A General Synthesis Method for Patterning PEDOT toward Wearable Electronics and Bioelectronics.

The conductive polymer poly-3,4-ethylenedioxythiophene (PEDOT), recognized for its superior electrical conductivity and biocompatibility, has become an attractive material for developing wearable technologies and bioelectronics. Nevertheless, the complexities associated with PEDOT's patterning synthesis on diverse substrates persist despite recent technological progress. In this study, we introduce a novel deep eutectic solvent (DES)-induced vapor phase polymerization technique, facilitating nonrestrictive patterning polymerization of PEDOT across diverse substrates. By controlling the quantity of DES adsorbed per unit area on the substrates, PEDOT can be effectively patternized on cellulose, wood, plastic, glass, and even hydrogels. The resultant patterned PEDOT exhibits numerous benefits, such as an impressive electronic conductivity of 282 S·m-1, a high specific surface area of 5.29 m2·g-1, and an extensive electrochemical stability range from -1.4 to 2.4 V in a phosphate-buffered saline. To underscore the practicality and diverse applications of this DES-induced approach, we present multiple examples emphasizing its integration into self-supporting flexible electrodes, neuroelectrode interfaces, and precision circuit repair methodologies.

Promising cellulose-based functional gels for advanced biomedical applications: A review.

Novel biomedical materials provide a new horizon for the diagnosis/treatment of diseases and tissue repair in medical engineering. As the most abundant biomass polymer on earth, cellulose is characterized by natural biocompatibility, good mechanical properties, and structure-performance designability. Owing to these outstanding features, cellulose as a biomacromolecule can be designed as functional biomaterials via hydrogen bonding (H-bonding) interaction or chemical modification for human tissue repair, implantable tissue organs, and controlling drug release. Moreover, cellulose can also be used to construct medical sensors for monitoring human physiological signals. In this study, the structural characteristics, functionalization approaches, and advanced biomedical applications of cellulose are reviewed. The current status and application prospects of cellulose and its functional materials for wound dressings, drug delivery, tissue engineering, and electronic skin (e-skin) are discussed. Finally, the key technologies and methods used for designing cellulosic biomaterials and broadening their application prospects in biomedical fields are highlighted.

The impact of submandibular glands protection on xerostomia as monitored by diffusion-weighted imaging in nasopharyngeal carcinoma patients.

PURPOSE: To determine the impact of sparing submandibular glands (SMGs) on alleviating xerostomia and the functional dynamics of the irradiated parotid glands (PGs) and sublingual glands (SLGs) by diffusion-weighted imaging. METHODS: 97 participants underwent 9 rounds of DWI scans before IC (pre-IC), pre-radiation (pre-RT), the midpoint of radiation (mid-RT), the end of radiation (post-RT), 1, 3, 6, 9, 12 (12m-RT) months following radiation. Apparent diffusion coefficient of SMGs (ADCSMG), PGs (ADCPG), and SLGs (ADCSLG), xerostomia questionnaire scores (XQ), and saliva flow rate measures under unstimulated (uSFR) and stimulated condition (sSFR) were documented. RESULTS: ADCPG, ADCSMG, ADCSLG, and XQ showed a rapid increase with a top at 3m-RT followed by regression, whereas uSFR and sSFR had the reverse trend. The change rate of ADC correlated with the dose to PGs, SMGs, and SLGs, as well as uSFR, sSFR, and XQ scores (p < 0.05 for all, except for uSFR with ADCPG (p = 0.063)). Maingroup for ADCPG, uSFR, and sSFR were significant (p values were 0.028, 0.000, 0.000 respectively); ADCPG in SMG sparing group was lower while uSFR, and sSFR were higher than those in the SMG-unsparing group. Simplegroup for ADCSMG, ADCSLG (all p < 0.05 from mid-RT to 12m-RT), and XQ (all p < 0.001 at mid-, 6m-, 9m-, and 12m-RT) were significant; ADCSMG, ADCSLG, and XQ were lower in the SMG-sparing group. CONCLUSIONS: SMG protection has a great impact on the functional retention of PGs and SLGs, resulting in alleviating xerostomia and improving quality of life. TRIAL REGISTRATION: The clinical trial was also registered with the Chinese Clinical Study Registry (registered number: ChiCTR1900024328, Date: July 6, 2019; URL: https://www.chictr.org.cn/showproj.aspx?proj=40726 ).

A novel fixed-time stability lemma and its application in the stability analysis of BAM neural networks.

