensitizers for the precise therapy of EGFR-positive tumors.

Precise liver tumors and associated organ segmentation hold immense value for surgical and radiological intervention, enabling anatomical localization for pre-operative planning and intra-operative guidance. Modern deep learning models for medical image segmentation have evolved from convolution neural networks to transformer architectures, significantly boosting global context understanding. However, accurate delineation especially of hepatic lesions remains an enduring challenge due to models’ predominant focus solely on spatial feature extraction failing to adequately characterize complex medical anatomies. Moreover, the relative paucity of expertly annotated medical imaging data restricts model exposure to diverse pathological presentations. In this paper, we present a three-phrased cascaded segmentation framework featuring an X-Fuse model that synergistically integrates spatial and frequency domain’s complementary information in dual encoders to enrich latent feature representation. To enhance model generalizability, building upon X-Fuse topology and taking advantage of additional unlabeled pathological data, our proposed integration of curriculum pseudo-labeling with Jensen–Shannon variance-based uncertainty rectification promotes optimized pseudo supervision in the context of semi-supervised learning. We further introduce a tumor-focus augmentation technique including training-free copy-paste and knowledge-based synthesis that show efficacy in simplicity, contributing to the substantial elevation of model adaptability on diverse lesional morphologies. Extensive experiments and modular evaluations on a holdout test set demonstrate that our methods significantly outperform existing state-of-the-art segmentation models in both supervised and semi-supervised settings, as measured by the Dice similarity coefficient, achieving superior delineation of bones (95.42%), liver (96.26%), and liver tumors (89.53%) with 16.41% increase comparing to V-Net on supervised-only and augmented-absent scenario. Our method marks a significant step toward the realization of more reliable and robust AI-assisted diagnostic tools for liver tumor intervention. We have made the codes publicly available [ https://github.com/lyupengju/X-Fuse ].

Abstract Purpose The interaction between tumor cells and tumor microenvironment (TME) has an important impact on progression and prognosis of multiple myeloma (MM), and has been proven to be promising therapeutic targets. This study intended to explore the relationship between TME and prognosis and identify valuable biomarkers of MM. Methods The transcriptomic and clinical information of MM retrieved from the Gene Expression Omnibus (GEO) were used to establish the model. The curve of Kaplan–Meier survival and the time-dependent receiver operating characteristic (ROC) were used to appraise the predictive ability. A nomogram was established for clinical application. Furthermore, the CIBERSORT algorithm was used to investigate the relation between IRGPI with the infiltration of immune cells. We also used histology, as well as in vitro and in vivo experiments to validate these findings. Results The results demonstrated an immune-related gene-based prognostic index (IRGPI) combined with clinical information. Patients were separated into high- and low-risk groups based on risk score, which had significantly difference in survival status and immune infiltrations. Furthermore, we identified CXCL11 as a key factor, which positively promotes the progression of MM and correlate with macrophage M2-like polarization and tumor immune cells infiltration. Conclusion Our findings suggest the IRGPI significantly demonstrate the differential prognosis and prediction of immune cells infiltration. It provides some insights into the complex interaction between myeloma tumor cells and the TME, as well as in the development of a novel biomarker target for anti-MM therapy.

Considering the difficulty in measuring the P-band electromagnetic parameters of porous materials such as graphene foam (GF), this paper proposes a free-space frequency-domain technology to measure the electrical properties of finite-plate materials from multiple perspectives. First, the multiangle reflectance of the sample was measured with the arch method; second, an electromagnetic simulation model consistent with the sample size was established, and the reflectance of different angles was calculated using the method of moments (MoM). Finally, the genetic algorithm (GA) was optimized for the electromagnetic parameters such that the reflective difference between the electromagnetic simulation model and the test obtained was minimized. The validity of the method was verified by reconstructing the electromagnetic parameters of a 1 $\text{m}\times 1\,\,\text{m}\times 0.03$ m absorbing material at 0.5 GHz. The unique advantage of this method is that it solves the problem of inversion of electromagnetic parameters based on the multiangle reflectivity of an absorbing material plate within three times the wavelength. Because the electromagnetic simulation model can automatically divide the appropriate mesh size according to the size of the electromagnetic parameters, this method is suitable for high-loss and low-loss materials. In addition, this method avoids the special processing requirements regarding the shape size of the absorbing material, which has a wide application scope and low cost.

