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Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

DNA barcodes, short and unique DNA sequences, play a crucial role in sample identification when processing many samples simultaneously, which helps reduce experimental costs. Nevertheless, the low quality of long-read sequencing makes it difficult to identify barcodes accurately, which poses significant challenges for the design of barcodes for large numbers of samples in a single sequencing run. Here, we present a comprehensive study of the generation of barcodes and develop a tool, PRO, that can be used for selecting optimal barcode sets and demultiplexing. We formulate the barcode design problem as a combinatorial problem and prove that finding the optimal largest barcode set in a given DNA sequence space in which all sequences have the same length is theoretically NP-complete. For practical applications, we developed the novel method PRO by introducing the probability divergence between two DNA sequences to expand the capacity of barcode kits while ensuring demultiplexing accuracy. Specifically, the maximum size of the barcode kits designed by PRO is 2,292, which keeps the length of barcodes the same as that of the official ones used by Oxford Nanopore Technologies (ONT). We validated the performance of PRO on a simulated nanopore dataset with high error rates. The demultiplexing accuracy of PRO reached 98.29% for a barcode kit of size 2,922, 4.31% higher than that of Guppy, the official demultiplexing tool. When the size of the barcode kit generated by PRO is the same as the official size provided by ONT, both tools show superior and comparable demultiplexing accuracy.

Abstract Background The aberrant expression of phosphofructokinase-platelet (PFKP) plays a crucial role in the development of various human cancers by modifying diverse biological functions. However, the precise molecular mechanisms underlying the role of PFKP in head and neck squamous cell carcinoma (HNSCC) are not fully elucidated. Methods We assessed the expression levels of PFKP and c-Myc in tumor and adjacent normal tissues from 120 HNSCC patients. A series of in vitro and in vivo experiments were performed to explore the impact of the feedback loop between PFKP and c-Myc on HNSCC progression. Additionally, we explored the therapeutic effects of targeting PFKP and c-Myc in HNSCC using Patient-Derived Organoids (PDO), Cell Line-Derived Xenografts, and Patients-Derived Xenografts. Results Our findings indicated that PFKP is frequently upregulated in HNSCC tissues and cell lines, correlating with poor prognosis. Our in vitro and in vivo experiments demonstrate that elevated PFKP facilitates cell proliferation, angiogenesis, and metastasis in HNSCC. Mechanistically, PFKP increases the ERK-mediated stability of c-Myc, thereby driving progression of HNSCC. Moreover, c-Myc stimulates PFKP expression at the transcriptional level, thus forming a positive feedback loop between PFKP and c-Myc. Additionally, our multiple models demonstrate that co-targeting PFKP and c-Myc triggers synergistic anti-tumor effects in HNSCC. Conclusion Our study demonstrates the critical role of the PFKP/c-Myc positive feedback loop in driving HNSCC progression and suggests that simultaneously targeting PFKP and c-Myc may be a novel and effective therapeutic strategy for HNSCC.

Biomass-related airborne fine particulate matter (PM2.5) is an important risk factor for chronic obstructive pulmonary disease (COPD). Macrophage polarization has been reported to be involved in PM2.5-induced COPD, but the dynamic characteristics and underlying mechanism of this process remain unclear. Our study established a PM2.5-induced COPD mouse model and revealed that M2 macrophages predominantly presented after 4 and 6 months of PM2.5 exposure, during which a notable increase in MMP12 was observed. Single cell analysis of lung tissues from COPD patients and mice further revealed that M2 macrophages were the dominant macrophage subpopulation in COPD, with MMP12 being involved as a hub gene. In vitro experiments further demonstrated that PM2.5 induced M2 polarization and increased MMP12 expression. Moreover, we found that PM2.5 increased IL-4 expression, STAT6 phosphorylation and nuclear translocation. Nuclear pSTAT6 then bound to the MMP12 promoter region. Furthermore, the inhibition of STAT6 phosphorylation effectively abrogated the PM2.5-induced increase in MMP12. Using a coculture system, we observed a significantly reduced level of E-cadherin in alveolar epithelial cells cocultured with PM2.5-exposed macrophages, while the decrease in E-cadherin was reversed by the addition of an MMP12 inhibitor to the co-culture system. Taken together, these findings indicated that PM2.5 induced M2 macrophage polarization and MMP12 upregulation via the IL-4/STAT6 pathway, which resulted in alveolar epithelial barrier dysfunction and excessive extracellular matrix (ECM) degradation, and ultimately led to COPD progression. These findings may help to elucidate the role of macrophages in COPD, and suggest promising directions for potential therapeutic strategies.

In response to the problem of poor performance of classical fusion methods in combining multiple shortwave channel information due to the complexity and harshness of shortwave channels and prominent conflicts among channel in-formation, a shortwave multi-channel information fusion method based on the DSmT evidence discounting theory was pro-posed.The DSmT evidence discounting theory was employed as the fusion framework to achieve fusion, inference, and de-cision-making of multiple shortwave channel information.Firstly, the relative envelope standard deviation of the OFDM frame was normalized, and the discount weight was calculated through the relative envelope standard deviation and the dis-count factor.Then, the bit soft information extraction function was used to obtain the bit probability, which was quantita-tively assigned to the basic belief assignment function as evidence.Finally, the PCR6 fusion rule was applied to fuse, infer, and make decisions on the soft information from multiple channels.Simulation experiments comparing the proposed method with classical fusion methods under various channel conditions and modulation schemes indicate significant gains in bit error rate and packet error.

