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In recent years, the deep integration of basic research and clinical translational research of nanotechnology and oncology has led to the emergence of a new branch, namely integrated nano-oncology. This is an emerging and important interdisciplinary field, which plays an irreplaceable role in the diagnosis, treatment, early warning, monitoring and prevention of tumors, and has become a new interdisciplinary frontier. Here main advances of integrated nano-oncology was reviewed, mainly included controlled preparation of nanomaterials, ultra-sensitive detection of tumor biomarkers, multi-functional nanoimaging probes and integrated diagnosis and treatment technology, innovative nano drugs and nano drug delivery system, DNA nanotechnology, RNA nanotechnology, nano self-assembly technology, nanosensors, intelligent nanorobots, nanotherapeutic machines. The terms, concepts, trends and challenges are also discussed with the aim of promoting the application of nanotechnology in integrated oncology and solving the scientific and key technical problems in basic and clinical translational research of cancer.

Abstract Ficus carica L. (FC) is rich in phenolic compounds such as phenolic acids, flavonoids, and anthocyanins. Phenolic compounds in FC show remarkable health benefits and applications. The composition of phenolic compounds in FC can vary with some factors, such as variety, ripeness, geographical location, extraction methods and processing methods. New techniques such as supercritical fluid extraction, subcritical water extraction and ionic liquid extraction should be further developed and combined with traditional techniques to obtain higher quality sources of phenolic compounds. Numerous in vivo and in vitro studies reported healthy benefits of phenolic compounds in FC, such as anti‐oxidant, anti‐inflammatory, anti‐diabetic, anti‐obesity, anti‐cancer and anti‐Alzheimer's. Further works are required to reveal mechanisms and structure‐activity relationships, such as targeted metabolomics and pharmacological studies. In terms of applications, phenolic compounds in FC as prebiotics on the intestinal flora need to be further studied to expand their applicability in food fermentation. In a word, this review highlights the remarkable health benefits and application value of phenolic compounds in FC.

Study region: The Middle Route of the South-North Water Transfer Project (SNWDMRP) is a major water transfer project to optimize the spatial allocation of water resources in China. Study focus: It is difficult for traditional flow adjustment to ensure the safe operation of open-channel water transfer projects. In this study, the Long Short Term Memory (LSTM) water level prediction model is combined with the elastic interval water level control method to achieve rolling prediction control of the water level in front of the gates of a single pool, and then the model is coupled with storage compensation algorithm for prediction control of the water level of the gate group of tandem multi-canal pools. The method is applied to the SNWDMRP, and the results show that the new method is stable within the restricted water level for both single and multiple canal pools. Moreover, the control algorithm proposed in this study can make full use of the storage capacity to satisfy the need of multiple scheduling scenarios

Abstract The aging population and the prevalence of infectious diseases have impacted the traditional medical order, significantly increasing the burden on healthcare and adversely affecting the socio‐economic system. Medical sensors based on artificial intelligence (AI) provide new ideas for modern medical data collection to monitor the health status of individuals and environmental changes. Meanwhile, with the aid of AI algorithms, the big data processing capabilities of sensor systems have been greatly improved, further realizing early predictions and timely diagnoses. In this paper, a brief overview is offered on the development status of AI‐enabled medical sensors for off‐body detection, near‐body monitoring, disease prediction, and clinical decision support system, and the ongoing challenges and future prospects to move from concept to implementation are discussed. In the foreseeable future, breakthroughs in the combination of medical sensors and AI algorithms are expected to pave the way for early detection and clinical decision support and improve the accuracy and efficiency for disease diagnosis.

ObjectiveThe oblique lateral interbody fusion (OLIF) technique is a promising interbody fusion technique. This study summarizes the technical aspects of OLIF as a salvage surgery and the preliminary outcomes of a series of cases.Patients and methodsA retrospective review of patients with leg or back pain induced by pseudoarthrosis or adjacent segment disease after posterior lumbar interbody fusion/transforaminal lumbar interbody fusion was done. These patients underwent salvage OLIF surgeries in our institution from January 2021 to March 2022. Variables such as the demographic, clinical, surgical, and radiological characteristics of the enrolled patients were recorded and analyzed.ResultsEight patients (five females and three males; mean age 69.1 ± 5.7 years, range 63–80 years) were enrolled in this study. The mean operative time was 286.25 min (range: 230–440 min), and the estimated blood loss was 90 ml (range: 50–150 ml). Only one of the eight patients experienced a complication of lower limb motor weakness, which disappeared within 5 days after surgery. The latest data showed that the mean intervertebral space height increased from 8.36 mm preoperatively to 12.70 mm and the mean segmentary lordosis increased from 8.92° preoperatively to 15.05°. Bone fusion was achieved in all but one patient, who was followed up for only 3 months. The JOA scores Japanese Orthopaedic Association (JOA) Scores for low back pain of all patients significantly improved at the final follow-up.ConclusionOLIF provides a safe and effective salvage strategy for patients with failed posterior intervertebral fusion surgery. Patients effectively recovered intervertebral and foraminal height with no additional posterior direct decompression.

In recent years, automatic detection of threats in X-ray baggage has become important in security inspection. However, the training of threat detectors often requires extensive, well-annotated images, which are hard to procure, especially for rare contraband items. In this paper, a few-shot SVM-constraint threat detection model, named FSVM is proposed, which aims at detecting unseen contraband items with only a small number of labeled samples. Rather than simply finetuning the original model, FSVM embeds a derivable SVM layer to back-propagate the supervised decision information into the former layers. A combined loss function utilizing SVM loss is also created as the additional constraint. We have evaluated FSVM on the public security baggage dataset SIXray, performing experiments on 10-shot and 30-shot samples under three class divisions. Experimental results show that compared with four common few-shot detection models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g., X-ray parcels).