BackgroundTofu whey wastewater (TWW) is the wastewater of tofu processing, which is rich in a variety of nutrients. Rhodotorula glutinis can make full use of TWW to ferment and reproduce yeast cells, produce carotenoids and other nutrients, improve the utilization value of TWW, and reduce environmental pollution and resource waste.MethodsIn this study, the nutrient composition changes of TWW treated by Rhodotorula glutinis were analyzed to reformulate TWW medium, and the optimal composition and proportion of TWW medium that can improve the biomass and carotenoids production of Rhodotorula glutinis were explored. Meanwhile, the Rhodotorula glutinis liquid obtained under these conditions was used to prepare biological feed for laying hens, and the effect of Rhodotorula glutinis growing on TWW as substrate on laying performance and egg quality of laying hens were verified.ResultsThe results showed that the zinc content of TWW after Rhodotorula glutinis fermentation increased by 62.30%, the phosphorus content decreased by 42.31%, and the contents of vitamin B1, B2 and B6 increased to varying degrees. The optimal fermentation conditions of Rhodotorula glutinis in the TWW medium were as follow: the initial pH was 6.40, the amount of soybean oil, glucose and zinc ions was 0.80 ml/L, 16.32 g/L, and 20.52 mg/L, respectively. Under this condition, the biomass of Rhodotorula glutinis reached 2.23 g/L, the carotenoids production was 832.86 μg/g, and the number of effective viable yeast count was 7.08 × 107 cfu/ml. In addition, the laying performance and egg quality of laying hens fed Rhodotorula glutinis biological feed were improved.DiscussionIn this study, we analyzed the composition changes of TWW, optimized the fermentation conditions of Rhodotorula glutinis in TWW medium, explored the influence of Rhodotorula glutinis utilizing TWW on laying layers, and provided a new idea for the efficient utilization of TWW.

The Hong Kong and Macao Special Administrative Regions, situated within China’s Guangdong–Hong Kong–Macao Greater Bay Area, significantly influence and are impacted by their air quality conditions. Rapid urbanization, high population density, and air pollution from diverse factors present challenges, making the health of the atmospheric environment in these regions a research focal point. This study offers three key contributions: (1) It applied an interpretable dynamic Bayesian network (DBN) to construct a dynamic causal model of air quality in Hong Kong and Macao, amidst complex, unstable, multi-dimensional, and uncertain factors over time. (2) It investigated the dynamic interaction between meteorology and air quality sub-networks, and both qualitatively and quantitatively identified, evaluated, and understood the causal relationships between air pollutants and their determinants. (3) It facilitated an online collaborative forecast of air pollutant concentrations, enabling pollution warnings. The findings proposed that a DBN-based dynamic causal model can effectively explain and manage complex atmospheric environmental systems in Hong Kong and Macao. This method offers crucial insights for decision-making and the management of atmospheric environments not only in these regions but also for neighboring cities and regions with similar geographical contexts.

Toona ciliata var. pubescens is classified as Toona subgenus of Meliaceae family, which belongs to a large deciduous tree species. It is also a kind of precious timber tree species and has a certain medicinal value. Here, the first complete chloroplast genome (cpDNA) sequence of T. ciliata var. pubescens was determined using the Illumina sequencing platform. The cpDNA genome is 159,481 bp in length, containing a large single-copy region (LSC) of 87,176 bp and a small single-copy region (SSC) of 18,381 bp, which were separated by a pair of inverted repeats (IRs) regions of 26,962 bp. The genome contains 138 genes, including 90 protein-coding genes, eight ribosomal RNA genes, and 40 transfer RNA genes. The phylogenetic analysis based on 19 cpDNA genomes showed a close relationship with Toona ciliate.