A gas chromatography tandem mass spectrometry (GC–MS/MS) assay was developed for the simultaneous detection of 64 anabolic androgenic steroids (AAS). Urine is extracted with tertbutyl methyl ether under alkaline conditions, purified with C18, and derived through silanization, Detection is carried out in dynamic multiple reaction monitoring (dMRM) mode, The overall recoveries ranged from 74.31 % to 119.66 %, The relative standard deviation is

ObjectiveJianPiHuaTan Formula (JPHTF), a traditional Chinese medicine (TCM), has been utilized as an adjunctive therapy for colorectal cancer (CRC). The study aims to evaluate the potential clinical benefits of JPHTF and its effectiveness in inhibiting tumor growth.Methods300 stage II/III CRC patients and 412 advanced CRC patients were enrolled to verify the clinical value of JPHTF in CRC treatment. Furthermore, CRC patient-derived xenograft (PDX) mice were utilized to investigate the regulatory mechanisms of JPHTF.ResultsJPHTF significantly improved abdominal distension, shortness of breath, drowsiness, loss of appetite, sleep, and tiredness in stage II/III CRC patients, thereby improving their quality of life. Simultaneously, JPHTF served as a supportive therapy in extending the overall survival (OS) of stage IV CRC patients with RAS/RAF mutations undergoing chemotherapy. Additionally, JPHTF effectively impeded tumor progression in CRC PDX models with RAS mutation, accompanied by a reduction in tumor cell content in the JPHTF group. Transcriptomic analysis revealed the involvement of the Hippo and Hedgehog signaling pathways in JPHTF-mediated CRC inhibition. Furthermore, mice in the JPHTF group exhibited increased immune cell infiltration.ConclusionThese findings suggested that JPHTF may inhibits tumor growth in CRC with RAS mutation by modulating RAS/RAF downstream signaling pathways, specifically the Hippo and Hedgehog signaling, leading to increased immune cell infiltration.

The extruded AZ80+0.4% Ce magnesium alloy was twisted in the temperature range of 300 - 380 °C by using a Gleeble 3500 thermal simulation test machine with a torsion unit. The deformed cylindrical specimens were cooled at a cooling rate of 10 °C/s or 0.1 °C/s, respectively, and aged at 170 °C. The microstructure analysis results showed that the grain size decreased with increasing specimen radial position from center (SRPC), and that the strong initial basal texture of the extruded magnesium alloy was weakened. Both continuous and discontinuous dynamic recrystallization mechanisms were involved in contributing to the grain refinement for all specimens investigated. And a novel extension twinning induced dynamic recrystallization mechanism was proposed for specimen deformed at 300 °C. For the specimens deformed at 300 °C and 340 °C followed by a slow cooling rate (0.1 °C/s), precipitates of various shapes (β-Mg17Al12), with the dominant precipitates being on the grains boundaries, appeared on the surface section. For specimen deformed at 380 °C, lamellar precipitates (LPS) in the interiors of the grains were predominant. After aging, the LPS still dominated for specimens twisted at 380 °C; however, the LPS gradually decreased with decreasing deformation temperatures from 380 °C to 300 °C. Dynamically precipitated β, especially those decorating the grain boundaries, changed the competition pictures for the LPS and precipitates of other shapes after aging. Interestingly, LPS dominated the areas for the center section of the specimens after aging regardless of deformation temperatures. Low temperature deformation with high SRPC followed by rapid cooling rate increased the micro hardness of the alloy after aging due to refined grain, reduced precipitates size, decreased lamellar spacing as well as strain hardening.

Abstract Background Metastasis is the primary cause of recurrence and death in patients with papillary thyroid carcinoma (PTC). LncRNA ACTA2-AS1, a long non-coding RNA, acts as a tumor suppressor in multiple types of human malignancies, while the role of ACTA2-AS1 in PTC metastasis remains unclear. Methods The ACTA2-AS1 expression in PTC tissues was analyzed. The sponged roles of ACTA2-AS1 via miR-4428/KLF9 axis were identified using starBase tool. The function of ACTA2-AS1 in PTC was performed with in vitro and in vivo experiments. The correlation between DNA methylation and mRNA expressions of these gene in the TCGA dataset was explored. Results ACTA2-AS1 expression was downregulated in PTC tissues without metastasis and further decreased in PTC tissues with lymph node metastasis compared with that in normal tissues. Functionally, the overexpression of ACTA2-AS1 inhibited the growth, proliferation, and invasion of PTC cells, whereas its depletion exerted opposite effect. In vivo, ACTA2-AS1 expression inhibited PTC metastasis. Furthermore, ACTA2-AS1 acted as a competing endogenous RNA for miR-4428, thereby positively regulating the expression of miR-4428 target gene, KLF9. Finally, miR-4428 overexpression enhanced invasive potential of PTC cells and significantly weakened the effects of ACTA2-AS1 on promotion and inhibition of KLF9 expression as well as invasive ability of PTC cells, respectively. In the TCGA dataset, the methylation level of ACTA2-AS1 was significantly correlated with its mRNA expression (r = 0.21, p = 2.1 × e−6). Conclusions Our findings demonstrate that ACTA2-AS1 functions as a tumor suppressor in PTC progression at least partly by regulating the miR-4428-dependent expression of KLF9. Graphical abstract

Predicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learning prediction methods.To address this issue, a model-data dual-driven bidirectional gated recurrent unit (BiGRU) network for short-term prediction of MUF was proposed.On the model-driven, a large-scale dataset generated by the classical MUF prediction model was used as the model-driven training set, and a preliminary network was obtained after joint learning of the 2D CNN and the BiGRU network.On the data-driven, the preliminary network was trained twice using a small-scale measured dataset to obtain the final network CNN-BiGRU-NN.The simulation results show that the proposed network has reduced average root mean squared error (RMSE) at both daily and momentary scales compared with the GRU network, LSTM network and VOACAP model.

Glycation Stress (GS), induced by advanced glycation end-products (AGEs), significantly impacts aging processes. This study introduces a new model of GS of Caenorhabditis elegans by feeding them Escherichia coli OP50 cultured in a glucose-enriched medium, which better simulates human dietary glycation compared to previous single protein–glucose cross-linking methods. Utilizing WormCNN, a deep learning model, we assessed the health status and calculated the Healthy Aging Index (HAI) of worms with or without GS. Our results demonstrated accelerated aging in the GS group, evidenced by increased autofluorescence and altered gene expression of key aging regulators, daf-2 and daf-16. Additionally, we observed elevated pharyngeal pumping rates in AGEs-fed worms, suggesting an addictive response similar to human dietary patterns. This study highlights the profound effects of GS on worm aging and underscores the critical role of computer vision in accurately assessing health status and aiding in the establishment of disease models. The findings provide insights into glycation-induced aging and offer a comprehensive approach to studying the effects of dietary glycation on aging processes.

Abstract DNA barcodes enable Oxford Nanopore sequencing to sequence multiple barcoded DNA samples on a single flow cell. DNA sequences with the same barcode need to be grouped together through demultiplexing. As the number of samples increases, accurate demultiplexing becomes difficult. We introduce HycDemux, which incorporates a GPU-parallelized hybrid clustering algorithm that uses nanopore signals and DNA sequences for accurate data clustering, alongside a voting-based module to finalize the demultiplexing results. Comprehensive experiments demonstrate that our approach outperforms unsupervised tools in short sequence fragment clustering and performs more robustly than current state-of-the-art demultiplexing tools for complex multi-sample sequencing data.