In this paper, we put forward an interesting fixed-time (FXT) stability lemma, which is based on a whole new judging condition, and the minimum upper bound for the stability start time is obtained. In the new FXT stability lemma, the mathematical relation between the upper bound of the stability start time and the system parameters is very simple, and the judgment condition only involves two system parameters. To indicate the usability of the new FXT stability lemma, we utilize it to study the FXT stability of a bidirectional associative memory neural network (BAMNN) with bounded perturbations via sliding mode control. To match the developed FXT stability lemma, novel sliding mode state variables and a two-layer sliding mode controller are designed. According to the developed FXT stability lemma, the perturbed BAMNN can achieve FXT stability under the devised sliding mode controller. The upper bound of the stability start time can be calculated easily by virtue of the control parameters, and the sufficient conditions guaranteeing that the perturbed BAMNN can achieve FXT stability have also been derived. Last, we provide some confirmatory simulations.

A novel camptothecin derivative, ZBH-01, exhibits superior antitumor efficacy than irinotecan by regulating the cell cycle.

BACKGROUND: Irinotecan (CPT-11) is a classic chemotherapeutic agent that plays an important role in the clinical treatment of metastatic colon cancer and other malignant tumors. We previously designed a series of novel irinotecan derivatives. In this study, we select one representative, ZBH-01, to investigate its sophisticated antitumor mechanism in colon tumor cells. METHODS: The cytotoxic activity of ZBH-01 on colon cancer cells was evaluate by MTT or Cell Counting Kit-8 (CCK8) assay, 3D and xenograft model. The inhibitory effect of ZBH-01 on TOP1 was detected by DNA relaxation assay and Immuno Complex of Ezyme (ICE) bioassay. The molecular mechanism of ZBH-01 was explored by Next-Generation Sequencing (NGS), bioinformatics analyses, flow cytometry, qRT-PCR, and western blot etc. RESULTS: ZBH-01 can induce obvious DNA damage and has superior antitumor activity against colon cancer cells compared to CPT-11 and SN38 (7-Ethyl-10-hydroxy camptothecin, the in vivo active form of CPT-11) both in vivo and in vitro. Its inhibitory effect on topoisomerase I (TOP1) was also comparable with these two control drugs. There are a much larger number of 842 downregulated and 927 upregulated mRNAs in ZBH-01 treatment group than that in the controls. The most significantly enriched KEGG pathways for these dysregulated mRNAs were DNA replication, the p53 signaling pathway, and the cell cycle. After constructing a protein-protein interaction (PPI) network and screening out a prominent cluster, 14 involved in the cell cycle process was identified. Consistently, ZBH-01 induced G0/G1 phase arrest in colon cancer cells, while CPT-11/SN38 caused S phase arrest. The initiation of apoptosis by ZBH-01 was also superior to CPT-11/SN38, followed by the increased expression of Bax, active caspase 3, and cleaved-PARP, and decreased expression of Bcl-2. Additionally, CCNA2 (cyclin A2), CDK2 (cyclin-dependent kinase 2), and MYBL2 (MYB proto-oncogene like 2) might be involved in the G0/G1 cell cycle arrest induced by ZBH-01. CONCLUSIONS: ZBH-01 can be an antitumor candidate drug for preclinical study in the future.

Impact of simplicial complexes on epidemic spreading in partially mapping activity-driven multiplex networks.

Over the past decade, the coupled spread of information and epidemic on multiplex networks has become an active and interesting topic. Recently, it has been shown that stationary and pairwise interactions have limitations in describing inter-individual interactions , and thus, the introduction of higher-order representation is significant. To this end, we present a new two-layer activity-driven network epidemic model, which considers the partial mapping relationship among nodes across two layers and simultaneously introduces simplicial complexes into one layer, to investigate the effect of 2-simplex and inter-layer mapping rate on epidemic transmission. In this model, the top network, called the virtual information layer, characterizes information dissemination in online social networks, where information can be diffused through simplicial complexes and/or pairwise interactions. The bottom network, named as the physical contact layer, denotes the spread of infectious diseases in real-world social networks. It is noteworthy that the correspondence among nodes between two networks is not one-to-one but partial mapping. Then, a theoretical analysis using the microscopic Markov chain (MMC) method is performed to obtain the outbreak threshold of epidemics, and extensive Monte Carlo (MC) simulations are also carried out to validate the theoretical predictions. It is obviously shown that MMC method can be used to estimate the epidemic threshold; meanwhile, the inclusion of simplicial complexes in the virtual layer or introductory partial mapping relationship between layers can inhibit the spread of epidemics. Current results are conducive to understanding the coupling behaviors between epidemics and disease-related information.