Melatonin is a multifunctional nontoxic bio-stimulant or signaling molecule, generally distributing in different animal and plant organs for invigorating numerous physiological processes against abiotic stresses. In this study, we investigated the potential impact of melatonin on the cyanobacterium Nostoc flagelliforme when exposed to salt stress according to some biochemical and physiological parameters, such as relative electrolyte leakage, PSII activity, and photosynthetic pigments including chlorophyll a, phycocyanobilin, and phycoerythrobilin. We found that melatonin could also maintain K+ homeostasis in salt-stressed N. flagelliforme. These above results confirmed melatonin had multiple functions in hyperosmotic stress and ion stress caused by salinity. Notably, we observed melatonin could regulate the reactive oxygen species (ROS) signal and distinctly decrease the content of hydrogen peroxide and superoxide anion in salt-stressed cells, which were largely attributed to the increased antioxidant enzymes activities including catalase, superoxide dismutase, ascorbate peroxidase, and glutathione reductase. Finally, qRT-PCR analysis showed that melatonin stimulated the expression of antioxidant genes (NfCAT, NfSOD, and NfGR). In general, our findings demonstrate melatonin has beneficial effects on N. flagelliforme under salt stress by intensively regulating antioxidant system.

Gallstone disease (GD) is one of the most common gastrointestinal diseases worldwide. Nowadays, intestinal microbiota are thought to play important roles in the formation of gallstones. In our study, human fecal samples were extracted for metagenomic next-generation sequencing (mNGS) on the Illumina HiSeq platform, followed by bioinformatics analyses. Our results showed that there was a particular intestinal micro-ecosystem in GD patients. In contrast to healthy people, the sequences of Bacteroidetes, Bacteroides and Thetaiotaomicron were obviously more abundant in GD patients at phylum, genus and species levels, respectively. On the other hand, the glycan metabolism and drug resistance, especially for the β-lactams, were the most profound functions of gut microbes in GD patients compared to those in normal subjects. Furthermore, a correlation analysis drew out that there existed a significant relationship between the serum levels of biochemical indicators and abundances of intestinal microbes in GD patients. Our results illuminate both the composition and functions of intestinal microbiota in GD patients. All in all, our study can broaden the insight into the potential mechanism of how gut microbes affect the progression of gallstones to some extent, which may provide potential targets for the prevention, diagnosis or treatment of GD.

Abstract Background Wood basic density (WBD) is one of the most crucial wood property in tree and mainly determined the end use of wood for industry. However, the measurement WBD is time- and cost-consuming, which an alternatively fast and no-destructive measurement is needed. In this study, capability of NIR spectroscopy combined with partial least squares regression (PLSR) to quantify the WBD were examined in multiple wood species. To obtain more accurate and robust prediction models, the grain angle (0° (transverse surface), 45°, 90° (radial surface)) influence on the collection of solid wood spectra and a comparison of found variable selection methods for NIR spectral variables optimization were conducted, including significant Multivariate Correlation (sMC), Regularized elimination procedure (Rep), Iterative predictor weighting (Ipw) and Genetic algorithm (Ga). Models made by random calibration data selection were conducted 200 times performance evaluation. Results These results indicate that 90° angle models display relatively highest efficiency than other angle models, mixed angle model yield a satisfied WBD prediction results as well and could reduce the influence of grain angle. Rep method shows a higher efficiency than other methods which could eliminate the uninformative variables and enhance the predictive performance of 90° angle and mix angle models. Conclusions This study is potentially shown that the WBD (g/cm3) on solid wood across grain angles and varies wood species could be measured in a rapid and efficient way using NIR technology. Combined with the PLSR model, our methodology could serve as a tool for wood properties breeding and silviculture study.