An adaptive tensor train (TT) learning algorithm for the online decomposition problem of high-order tensors in single-aspect streaming model was investigated.Firstly, it was deduced that single-aspect streaming increment only changes the dimension of temporal TT core.Secondly, the forgetting factor and regularization item were introduced to construct the objective function of exponentially weighted least-squares.Finally, the block-coordinate descent learning strategy was used to estimate the temporal and non-temporal TT core tensors respectively.Simulation results demonstrate that the proposed algorithm is validated in terms of increment size, TT-rank, noise and time-varying intensity, the average relative error and operation time are smaller than that of the comparison algorithms.The tensor slice reconstruction ability is superior than that of the comparison algorithms in the video adaptive analysis.

Abstract Background Cocaine is an illegal recreational drug used worldwide, yet little is known about whether cocaine inhalation (smoking/snorting) increases the risk of head and neck cancer (HNC). Methods The analyses were conducted by pooling data from three case–control studies with 1639 cases and 2506 controls from the International Head and Neck Cancer Epidemiology Consortium. Epidemiologic data, including cocaine use histories, were obtained in face‐to‐face interviews. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated using hierarchical logistic regression models. Results Controlling for cumulative tobacco and alcohol use, we observed a weak positive association between cocaine use and HNC (ORever vs. never = 1.35, 95% CI: 0.96, 1.90). In stratified analysis, while we did not detect associations among never tobacco or alcohol users due to the limited sample size, the association with cocaine use was observed among tobacco users and alcohol drinkers. ORs for ever and high cumulative use (>18 times) versus never use were 1.40 (95% CI: 0.98, 2.00) and 1.66 (95% CI: 1.03, 2.69) among tobacco users, and 1.34 (95% CI: 0.93, 1.92) and 1.59 (95% CI: 1.00, 2.51) among alcohol drinkers, respectively. Conclusion In this pooled analysis, we observed a weak positive association between cocaine inhalation and HNC risk. Our findings provide preliminary evidence of the potential carcinogenic effect of cocaine on HNC. Because of study limitations, including limited number of cocaine users, confounding, and heterogeneity across studies, future investigations will require larger studies with more detailed information on cocaine use history.

This paper introduces three types of controllers: a PID-type iterative learning controller, an adaptive iterative learning controller, and an optimal iterative learning controller, and reviews the history and research status of initial shifts rectifying algorithms. Initial state shifts have attracted research attention because they affect both the tracking performance and system stability. This study focuses on the current common initial shifts rectifying methods and analyzes the underlying mechanism in detail. To verify the effectiveness of the presented initial shifts rectifying algorithms, we simulated those using ideal first- and second-order systems. Finally, directions for the future development of iterative learning control (ILC) and some challenging topics related to initial shifts rectifying for ILC are presented. This article aims to introduce recent developments and advances in initial shifts rectifying algorithms and discuss the directions for their further exploration.

Abstract Background Smoking, alcohol consumption, and human papillomavirus (HPV) infection are known risk factors for oral squamous cell carcinoma (OSCC) including SCC of oropharynx (SCCOP) and SCC of oral cavity (SCCOC). Researchers have examined each of these risk factors independently, but few have observed the potential risk of their interaction. This study investigated the interactions among these risk factors and risk of OSCC. Methods Totally 377 patients with newly diagnosed SCCOP and SCCOC and 433 frequency-matched cancer-free controls by age and sex were included. Multivariable logistic regression was performed to calculate ORs and 95% CIs. Results We found that overall OSCC risk was independently associated with smoking (adjusted OR(aOR), 1.4; 95%CI, 1.0–2.0), alcohol consumption (aOR, 1.6; 95%CI, 1.1–2.2), and HPV16 seropositivity (aOR, 3.3; 95%CI, 2.2–4.9), respectively. Additionally, we found that HPV16 seropositivity increased the risk of overall OSCC in ever-smokers (aOR, 6.8; 95%CI, 3.4–13.4) and ever-drinkers (aOR, 4.8; 95%CI, 2.9–8.0), while HPV16-seronegative ever-smokers and ever-drinkers had less than a twofold increase in risk of overall OSCC (aORs, 1.2; 95%CI, 0.8–1.7 and 1.8; 95%CI, 1.2–2.7, respectively). Furthermore, the increased risk was particularly high for SCCOP in HPV16-seropositive ever-smokers (aOR, 13.0; 95%CI, 6.0–27.7) and in HPV16-seropositive ever-drinkers (aOR, 10.8; 95%CI, 5.8–20.1), while the similar increased risk was not found in SCCOC. Conclusion These results suggest a strong combined effect of HPV16 exposure, smoking, and alcohol on overall OSCC, which may indicate a strong interaction between HPV16 infection and smoking and alcohol consumption, particularly for SCCOP.

Summary: Solar-driven thermochemical conversion of H2O and CO2 into sustainable fuels, based on redox cycle, provides a promising path for alternative energy, as it employs the solar energy as high-temperature heat supply and adopts H2O and CO2 as initial feedstock. This review describes the sustainable fuels production system, including a series of physical and chemical processes for converting solar energy into chemical energy in the form of sustainable fuels. Detailed working principles, redox materials, and key devices are reviewed and discussed to provide systematic and in-depth understanding of thermochemical fuels production with the aid of concentrated solar power technology. In addition, limiting factors affecting the solar-to-fuel efficiency are analyzed; meanwhile, the improvement technologies (heat recovery concepts and designs) are summarized. This study therefore sets a pathway for future research works based on the current status and demand for further development of such technologies on a commercial scale.

Aiming at the problem that short-wave point-to-point communication was not reliable enough to meet the actual needs, a wide area cooperative resource allocation algorithm based on shortwave communication access network was proposed.The reliability of the shortwave communication was improved by invoking multiple stations to coordinately guarantee users from different channels.The resource allocation problem was decomposed into two sub-problems of channel and station matching and station allocation.Firstly, the maximum expected success rate matching model was established to match the channel and the station, and the stations allocated to the channel were combined into different guarantee schemes.Then, the fuzzy analytic hierarchy process and the entropy weight method were used to obtain the subjective and objective scores of the scheme, and the optimal scheme was selected through fusion of evidence reasoning.The results show that the proposed algorithm can effectively improve the success rate of shortwave communication and has good adaptability.