Adversarial and Random Transformations for Robust Domain Adaptation and Generalization.

Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve accuracy and robustness. However, due to the non-differentiable properties of image transformations, searching algorithms such as reinforcement learning or evolution strategy have to be applied, which are not computationally practical for large-scale problems. In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained. To further improve the accuracy and robustness with adversarial examples, we propose a differentiable adversarial data augmentation method based on spatial transformer networks (STNs). The combined adversarial and random-transformation-based method outperforms the state-of-the-art on multiple DA and DG benchmark datasets. Furthermore, the proposed method shows desirable robustness to corruption, which is also validated on commonly used datasets.

Elucidating the Mechanism of Self-Healing in Hydrogel-Lead Halide Perovskite Composites for Use in Photovoltaic Devices.

Since the emergence of organometal halide perovskite (OMP) solar cells, there has been growing interest in the benefits of incorporating polymer additives into the perovskite precursor, in terms of both photovoltaic device performance and perovskite stability. In addition, there is interest in the self-healing properties of polymer-incorporated OMPs, but the mechanisms behind these enhanced characteristics are still not fully understood. Here, we study the role of poly(2-hydroxyethyl methacrylate) (pHEMA) in improving the stability of methylammonium lead iodide (MAPI, CH3NH3PbI3) and determine a mechanism for the self-healing of the perovskite-polymer composite following exposure to atmospheres of differing relative humidity, using photoelectron spectroscopy. Varying concentrations of pHEMA (0-10 wt %) are incorporated into a PbI2 precursor solution during the conventional two-step fabrication method for producing MAPI. It is shown that the introduction of pHEMA results in high-quality MAPI films with increased grain size and reduced PbI2 concentration compared with pure MAPI films. Devices based on pHEMA-MAPI composites exhibit an improved photoelectric conversion efficiency of 17.8%, compared with 16.5% for a pure MAPI device. pHEMA-incorporated devices are found to retain 95.4% of the best efficiency after ageing for 1500 h in 35% RH, compared with 68.5% achieved from the pure MAPI device. The thermal and moisture tolerance of the resulting films is investigated using X-ray diffraction, in situ X-ray photoelectron spectroscopy (XPS), and hard XPS (HAXPES). It is found that exposing the pHEMA films to cycles of 70 and 20% relative humidity leads to a reversible degradation, via a self-healing process. Angle-resolved HAXPES depth-profiling using a non-destructive Ga Kα source shows that pHEMA is predominantly present at the surface with an effective thickness of ca. 3 nm. It is shown using XPS that this effective thickness reduces with increasing temperature. It is found that N is trapped in this surface layer of pHEMA, suggesting that N-containing moieties, produced during reaction with water at high humidity, are trapped in the pHEMA film and can be reincorporated into the perovskite when the humidity is reduced. XPS results also show that the inclusion of pHEMA enhances the thermal stability of MAPI under both UHV and 9 mbar water vapor pressure.

Development and validation of a nomogram for prediction of recovery from moderate-severe xerostomia post-radiotherapy in nasopharyngeal carcinoma patients.

PURPOSE: Aim to create and validate a comprehensive nomogram capable of accurately predicting the transition from moderate-severe to normal-mild xerostomia post-radiotherapy (postRT) in patients with nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: We constructed and internally verified a prediction model using a primary cohort comprising 223 patients who were pathologically diagnosed with NPC from February 2016 to December 2019. LASSO regression model was used to identify the clinical factors and relevant variables (the pre-radiotherapy (XQ-preRT) and immediate post-radiotherapy (XQ-postRT) xerostomia questionnaire scores, as well as the mean dose (Dmean) delivered to the parotid gland (PG), submandibular gland (SMG), sublingual gland (SLG), tubarial gland (TG), and oral cavity). Cox proportional hazards regression analysis was performed to develop the prediction model, which was presented as a nomogram. The models' performance with regard to calibration, discrimination, and clinical usefulness was evaluated. The external validation cohort comprised 78 patients. RESULTS: Due to better discrimination and calibration in the training cohort, age, gender, XQ-postRT, and Dmean of PG, SMG, and TG were included in the individualized prediction model (C-index of 0.741 (95% CI:0.717 to 0.765). Verification of the nomogram's performance in internal and external validation cohorts revealed good discrimination (C-index of 0.729 (0.692 to 0.766) and 0.736 (0.702 to 0.770), respectively) and calibration. Decision curve analysis revealed that the nomogram was clinically useful. The 12-month and 24-month moderate-severe xerostomia rate was statistically lower in the SMG-spared arm (28.4% (0.230 to 35.2) and 5.2% (0.029 to 0.093), respectively) than that in SMG-unspared arm (56.8% (0.474 to 0.672) and 12.5% (0.070 to 0.223), respectively), with an HR of 1.84 (95%CI: 1.412 to 2.397, p = 0.000). The difference in restricted mean survival time for remaining moderate-severe xerostomia between the two arms at 24 months was 5.757 months (95% CI, 3.863 to 7.651; p = 0.000). CONCLUSION: The developed nomogram, incorporating age, gender, XQ-postRT, and Dmean to PG, SMG, and TG, can be used for predicting recovery from moderate-severe xerostomia post-radiotherapy in NPC patients. Sparing SMG is highly important for the patient's recovery.