Abstract Background Plant traits related to nutrition have an influential role in tree growth, tree production and nutrient cycling. Therefore, the breeding program should consider the genetics of the traits. However, the measurement methods could seriously affect the progress of breeding selection program. In this study, we tested the ability of spectroscopy to quantify the specific leaf nutrition traits including anthocyanins (ANTH), flavonoids (FLAV) and nitrogen balance index (NBI), and estimated the genetic variation of these leaf traits based on the spectroscopic predicted data. Fresh leaves of Sassafras tzumu were selected for spectral collection and ANTH, FLAV and NBI concentrations measurement by standard analytical methods. Partial least squares regression (PLSR), five spectra pre-processing methods, and four variable selection algorisms were conducted for the optimal model selection. Each trait model was simulated 200 times for error estimation. Results The standard normal variate (SNV) to the ANTH model and 1st derivatives to the FLAV and NBI models, combined with significant Multivariate Correlation (sMC) algorithm variable selection are finally regarded as the best performance models. The ANTH model produced the highest accuracy of prediction with a mean R2 of 0.72 and mean RMSE of 0.10%, followed by FLAV and NBI model (mean R2 of 0.58, mean RMSE of 0.11% and mean R2 of 0.44, mean RMSE of 0.04%). High heritability was found for ANTH, FLAV and NBI with h 2 of 0.78, 0.58 and 0.61 respectively. It shows that it is beneficial and possible for breeding selection to the improvement of leaf nutrition traits. Conclusions Spectroscopy can successfully characterize the leaf nutrition traits in living tree leaves and the ability to simultaneous multiple plant traits provides a promising and high-throughput tool for the quick analysis of large size samples and serves for genetic breeding program.

Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy patients especially those with refractory epilepsy. At present, a large number of deep learning algorithms such as Convolutional Neural Networks and Recurrent Neural Networks have been used to predict epileptic seizures and have obtained better performance than traditional machine learning methods. However, these methods usually transform the Electroencephalogram (EEG) signal into a Euclidean grid structure. The conversion suffers from loss of adjacent spatial information, which results in deep learning models requiring more storage and computational consumption in the process of information fusion after information extraction. This study proposes a general Graph Convolutional Networks (GCN) model architecture for predicting seizures to solve the problem of oversized seizure prediction models based on exploring the graph structure of EEG signals. As a graph classification task, the network architecture includes graph convolution layers that extract node features with one-hop neighbors, pooling layers that summarize abstract node features; and fully connected layers that implement classification, resulting in superior prediction performance and smaller network size. The experiment shows that the model has an average sensitivity of 96.51%, an average AUC of 0.92, and a model size of 15.5 k on 18 patients in the CHB-MIT scalp EEG dataset. Compared with traditional deep learning methods, which require a large number of parameters and computational effort and are demanding in terms of storage space and energy consumption, this method is more suitable for implementation on compact, low-power wearable devices as a standard process for building a generic low-consumption graph network model on similar biomedical signals. Furthermore, the edge features of graphs can be used to make a preliminary determination of locations and types of discharge, making it more clinically interpretable.

Photomediated-reversible-deactivation radical polymerisation has a limited scope of available photocatalysts due to multiple stringent requirements of properties. Here the authors show, an oxygen-mediated reductive quenching pathway for photoinduced electron transfer reversible addition-fragmentation chain transfer polymerisation.