A link-establishment method based on short-wave digital channelized receiver was proposed for the traditional automatic link establishment technology in short-wave communications with the low accuracy of frequency selection, low efficiency of link establishment, and low utilization of resources, etc.The parallel receiving mechanism for full frequency band in short-wave communications was realized by using digital channelization.An integrated waveform of communication and detection was designed to deal with the time redundancy and equipment compatibility problems caused by waveform switching during link establishment.A fast link-establishment method was designed, to achieve a fast and robust connection in complex electromagnetic environments.The results show that the channelized structure can enhance the receiving performance of the link-establishment system, the link-establishment method has obvious advantages, channel parameter estimation method has a high estimation accuracy compared to the traditional link-establishment method and the applicability of the proposed method in practical short-wave channel environment is verified.

Based on the deforming technique of severe plastic deformation (SPD), the grain refinement of a Mg-9Gd-3Y-2Zn-0.5Zr alloy treated with decreasing temperature reciprocating upsetting-extrusion (RUE) and its influence on the mechanical properties and wear behavior of the alloy were studied. The RUE process was carried out for 4 passes in total, starting at 0 °C and decreasing by 10 °C for each pass. The results showed that as the number of RUE passes increased, the grain refinement effect was obvious, and the second phase in the alloy was evenly distributed. Room temperature tensile properties of the alloy and the deepening of the RUE degree showed a positive correlation trend, which was due to the grain refinement, uniform distribution of the second phase and texture weakening. And the microhardness of the alloy showed that the microhardness of RUE is the largest in 2 passes. The change in microhardness was the result of dynamic competition between the softening effect of DRX and the work hardening effect. In addition, the wear resistance of the alloy showed a positive correlation with the degree of RUE under low load conditions. When the applied load was higher, the wear resistance of the alloy treated with RUE decreased compared to the initial state alloy. This phenomenon was mainly due to the presence of oxidative wear on the surface of the alloy, which could balance the positive contribution of severe plastic deformation to wear resistance to a certain extent.

The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has increased rapidly in the United States, driven by rising human papillomavirus (HPV) infections in the U.S. population. HPV-positive OPSCC patients have a better prognosis than HPV-negative patients. To gain insights into the unique biology of HPV(+) OPSCC that may contribute to its clinical behaviors, we performed a multi-stage epigenome-wide methylation profiling of leukocyte and tumor DNA in OPSCC patients and compared the methylation levels of CpG sites between HPV(+) and HPV(−) OPSCC patients. We identified and validated a significantly differentially methylated region (DMR) of 1,355 bp encompassing non-coding RNA 886 (nc886) gene and its promoter region. Nc886 is hypermethylated in both leukocytes and tumor DNA of HPV(+) OPSCC patients. Homozygous knockout of nc886 by CRISPR-Cas9 in head and neck cell lines was lethal, but nc886 could be knocked out on the background of protein kinase R (PKR) knockout. Our data suggest that HPV induces nc886 hypermethylation, and nc886 acts as both a viral sensor and a tumor sensor in OPSCC patients and contribute to the better prognosis of HPV(+) OPSCC patients. Nc886 may become a therapeutic target in OPSCC.

Aiming at the problems of difficult to extract effective feature information and the slow convergence speed of the underdetermined matrix estimation, an underdetermined matrix estimation algorithm of instantaneous mixtures based on tensor analysis was proposed to overcome the constraint of signal sparsity.In the proposed algorithm, the symmetric third-order tensor was constructed via the autocovariance matrix of segmentation sub-block, which was compressed into a kernel tensor to reduce the size of the data.An enhanced line search technology was applied to speed up the convergence of alternating least squares method, and the factor matrix was used as the measure of the mixing matrix estimation, but the selection of the number of segmentation sub-blocks was an open problem.Experimental results demonstrate that the proposed algorithm outperforms the sparse transformation method and the traditional high-order statistical method in handling the underdetermined mixing matrix estimation.

Aiming at the problems of poor anti-noise performance and high computational complexity of the existing orthogonal time sequency multiplexing (OTSM) equalization methods, a low-complexity iterative rake equalizer based on maximum ratio combining (MRC) was proposed.The main idea was to use MRC to iteratively extract and coherently combine the received multipath components of the transmitted symbols in the delay-time domain to improve the signal-to-noise ratio of the combined signal.In order to speed up the convergence of the MRC iterative algorithm, a single-tap time-frequency equalizer was designed to provide an initial estimate, and then combined with an external error correction code to further improve the bit error performance.The simulation results show that the performance of the iterative rake equalizer based on MRC is significantly better than that of the LMMSE linear equalizer, and the performance and computational complexity are greatly improved compared with the currently widely used Gauss-Seidel (GS) iterative equalizer.

Summary: Background: Lymphocyte telomere length (LTL)-related genetic variants may modulate LTL and affect recurrence of squamous cell carcinoma of the oropharynx (SCCOP). Methods: A total of 1013 patients with incident SCCOP were recruited and genotyped for 16 genome-wide association study (GWAS)-identified TL-related polymorphisms. Of these patients, 489 had tumour HPV16 status determination. Univariate and multivariate analyses were performed to evaluate associations. Findings: Of the 16 TL-related polymorphisms, four were significantly associated with LTL: rs1920116, rs3027234, rs6772228, and rs11125529, and the patients with putatively favourable genotypes had approximately 1.5–3 times the likelihood of shorter LTL compared with patients with the corresponding risk genotypes. Moreover, patients with one to four favourable genotypes of the four combined polymorphisms had approximately 3–11 times the likelihood of shorter LTL compared with patients with no favourable genotype. The four LTL-related polymorphisms were significantly associated with approximately 40% reduced risk (for favourable genotypes) or doubled risk (for risk genotypes) of recurrence, and similar but more pronounced associations were observed in patients with tumour HPV16-positive SCCOP. Similarly, patients with one to four risk genotypes had significantly approximately 2.5–4 times increased recurrence risk compared with patients with no risk genotype, and similar but more pronounced associations were observed in patients with tumour HPV16-positive SCCOP. Interpretation: Four LTL-related polymorphisms individually or jointly modify LTL and risk of recurrence of SCCOP, particularly HPV-positive SCCOP. These LTL-related polymorphisms could have potential to further stratify patients with HPV-positive SCCOP for individualized treatment and better survival. Funding: Not applicable.