Bio-Inspired Multiscale Design for Strong and Tough Biological Ionogels.

Structure design provides an effective solution to develop advanced soft materials with desirable mechanical properties. However, creating multiscale structures in ionogels to obtain strong mechanical properties is challenging. Here, an in situ integration strategy for producing a multiscale-structured ionogel (M-gel) via ionothermal-stimulated silk fiber splitting and moderate molecularization in the cellulose-ions matrix is reported. The produced M-gel shows a multiscale structural superiority comprised of microfibers, nanofibrils, and supramolecular networks. When this strategy is used to construct a hexactinellid inspired M-gel, the resultant biomimetic M-gel shows excellent mechanical properties including elastic modulus of 31.5 MPa, fracture strength of 6.52 MPa, toughness reaching 1540 kJ m-3 , and instantaneous impact resistance of 3.07 kJ m-1 , which are comparable to those of most previously reported polymeric gels and even hardwood. This strategy is generalizable to other biopolymers, offering a promising in situ design method for biological ionogels that can be expanded to more demanding load-bearing materials requiring greater impact resistance.

A Three-Dimensional ResNet and Transformer-Based Approach to Anomaly Detection in Multivariate Temporal-Spatial Data.

Anomaly detection in multivariate time series is an important problem with applications in several domains. However, the key limitation of the approaches that have been proposed so far lies in the lack of a highly parallel model that can fuse temporal and spatial features. In this paper, we propose TDRT, a three-dimensional ResNet and transformer-based anomaly detection method. TDRT can automatically learn the multi-dimensional features of temporal-spatial data to improve the accuracy of anomaly detection. Using the TDRT method, we were able to obtain temporal-spatial correlations from multi-dimensional industrial control temporal-spatial data and quickly mine long-term dependencies. We compared the performance of five state-of-the-art algorithms on three datasets (SWaT, WADI, and BATADAL). TDRT achieves an average anomaly detection F1 score higher than 0.98 and a recall of 0.98, significantly outperforming five state-of-the-art anomaly detection methods.

HTRA1 expression is associated with immune-cell infiltration and survival in breast cancer.

Background: High temperature requirement A1 (HTRA1), a member of the HTRA family, is a serine peptidase involved in many crucial bioprocesses such as proliferation, mitochondrial homeostasis, apoptosis, and protein quality control. It also plays an important role in the development of various tumors. However, the potential role and mechanisms of action of HTRA1 in breast cancer (BRCA) remain unclear. We conducted a bioinformatics-based study to investigate HTRA1 expression in BRCA alongside its associations with immune-cell infiltrates and survival outcomes. Methods: The expression of HTRA1 in BRCA samples was analyzed using RNAseq datasets from The Cancer Genome Atlas and Gene Expression Omnibus. R software was employed to assess the relationship between HTRA1 expression and clinicopathological characteristics, tumor-infiltrating immune cells, and immunity-associated biomarkers in BRCA. MethSurv and cBioPortal database were utilized to evaluate DNA methylation and genovariation within the HTRA1 DNA. Receiver operating characteristic curves, Kaplan-Meier analysis, and Cox regression were performed to estimate the impact of HTRA1 on diagnosis, prognosis, and response to chemotherapy in BRCA. Results: HTRA1 expression was significantly downregulated in BRCA tissues compared to adjacent normal breast tissue controls. Differentially expressed genes associated with HTRA1 expression primarily enriched in cell proliferation pathways. Furthermore, altered HTRA1 expression significantly correlated with patient age, tumor histological type, T stage, progesterone receptor/estrogen receptor status, and PAM50 subtype of BRCA. Both positive and negative associations were observed between HTRA1 levels and the abundance of different types of immune cells, as well as immune biomarkers, including resting mast cells, follicular helper T cells, PD-L1, p53, and Ki67. Low HTRA1 expression was related with pathological complete response in luminal B BRCA patients undergoing chemotherapy. Additionally, lower HTRA1 expression in BRCA was associated with inferior overall survival and relapse-free survival. Conclusions: HTRA1 expression is associated with immune-cell infiltration, response to chemotherapy, and survival outcomes in BRCA. HTRA1 has the potential to serve as a promising biomarker and therapeutic target moving forward.