Abstract Background Risk stratification and prognosis prediction of acute myeloid leukemia (AML) are largely dependent on pre-treatment information. However, post-treatment data also provides much useful information. In this retrospective study, we explored whether the level of blood count recovery before and after the first minimal residual disease (MRD) negative complete remission (CR) is relevant to clinical outcomes of AML patients. Methods For each included patient, peripheral platelet counts were measured on the day before initial treatment (PLTpre), whereas platelet peak values (PLTpeak) were recorded after marrow recovery following the chemotherapy course inducing the first MRD-negative CR. The difference (DPLT) between these two values (DPLT = PLTpeak−PLTpre) was calculated. X-tile software was utilized to establish the optimal cut-point for DPLT, which was expected to distinguish CR patients with different clinical outcomes. A cross validation analysis was conducted to confirm the robustness of the established cut-point. The results were further tested by a Cox multivariate analysis. Results The optimal cut-point of DPLT was determined as 212 × 109/L. Patients in high DPLT group were observed to have a significantly better PFS (p = 0.016) and a better OS (without statistical significance, p = 0.106). Cox multivariate analysis showed that higher DPLT was associated with longer PFS (HR = 2.894, 95% CI: 1.320–6.345, p = 0.008) and longer OS (HR = 3.077, 95% CI: 1.130–8.376, p = 0.028). Conclusion Platelet recovery degree before and after achieving MRD-negative CR (DPLT) is a potential predictor of clinical outcomes in CR patients. Higher DPLT value is associated with longer PFS and OS. Our findings may help to develop simple methods for AML prognosis evaluation.

In view of the lack of clear physical significance of the parameters of the traditional nonlinear seepage models and the difficulty of obtaining accurate experimental measurements of the two-phase relative permeability curve, a nonlinear seepage model of a tight reservoir is established on the basis of fractal theory and boundary layer theory. The results show that the proposed model can comprehensively reflect the effects of reservoir matrix physical properties, reservoir fluid physical properties, wettability, and displacement-pressure gradient on the single-phase and two-phase nonlinear seepage characteristics of tight reservoirs. Furthermore, the introduction of the permeability loss factor makes the two-phase relative permeability model more representative of the morphological characteristics of the actual relative permeability curve and avoids the disadvantage that the relative permeability at the end point of the wetting phase has in the traditional model. Finally, by taking the tight core of Changqing Oilfield as an example, a sensitivity analysis of the proposed model is conducted, which proves the practical application of this model. The proposed model provides a convenient theoretical method for the accurate characterization of nonlinear seepage characteristics of tight reservoirs and is of great significance to the numerical simulation, productivity evaluation, and optimization of tight reservoirs.

The electrical properties of InN give it potential for applications in III-nitride electronic devices, and the use of lower-dimensional epitaxial structures could mitigate issues with the high lattice mismatch of InN to GaN (10%). N-polar MOCVD growth of InN was performed to explore the growth parameter space of the horizontal one-dimensional InN quantum wire-like structures on miscut substrates. The InN growth temperature, InN thickness, and NH3 flow during growth were varied to determine optimal quantum wire segment growth conditions. Quantum wire segment formation was observed through AFM images for N-polar InN samples with a low growth temperature of 540 °C and 1–2 nm of InN. Below 1 nm of InN, quantum dashes formed, and 2-D layers were formed above 2 nm of InN. One-dimensional anisotropy of the electrical conduction of N-polar InN wire-like samples was observed through TLM measurements. The sheet resistances of wire-like samples varied from 10–26 kΩ/□ in the longitudinal direction of the wire segments. The high sheet resistances were attributed to the close proximity of the treading dislocations at the InN/GaN interface and might be lowered by reducing the lattice mismatch of InN wire-like structures with the substrate using high lattice constant base layers such as relaxed InGaN.

Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R2 of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.

Chemotherapy is still one of the most common ways to treat human glioblastoma in clinic. However, severe side effects limited its clinic application. Design of cancer-targeted drugs with high efficiency and low side effect is urgently needed. Herein, silver nanoparticles (Ag NPs) and nano-selenium (Se NPs) conjugated with RGD peptides (Ag@Se@RGD NPs) to target integrin high-expressed glioma were designed. The results found that Ag@Se@RGD NPs displayed stable particle size and morphology in physiological condition, and induced significant integrin-targeted intracellular uptake. Ag@Se@RGD NPs in vitro dose-dependently inhibited U251 human glioma cells growth by induction of cells apoptosis through triggering the loss of mitochondrial membrane potential, overproduction of reactive oxygen species (ROS), and MAPKs activation. However, ROS inhibition dramatically attenuated Ag@Se@RGD NPs-induced MAPKs activation, indicating the significant role of ROS as an early apoptotic event. Importantly, Ag@Se@RGD NPs administration in vivov effectively inhibited U251 tumor xenografts growth by induction of apoptosis through regulation MAPKs activation. Taken together, our findings validated the rational design that Ag-Se NPs conjugated with RGD peptides was a promising strategy to combat human glioma by induction of apoptosis through triggering mitochondrial dysfunction and ROS-dependent MAPKs activation.