In order to quickly restore emergency communication services for large-scale post-disaster users, a distributed intellicise coverage optimization architecture based on multi-agent reinforcement learning (RL) was proposed, which could address the significant differences and dynamics of communication services caused by a large number of access users, and the difficulty of expansion caused by centralized algorithms.Specifically, a distributed k-sums clustering algorithm considering service differences of users was designed in the network characterization layer, which could make each unmanned aerial vehicle base station (UAV-BS) adjust the local networking natively and simply, and obtain states of cluster center for multi-agent RL.In the trajectory control layer, multi-agent soft actor critic (MASAC) with distributed-training-distributed-execution structure was designed for UAV-BS to control trajectory as intelligent nodes.Furthermore, ensemble learning and curriculum learning were integrated to improve the stability and convergence speed of training process.The simulation results show that the proposed distributed k-sums algorithm is superior to the k-means in terms of average load efficiency and clustering balance, and MASAC based trajectory control algorithm can effectively reduce communication interruptions and improve the spectrum efficiency, which outperforms the existing RL algorithms.

At present, alloys are affected by various stress states during their deformation process. Various stresses can facilitate alloy deformation at the same time, and the influence of these stresses on material deformation is difficult to clarify. It is essential to clarify the difference in the deformation mechanism of an alloy in a single stress state In this work, the internal deformation mechanism of the Mg-13Gd-4Y–2Zn-0.5Zr alloy under two stress states at different deformation temperatures was studied by compression and torsion experiments. The results show that the grain is slightly deformed under shear stress deformation, and the long period stacking ordered (LPSO) phases of different forms show smaller kink angles, fewer kink times, and larger relative width ratios of the kink bands. Under compressive stress deformation, the grain is obviously compressed along the CD direction and presents a streamline, and the LPSO phase is severely kinked. Different stress states have a great influence on dislocation slip, and the shear stress state can easily provoke the initiation of non-basal slip. Non-basal slip has difficulty starting under compressive stress, which leads to dislocation packing. The dynamic recrystallization (DRX) fraction of compressive stress deformation is higher than that of shear stress deformation. Shear and compressive stress deformation alloys activate different kinds of twinning. In shear stress deformed alloys, tensile twins usually occur in grains with a small basal slip Schmid factor (SF). However, compression twins are activated in the compressive stress deformation alloy and occur in grains with larger SF values. When the slip is not fully coordinated with the deformation, kinking and twining occur to coordinate the deformation.

Abstract Genetic susceptibility for xerostomia, a common sequela of radiotherapy and chemoradiotherapy for head and neck cancer, is unknown. Therefore, to identify genetic variants associated with moderate to severe xerostomia, we conducted a GWAS of 359 long-term oropharyngeal cancer (OPC) survivors using 579,956 autosomal SNPs. Patient-reported cancer treatment-related xerostomia was assessed using the MD Anderson Symptom Inventory. Patient response was dichotomized as moderate to severe or none to mild symptoms. In our study, 39.2% of OPC survivors reported moderate to severe xerostomia. Our GWAS identified eight SNPs suggestively associated with higher risk of moderate to severe xerostomia in six genomic regions (2p13.3, rs6546481, Minor Allele (MA) = A, ANTXR1, P = 4.3 × 10–7; 5p13.2–p13.1, rs16903936, MA = G, EGFLAM, P = 5.1 × 10–6; 4q21.1, rs10518156, MA = G, SHROOM3, P = 7.1 × 10–6; 19q13.42, rs11882068, MA = G, NLRP9, P = 1.7 × 10–5; 12q24.33, rs4760542, MA = G, GLT1D1, P = 1.8 × 10–5; and 3q27.3, rs11714564, MA = G, RTP1, P = 2.9 × 10–5. Seven SNPs were associated with lower risk of moderate to severe xerostomia, of which only one mapped to specific genomic region (15q21.3, rs4776140, MA = G, LOC105370826, a ncRNA class RNA gene, P = 1.5 × 10–5). Although our small exploratory study did not reach genome-wide statistical significance, our study provides, for the first time, preliminary evidence of genetic susceptibility to xerostomia. Further studies are needed to elucidate the role of genetic susceptibility to xerostomia.

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

IntroductionExposure to fine particulate matter (PM), especially PM2.5, can induce various adverse health effects in populations, including diseases and premature death, but the mechanism of its toxicity is largely unknown.MethodsWater-soluble components of PM2.5 (WS-PM2.5) were collected in the north of China in winter, and combined in two groups with the final concentrations of 94 μg/mL (CL group, AQI ≤ 100) and 119 μg/mL (CH group, 100 < AQI ≤ 200), respectively. The acute and long-term toxic effects of WS-PM2.5 samples were evaluated in several aspects such as development, lifespan, healthspan (locomotion behavior, heat stress tolerance, lipofucin). DAF mutants and genes were applied to verify the action of IIS pathway in WS-PM2.5 induced-effects. RNA-Sequencing was performed to elucidate the molecular mechanisms, as well as ROS production and Oil red O staining were also served as means of mechanism exploration.ResultsBody length and lifespan were shortened by exposure to WS-PM2.5. Healthspan of nematodes revealed adverse effects evaluated by head thrash, body bend, pharyngeal pump, as well as intestinal lipofuscin accumulation and survival time under heat stress. The abbreviated lifespan of daf-2(e1370) strain and reduced expression level of daf-16 and hsp-16.2 indicated that IIS pathway might be involved in the mechanism. Thirty-five abnormally expressed genes screened out by RNA-Sequencing techniques, were functionally enriched in lipid/lipid metabolism and transport, and may contribute substantially to the regulation of PM2.5 induced adverse effects in nematodes.ConclusionWS-PM2.5 exposure induce varying degrees of toxic effects, such as body development, shorten lifespan and healthspan. The IIS pathway and lipid metabolism/transport were disturbed by WS-PM2.5 during WS-PM2.5 exposure, suggesting their regulatory role in lifespan determination.