Red phosphorus is a promising anode for Na-ion batteries but suffers from large volume change upon cycling. Here the authors show a red-phosphorus-impregnated carbon nanofiber design in which the sodiated red phosphorus is featured by a “liquid-like” behavior and ultra-stable electrochemical performance is realized.

Long noncoding RNAs (lncRNAs) have been demonstrated to play key roles in various biological processes. However, the contributions of lncRNAs to Seneca Valley virus (SVV) infection and host defense remain largely unknown. In this study, differentially expressed lncRNAs and mRNAs in SVV-infected PK15 cells were detected by genome-wide analysis. A total of 14,127 lncRNAs and 63,562 mRNAs were identified, and 1,780 lncRNAs were differentially expressed. The functional prediction of SVV-induced lncRNAs showed high associations with biological regulation and many immunity-related signaling pathways, including the B-cell receptor pathway, RIG-I-like receptor signaling pathway, and NF-kappa B (NF-κB) signaling pathway. We next screened lncRNAs and target genes related to immune response pathways and further demonstrated their differential expression in SVV-infected PK15 cells. Our study investigated the function of lncRNAs involved in SVV infection and provided new insight into the pathogenic mechanisms of SVV.

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10−3 for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems.

Abstract Recently, ithas been observed that {0,±1}‐ternary codes, which are simply generated from deep features by hard thresholding, tend to outperform {−1,1}‐binary codes in image retrieval. To obtain better ternary codes, the authors for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non‐smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes. For the first time, the authors propose to generate ternary hash codes by jointly learning the codes with deep features via a continuation method. Experiments show that the proposed method outperforms existing methods.

Summary: Flexible sensors are essential for advancing implantable and wearable bioelectronics toward monitoring chemical signals within and on the body. Developing biosensors for monitoring multiple neurotransmitters in real time represents a key in vivo application that will increase understanding of information encoded in brain neurochemical fluxes. Here, arrays of devices having multiple In2O3 nanoribbon field-effect transistors (FETs) were fabricated on 1.4-μm-thick polyethylene terephthalate (PET) substrates using shadow mask patterning techniques. Thin PET-FET devices withstood crumpling and bending such that stable transistor performance with high mobility was maintained over >100 bending cycles. Real-time detection of the small-molecule neurotransmitters serotonin and dopamine was achieved by immobilizing recently identified high-affinity nucleic-acid aptamers on individual In2O3 nanoribbon devices. Limits of detection were 10 fM for serotonin and dopamine with detection ranges spanning eight orders of magnitude. Simultaneous sensing of temperature, pH, serotonin, and dopamine enabled integration of physiological and neurochemical data from individual bioelectronic devices.

Abstract With the development of information technology, the demand for information security is increasing. For more convenient and safer needs, the encryption technology based on biometrics has developed rapidly. Among them, iris technology has become an important research object of information security research due to the stability of iris characteristics and its difficulty in forgery. In this paper, the iris feature encryption technology based on the iris is studied by using the method of deep learning as the feature classification method and the iris feature as the research object. The simulation experiment is carried out by using the common iris database. The results show that the method can greatly improve the consistency of iris encryption and improve the security of encryption and decryption process.

The complete chloroplast (cp) genome of Rosa pricei Hayata has been characterized by reference-based assembly using Illumina paired-end data. The complete cp genome is 156,599 base pairs (bp) in length, containing a large single-copy region (LSC) of 85,732 bp, a small single-copy region (SSC) of 18,749 bp, which are separated by a pair of inverted repeat regions (IRs) of 26,059 bp, a total of 139 genes were predicted from the chloroplast genome, including 87 protein-coding genes, 39 tRNA genes, and eight rRNA genes (four rRNA species). Most genes occur as a single copy while 19 gene species are duplicated. Phylogenetic analysis revealed that R. pricei was resolved as sister to R. multiflora, R. lucieae, and R. maximowicziana.

Background/Aims: Treatments targeting cancer stem cells (CSCs) are most effective cancer therapy, whereas determination of CSCs is challenging. We have recently reported that Lgr5-positive cells are cancer stem cells (CSCs) in human skin squamous cell carcinoma (SCC). Ginsenoside Rh2 (GRh2) has been shown to significantly inhibit growth of some types of cancers, whereas its effects on the SCC have not been examined. Methods: Here, we transduced human SCC cells with lentivirus carrying GFP reporter under Lgr5 promoter. The transduced SCC cells were treated with different doses of GRh2, and then analyzed cell viability by CCK-8 assay and MTT assay. The effects of GRh2 on Lgr5-positive CSCs were determined by fow cytometry and by tumor sphere formation. Autophagy-associated protein and β-catenin were measured by Western blot. Expression of short hairpin small interfering RNA (shRNA) for Atg7 and β-catenin were used to inhibit autophagy and β-catenin signaling pathway, respectively, as loss-of-function experiments. Results: We found that GRh2 dose-dependently reduced SCC viability, possibly through reduced the number of Lgr5-positive CSCs. GRh2 increased autophagy and reduced β-catenin signaling in SCC cells. Inhibition of autophagy abolished the effects of GRh2 on β-catenin and cell viability, while increasing β-catenin abolished the effects of GRh2 on autophagy and cell viability. Conclusion: Taken together, our data suggest that GRh2 inhibited SCC growth, possibly through reduced the number of Lgr5-positive CSCs. This may be conducted through an interaction between autophagy and β-catenin signaling.