It is critical to regulate the senescence-associated secretory phenotype (SASP) due to its effect on promoting malignant phenotypes and limiting the efficiency of cancer therapy. In this study, we demonstrated that marchantin M (Mar-M, a naturally occurring bisbibenzyl) suppressed pro-inflammatory SASP components which were elevated in chemotherapy-resistant cells. Mar-M treatment attenuated the pro-tumorigenic effects of SASP and enhanced survival in drug-resistant mouse models. No toxicity was detected on normal fibroblast cells or in animals following this treatment. Inactivation of transcription factor EB (TFEB) and nuclear factor-κB (NF-κB) by Mar-M significantly accounted for its suppression on the components of SASP. Furthermore, inhibition of SASP by Mar-M contributed to a synergistic effect during co-treatment with doxorubicin to lower toxicity and enhance antitumor efficacy. Thus, chemotherapy-driven pro-inflammatory activity, seen to contribute to drug-resistance, is an important target for Mar-M. By decreasing SASP, Mar-M may be a potential approach to overcome tumor malignancy. Key words: SASP, Marchantin M, TFEB, NF-κB, Drug resistance

BackgroundUrinary tract infections (UTIs) are one the most common infections. The rapid and accurate identification of uropathogens, and the determination of antimicrobial susceptibility, are essential aspects of the management of UTIs. However, existing detection methods are associated with certain limitations. In this study, a new urinary tract infection high-throughput multiplex genetic detection system (UTI-HMGS) was developed for the semi-quantitative detection of 18 pathogens and the simultaneously screening of nine resistance genes directly from the clinical urine sample within 4 hours.MethodsWe designed and optimized a multiplex polymerase chain reaction (PCR) involving fluorescent dye-labeled specific primers to detect 18 pathogens and nine resistance genes. The specificity of the UTI-HMGS was tested using standard strains or plasmids for each gene target. The sensitivity of the UTI-HMGS assay was tested by the detection of serial tenfold dilutions of plasmids or simulated positive urine samples. We also collected clinical urine samples and used these to perform urine culture and antimicrobial susceptibility testing (AST). Finally, all urine samples were detected by UTI-HMGS and the results were compared with both urine culture and Sanger sequencing.ResultsUTI-HMGS showed high levels of sensitivity and specificity for the detection of uropathogens when compared with culture and sequencing. In addition, ten species of bacteria and three species of fungi were detected semi-quantitatively to allow accurate discrimination of significant bacteriuria and candiduria. The sensitivity of the UTI-HMGS for the all the target genes could reach 50 copies per reaction. In total, 531 urine samples were collected and analyzed by UTI-HMGS, which exhibited high levels of sensitivity and specificity for the detection of uropathogens and resistance genes when compared with Sanger sequencing. The results from UTI-HMGS showed that the detection rates of 15 pathogens were significantly higher (P