Abstract Background Acute-on-chronic liver failure (ACLF) is a comprehensive syndrome characterized by an acute deterioration of liver function and high short-term mortality rates in patients with chronic liver disease. Whether plasma soluble urokinase plasminogen activator receptor (suPAR) is a suitable biomarker for the prognosis of patients with ACLF remains unknown. Method A prospective cohort of 282 patients with ACLF from three hospitals in China was included. 88.4% of the group was hepatitis B virus-related ACLF (HBV-related ACLF). Cox regression was used to assess the impact of plasma suPAR and other factors on 30- and 90-day mortality. Reactive oxygen species (ROS) production were detected to explore the role of suPAR in regulating neutrophil function in HBV-related ACLF. Result There was no difference in plasma suPAR levels between HBV-related and non-HBV-related ACLF. Patients with clinical complications had higher suPAR levels than those without these complications. A significant correlation was also found between suPAR and prognostic scores, infection indicators and inflammatory cytokines. Cox’s regression multivariate analysis identified suPAR ≥ 14.7 ng/mL as a predictor for both day 30 and 90 mortality (Area under the ROC curve: 0.751 and 0.742 respectively), independent of the MELD and SOFA scores in patients with ACLF. Moreover, we firstly discovered suPAR enhanced neutrophil ROS production under E.coli stimulation in patients with HBV-related ACLF. Conclusions suPAR was a useful independent biomarker of short-term outcomes in patients with ACLF and might play a key role in the pathogenesis. Trial registration CNT, NCT02965560.

Abstract Background Natural variations derived from both evolutionary selection and genetic recombination, presume to have important functions to respond to various abiotic stresses, which could be used to improve drought tolerance via genomic selection. Results In the present study, the NAC-encoding gene of ZmNAC080308 was cloned and sequenced in 199 inbred lines in maize. Phylogenetic analysis showed that ZmNAC080308 is closely clusteredinto the same group with other well-known NAC genes responding to improve drought tolerance. In total, 86 SNPs and 47 InDels were identified in the generic region of ZmNAC080308, 19 of these variations were associated with GY (grain yield) in different environments. Nine variations in the 5’-UTR region of ZmNAC080308 are closely linked, they might regulate the gene expression and respond to improve GY under drought condition via Sp1-mediated transactivation. Two haplotypes (Hap1 and Hap2) identified in the, 5’-UTR region using the nine variations, and Hap2 containing insertion variants, exhibited 15.47 % higher GY under drought stress condition. Further, a functional marker was developed to predict the drought stress tolerance in a US maize inbred line panel. Lines carrying Hap2 exhibited > 10 % higher GY than those carrying Hap1 under drought stress condition. In Arabidopsis, overexpression ZmNAC080308 enhanced drought tolerance. Conclusions ZmNAC080308 is an important gene responding to drought tolerance, a functional marker is developed for improving maize drought tolerance by selecting this gene.

RNA molecules can adopt stable secondary and tertiary structures, which are essential in mediating physical interactions with other partners such as RNA binding proteins (RBPs) and in carrying out their cellular functions. In vivo and in vitro experiments such as RNAcompete and eCLIP have revealed in vitro binding preferences of RBPs to RNA oligomers and in vivo binding sites in cells. Analysis of these binding data showed that the structure properties of the RNAs in these binding sites are important determinants of the binding events; however, it has been a challenge to incorporate the structure information into an interpretable model. Here we describe a new approach, RNANetMotif, which takes predicted secondary structure of thousands of RNA sequences bound by an RBP as input and uses a graph theory approach to recognize enriched subgraphs. These enriched subgraphs are in essence shared sequence-structure elements that are important in RBP-RNA binding. To validate our approach, we performed RNA structure modeling via coarse-grained molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28B. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs.

In order to improve the thermal performance of natural draft wet cooling tower under crosswind, a three-dimensional numerical model for heat and mass transfer of cooling towers was established. The comparison with the design parameters of the cooling tower shows that this model satisfies the requirements of the thermal performance computation of cooling tower. According to the numerical calculation of the cooling tower and the analysis of the temperature, humidity and velocity field under various crosswinds velocities, a scheme of partition water distribution of cooling tower was proposed to deal with the effect of crosswind. Using this scheme, the influence of various spray water density ratio k on the thermal performance of cooling tower were researched, and the results show that the heat transfer performance of the cooling tower can be effectively improved under a certain comparing with uniform water distribution. When the cross wind speed is in the range of 2-6 m/s and this partition water distribution scheme is applied, maximum reduction of the outlet water temperature will reach 1.01K comparing with uniform water distribution.

To study the effects of different types of commercially available drinks/beverages on neurobehavior using the model organism C. elegans, and critically review their potential health hazards. Eighteen kinds of beverages from the supermarket were randomly selected and grouped into seven categories namely functional beverage, tea beverage, plant protein beverage, fruit juice beverage, dairy beverage, carbonated beverage and coffee beverage. The pH value, specific gravity and osmotic pressure were also examined. The L4 stage N2 worms were exposed to different concentration of tested beverages (0, 62.5, 125, 250 and 500 µL/mL) for 24 h to measure the survival rate and locomotory behavior such as head thrashing, body bending as well as pharyngeal pumping. All the 18 beverages tested did not induce any visible lethal effects in the nematodes. However, exposure to different types of tested beverages exhibited different effects on the behavioral ability of C. elegans: (1) sports functional beverage and herbal tea drink accelerated the head thrashing and body bending of nematodes when compared to the control group (P

Abstract Interferon-alpha (IFN-α) and nucleot(s)ide analogs (NAs) are first-line drugs for the treatment of chronic hepatitis B virus (HBV) infections. Generally, NAs target the reverse transcription of HBV pregenomic RNA, but they cannot eliminate covalently-closed-circular DNA (cccDNA). Although effective treatment with NAs can dramatically decrease HBV proteins and DNA loads, and even promote serological conversion, cccDNA persists in the nucleus of hepatocytes due to the lack of effective anti-cccDNA drugs. Of the medications currently available, only IFN-α can potentially target cccDNA. However, the clinical effects of eradicating cccDNA using IFN-α in the hepatocytes of patients with HBV are not proficient as well as expected and are not well understood. Herein, we review the anti-HBV mechanisms of IFN-α involving cccDNA modification as the most promising approaches to cure HBV infection. We expect to find indications of promising areas of research that require further study to eliminate cccDNA of HBV in patients.

To improve the low accuracy of ionospheric frequency forecasting, poor HF communication connectivity, and the severe impact of observational outliers in engineering on forecast accuracy, a short-term prediction method for oblique detection ionospheric MUF based on robust Kalman filtering was proposed.By studying the variation law of the measured MUF data and the causes as well as characteristics of observation errors, and exploiting the ionospheric reference model as a priori information, the state estimation equation of the Kalman filter model was improved.The cost function mechanism was introduced to realize the measurement update of the forecast state through Huber-M estimation, which reduced the influence of the observation outliers on the forecast results and improves the robustness of the proposed method.The simulation results show that the proposed method can effectively suppress the adverse effects caused by the observation outliers and has good robustness and stability.

With the commercialization of 5G, in order to recognize QAM signals, one of the main modulation modes in 5G communication systems, this paper put forward the BP-BSA network model based on bird swarm algorithm (BSA) and BP neural network. Firstly, the instantaneous features and high-order cumulants were selected as the appropriate feature statistics by analyzing the characteristics of MQAM signals. After that, the structure of the BP neural network model was determined, and the initial parameters of the BP neural network were optimized using BSA to accelerate the convergence speed of the network. Finally, the features processed with signal to noise ratio (SNR) disorder were used to train and test the BP neural network model. The BP-BSA network model proposed in this paper applies the bird swarm algorithm to the field of modulation recognition for the first time. And the simulation results show that the recognition accuracies of 16, 32, 64, 128, 256QAM signals in the SNR range of -5 dB to 20 dB all reach above 98%. Compared with the same type algorithms, the algorithm proposed in this paper has good recognition performance.

In the production process of steel pipe, the quenching is of significance to improve the quality of steel pipe. Therefore, the dynamic thermal behavior of the steel pipe in quenching furnace is studied in this work. The coupled conduction-radiation heat transfer model of steel pipe in walking beam quenching furnace is established. The thermal contact resistance, radiative heat transfer between the steel pipe and the tooth groove, the rotation process and the heating process of steel pipe are considered in this model. The effects of the rotation conditions, tooth groove temperature and size of the steel pipe on thermal behavior of steel pipe are investigated thoroughly. It can be found that keeping the rotation direction of the steel pipe unchanged can reduce the section temperature difference of steel pipe; When the loading of steel pipes is stopped for a period of time, the section temperature difference of the first steel pipe entering the furnace is very large; The large-size steel pipe usually has a large section temperature difference in the initial heating process, which means a lower temperature of tooth grooves in contact with the steel pipe during the initial heating process is required. This work can provide valuable reference for the improvement of quenching quality of steel pipe and on-line supervisory control of the production process.

The current study, using a systematic-literature-search approach, was intended to conduct hazard identification for dehydroacetic acid (DHA) and sodium dehydroacetate (DHA-S) which are currently used as food additives in China. DHA and DHA-S belong to the category of low toxic chemicals in terms of acute toxicity, mainly causing adverse effects in the central nervous system. Its subacute, subchronic and chronic toxicity mainly include significant reduction of body weight and food intake as well as anticoagulant effect. Reproductive development toxicity result show they can decrease body weight and inhibit bone development in offspring. According to the hazard identification result, oral intake of dehydroacetic acid and its sodium salt can produce a variety of toxic effects above certain doses. Further study is needed to determine the acceptable daily intake of DHA and its sodium salt and provide scientific evidence for safely use of these compounds as food additives.

Abstract Background Protein phosphorylation by kinases plays crucial roles in various biological processes including signal transduction and tumorigenesis, thus a better understanding of protein phosphorylation events in cells is fundamental for studying protein functions and designing drugs to treat diseases caused by the malfunction of phosphorylation. Although a large number of phosphorylation sites in proteins have been identified using high-throughput phosphoproteomic technologies, their specific catalyzing kinases remain largely unknown. Therefore, computational methods are urgently needed to predict the kinases that catalyze the phosphorylation of these sites. Results We developed KSP, a new algorithm for predicting catalyzing kinases for experimentally identified phosphorylation sites in human proteins. KSP constructs a network based on known protein-protein interactions and kinase-substrate relationships. Based on the network, it computes an affinity score between a phosphorylation site and kinases, and returns the top-ranked kinases of the score as candidate catalyzing kinases. When tested on known kinase-substrate pairs, KSP outperforms existing methods including NetworKIN, iGPS, and PKIS. Conclusions We developed a novel accurate tool for predicting catalyzing kinases of known phosphorylation sites. It can work as a complementary network approach for sequence-based phosphorylation site predictors.

In this paper, two iterative learning control methods are proposed for the different high-order systems with arbitrary initial shifts. The tracking errors caused by nonzero initial shifts are easily detected when applying conventional learning algorithms. But this defect is overcome through applying a step-by-step rectifying controller with initial rectifying action introduced in a small interval. It demonstrates the improvement of tracking performance and shows the robustness with respect to the stochastic initial shifts. Finally, simulation results are presented to illustrate the effectiveness of the stated algorithms.

In this paper, a discussion is made on the consensus tracking control by iterative learning method for high-order nonlinear multi-agent systems. Among them, all agents with initial state errors are enabled to perform a given repetitive task over a finite interval. The method proposed can achieve consensus tracking through a series of initial shifts correction actions. In the process of tracking, this algorithm rectifies the initial error of the state $x_{n}$ of each agent at first, then the error of $x_{n-1}$ , and so on. All of these rectifying actions are finished in a specified interval. Furthermore, the algorithm has shown effective in the improvement of tracking performance through simulation.

Quasi-dry CO2 fracturing technology is a new CO2 fracturing technology that combines liquid CO2 fracturing (dry CO2 fracturing) and water-based fracturing. It uses a liquid CO2 system containing a small amount of water-based fracturing fluid to carry sand, and it is characterized by sand blending at normal pressure, convenient preparation, the integrated application of resistance reduction and sand carrying, and no dedicated closed sand blender requirement. We developed a self-crosslinking emulsion-type water-based fracturing fluid (ZJL-1), which contained ionic bonds, hydrogen bonds, van der Waals forces, and hydrophobic associations, for quasi-dry CO2 fracturing, and the comprehensive properties of the ZJL-1 fracturing fluid were evaluated. The results showed that the ZJL-1 fracturing fluid had obvious viscoelastic characteristics, a heat loss rate of less than 10% at 200 °C, a good thermal stability, sufficient rheology under high temperature and high shear conditions, and a good thermal stability. The resistance reduction rate reached 70%, which demonstrates a good resistance reduction performance. Compared with conventional guar fracturing fluid, ZJL-1 can carry more sand and has a lower core damage rate. The on-site use of quasi-dry fracturing showed that optimizing the mixing ratio of liquid CO2 fracturing fluid and ZJL-1 fracturing fluid effectively enhanced oil and gas recovery. This can be used to optimize quasi-dry fracturing and can be used as a reference.