Today, intelligence in all walks of life is developing at an unexpectedly fast speed. The complexity of the Internet of Things (IoT) big data system of intelligent parks is analyzed to unify the information transmission of various industries, such as smart transportation, smart library, and smart medicine, thereby diminishing information islands. The traditional IoT systems are analyzed; on this basis, a relay node is added to the transmission path of the data information, and an intelligent park IoT big data system is constructed based on relay cooperation with a total of three hops. Finally, the IoT big data system is simulated and tested to verify its complexity. Results of energy efficiency analysis suggest that when the power dividing factor is 0.5, 0.1, and 0.9, the energy efficiency of the IoT big data system first increases and then decreases as α0 increases, where the maximum value appears when α0 is about 7 J. Results of outage probability analysis demonstrate that the system’s simulation result is basically the same as that of the theoretical result. Under the same environment, the more hop paths the system has, the more the number of relays is; moreover, the larger the fading index m, the better the system performance, and the lower the outage possibility. Results of transmission accuracy analysis reveal that the IoT big data system can provide a result that is the closest to the actual result when the successful data transmission probability is 100%, and the parameter λ values are between 0.01 and 0.05; in the meantime, the delay of successful data transmission is reduced gradually. In summary, the wireless relay cooperation transmission technology can reduce the outage probability and data transmission delay probability of the IoT big data system in the intelligent park by adding the multihop path, thereby improving the system performance. The above results can provide an experimental basis for exploring the complexity of IoT systems in intelligent parks.

Abstract Background Acute myeloid leukemia (AML) is one of the most common types of adult acute leukemia. Standard chemotherapies can induce complete remission in selected patients; however, a majority of patients eventually relapse and succumb to the disease. Thus, the development of novel therapeutics for AML is urgently needed. Human C-type lectin-like molecule-1 (CLL-1) is a type II transmembrane glycoprotein, and its expression is restricted to myeloid cells and the majority of AML blasts. Moreover, CLL-1 is expressed in leukemia stem cells (LSCs), but absent in hematopoietic stem cells (HSCs), which may provide a potential therapeutic target for AML treatment. Methods We tested the expression of CLL-1 antigen on peripheral blood cells and bone marrow cells in healthy donor and AML patients. Then, we developed a chimeric antigen receptor (CAR) containing a CLL1-specific single-chain variable fragment, in combination with CD28, 4-1BB costimulatory domains, and CD3-ζ signaling domain. We further investigate the function of CLL-1 CAR-T cells. Results The CLL-1 CAR-T cells specifically lysed CLL-1+ cell lines as well as primary AML patient samples in vitro. Strong anti-leukemic activity was observed in vivo by using a xenograft model of disseminated AML. Importantly, CLL-1+ myeloid progenitor cells and mature myeloid cells were specifically eliminated by CLL-1 CAR-T cells, while normal HSCs were not targeted due to the lack of CLL-1 expression. Conclusions CLL-1 CAR-T represents a promising immunotherapy for the treatment of AML.

Magnetic semiconductors provide control of spin states in addition to charge states realized in conventional semiconductors, yet currently limited to weak magnetism at low temperature. Liuet al. introduce oxygen into a ferromagnetic metallic glass, resulting in a Curie temperature above 600 K.

Big data are widely woven into every sector and play an important role in the global economy as an essential factor of production.Big data can be brought together and analyzed to discern patterns,make better decisions so as to reduce production cost and increase the quality of products and services.A concise and comprehensive description that big data would have a significant influence on the structure of traditional industry including agriculture,industry and services,was given.Obviously,the big data industry is currently becoming a new economic growth point,promoting economic transformation and upgrading.

BACKGROUND: The prognostic value of the copy number (GCN) and protein expression of the mesenchymal-epithelial transition (MET) gene for survival of patients with non-small cell lung cancer (NSCLC) remains controversial. This study aims to comprehensively and quantitatively asses the suitability of MET GCN and protein expression to predict patients' survival. METHODS: PubMed, Embase, Web of Science and Google Scholar were searched for articles comparing overall survival in patients with high MET GCN or protein expression with those with low level. Pooled hazard ratio (HR) and 95% confidence intervals (CIs) were calculated using the random and the fixed-effects models. Subgroup and sensitivity analyses were also performed. RESULTS: Eighteen eligible studies enrolling 5,516 patients were identified. Pooled analyses revealed that high MET GCN or protein expression was associated with poor overall survival (OS) (GCN: HR = 1.90, 95% CI 1.35-2.68, p