With the change of graduate education from quantity expansion to quality promotion, how to improve the quality of graduate cultivation has aroused wide concern. However, existing scientific quantitative methods tend to investigate the results of graduate training, with a lack of attention to the multidimensional data during the training process. Thus, exploratory analysis of multidimensional data in the graduate training process and accurate grasp of the key process factors affecting graduate academic competence is an indispensable task for achieving the stated goals of graduate education. In this paper, a visual analytic system of graduate training data is proposed to help users implement in-depth analysis based on the graduate training process. First, a questionnaire is designed about the training process to identify multidimensional data timely and accurately. Then, a series of data mining methods are utilized to further detect key factors in the training process, which will be used to make academic predictions for first-year graduates. Meanwhile, an interactive visual analytic system has been developed to help users understand and analyze the key factors affecting the graduate training process. Based on the results of the visual analysis, effective suggestions will be provided for graduate students, supervisors, and university administrators to improve the quality of graduate education.

Abstract High‐throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivity‐coverage iteration strategy and outputting those with significant coverage and exclusivity for both cancers. The associations of the cancer pairs are further evaluated by Fisher's exact test. Being applied to 11 TCGA cancer datasets, ComCovEx identifies 13 significantly associated cancer pairs with plenty of biologically significant common gene modules. The novel results of cancer relationship and common gene modules reveal the relevant pathological basis of different cancer types and provide new clues to diagnosis and drug treatment in associated cancers.

Objective: Recent investigations revealed the relationship between Fusobacterium nucleatum (Fn) infection and colorectal cancer (CRC). However, how the host genes changes contribute to CRC in response to Fn infection remains largely unknown.Materials and methods: In the present study, we aimed to comprehensively analyze microarray data obtained from a Caco-2 infection cell model using integrated bioinformatics analysis and further identify and validate potential candidate genes in Fn-infected Caco-2 cells and CRC specimens.Results: We identified 10 hub genes potentially involved in Fn induced tumor initiation and progression. Furthermore, we demonstrated that the expression of centrosomal protein of 55 kDa (CEP55) is significantly higher in Fn-infected Caco-2 cells. Knocking down of CEP55 could arrest the cell cycle progression and induce apoptosis in Fn-infected Caco-2 cells. The expression of CEP55 was positively correlated with the Fn amount in Fn-infected CRC patients, and these patients with high CEP55expression had an obviously poorer differentiation, worse metastasis and decreased cumulative survival rate.Conclusion: CEP55 plays an important role in Fn-infected colon cancer cell growth and cell cycle progression and could be used as a new diagnostic and prognostic biomarker for Fn-infected CRC.

Crime prediction is crucial for sustainable urban development and protecting citizens’ quality of life. However, there exist some challenges in this regard. First, the spatio-temporal correlations in crime data are relatively complex and are heterogenous in time and space, hence it is difficult to model the spatio-temporal correlation in crime data adequately. Second, crime prediction at fine spatial temporal scales can be applied to micro patrol command; however, crime data are sparse in both time and space, making crime prediction very challenging. To overcome these challenges, based on the deep spatio-temporal 3D convolutional neural networks (ST-3DNet), we devise an improved ST-3DNet framework for crime prediction at fine spatial temporal scales (ST3DNetCrime). The framework utilizes diurnal periodic integral mapping to solve the problem of sparse and irregular crime data at fine spatial temporal scales. ST3DNetCrime can, respectively, capture the spatio-temporal correlations of recent crime data, near historical crime data and distant historical crime data as well as describe the difference in the correlations’ contributions in space. Extensive experiments on real-world datasets from Los Angeles demonstrated that the proposed ST3DNetCrime framework has better prediction performance and enhanced robustness compared with baseline methods. In additon, we verify that each component of ST3DNetCrime is helpful in improving prediction performance.

Based on the characteristics of low-permeability oil and gas reservoirs and the requirements for profile control and water plugging and for water cut decrease and production increase in the high water cut stage of the middle and late exploitation periods, ultrafine temperature-resistant and salt-tolerant swellable particles DS-1 suitable for low permeability oilfields were prepared by introducing N,N-dimethylacrylamide(DMAA) monomers into the 2-acrylamido-2-methylpropanesulfonic acid (AMPS)/acrylamide(AM)/N,N-dimethylbisacrylamide ternary crosslinking system. The median of initial particle size was 22.00 μm, and is compatible with formation pore throats. A static water absorption experiment showed that the particles can still maintain a high swelling ratio after 15 days at a high temperature and high mineralization degree, so they have long-term stability. The physical and chemical properties of the particles were analyzed microscopically using the infrared spectrum method and the scanning electron microscope (SEM) method. A dynamic displacement experiment confirmed that the particles can effectively plug dominant channels of waterflooding, increase the injection pressure, and improve the interlayer and intralayer heterogeneity. In the field experiment, the swellable particles DS-1 were used as a main slug for water plugging operations, and a good water cut decrease and oil production increase effect was obtained.

Abstract Background The complex anatomy of the head and neck creates a formidable challenge for surgical reconstruction. However, good functional reconstruction plays a vital role in the quality of life of patients undergoing head and neck surgery. Precision medical treatment in the field of head and neck surgery can greatly improve the prognosis of patients with head and neck tumors. In order to achieve better shape and function, a variety of modern techniques have been introduced to improve the restoration and reconstruction of head and neck surgical defects. Digital surgical technology has great potential applications in the clinical treatment of head and neck cancer because of its advantages of personalization and accuracy. Case presentation Our department has identified the value of modern digital surgical techniques in the field of head and neck surgery and has explored its utility, including CAD/CAM technology and VR technology. We have achieved good results in the reconstruction of head and neck surgical resection defects. Conclusion In this article, we share five typical cases from the department of head and neck surgery where the reconstruction was performed with the assistance of digital surgical technology.