[Clinical study on the treatment of premature ejaculation with Nailifu Spray].

OBJECTIVE: To observe the effect of Nailifu Spray on the treatment of premature ejaculation. METHODS: A total of 90 patients were included in this study from January 1, 2022 to January 1, 2023. Nailifu spray was used to spray the surface of penile skin once a day, 2 sprays per session for 4 weeks.And the patients' premature ejaculation diagnostic tool (PEDT) scores, intravaginal ejaculation latency time (IELT), and international index of erectile function-5 (IIEF-5) scores were collected before and after treatment, respectively. RESUTS: The median (P25,P75) PEDT scores was 16.0(15.0,18.0) scores before treatment and 10.0(10.0,10.0) scores after treatment. The median (P25,P75) of IELT was 20.0 (10.0,30.0) s before treatment and 240.0 (180.0,300.0) s after treatment. The median (P25,P75) of IIEF-5 scores was 21.0 (21.0,22.0) scores before treatment and 21.0 (21.0,21.0) scores after treatment. Compared with baseline levels, IELT was significantly longer and PEDT scores were significantly lower, with statistically significant differences. No significant changes in IIEF-5 scores were seen. CONCLUSION: Nailifu spray treatment of premature ejaculation is accurate and effective, worthy of clinical promotion.

Coupled spreading between information and epidemics on multiplex networks with simplicial complexes.

The way of information diffusion among individuals can be quite complicated, and it is not only limited to one type of communication, but also impacted by multiple channels. Meanwhile, it is easier for an agent to accept an idea once the proportion of their friends who take it goes beyond a specific threshold. Furthermore, in social networks, some higher-order structures, such as simplicial complexes and hypergraph, can describe more abundant and realistic phenomena. Therefore, based on the classical multiplex network model coupling the infectious disease with its relevant information, we propose a novel epidemic model, in which the lower layer represents the physical contact network depicting the epidemic dissemination, while the upper layer stands for the online social network picturing the diffusion of information. In particular, the upper layer is generated by random simplicial complexes, among which the herd-like threshold model is adopted to characterize the information diffusion, and the unaware-aware-unaware model is also considered simultaneously. Using the microscopic Markov chain approach, we analyze the epidemic threshold of the proposed epidemic model and further check the results with numerous Monte Carlo simulations. It is discovered that the threshold model based on the random simplicial complexes network may still cause abrupt transitions on the epidemic threshold. It is also found that simplicial complexes may greatly influence the epidemic size at a steady state.

Optimal Security Protection Strategy Selection Model Based on Q-Learning Particle Swarm Optimization.

With the rapid development of Industrial Internet of Things technology, the industrial control system (ICS) faces more and more security threats, which may lead to serious risks and extensive damage. Naturally, it is particularly important to construct efficient, robust, and low-cost protection strategies for ICS. However, how to construct an objective function of optimal security protection strategy considering both the security risk and protection cost, and to find the optimal solution, are all significant challenges. In this paper, we propose an optimal security protection strategy selection model and develop an optimization framework based on Q-Learning particle swarm optimization (QLPSO). The model performs security risk assessment of ICS by introducing the protection strategy into the Bayesian attack graph. The QLPSO adopts the Q-Learning to improve the local optimum, insufficient diversity, and low precision of the PSO algorithm. Simulations are performed on a water distribution ICS, and the results verify the validity and feasibility of our proposed model and the QLPSO algorithm.

WSNEAP: An Efficient Authentication Protocol for IIoT-Oriented Wireless Sensor Networks.

With the development of the Industrial Internet of Things (IIoT), industrial wireless sensors need to upload the collected private data to the cloud servers, resulting in a large amount of private data being exposed on the Internet. Private data are vulnerable to hacking. Many complex wireless-sensor-authentication protocols have been proposed. In this paper, we proposed an efficient authentication protocol for IIoT-oriented wireless sensor networks. The protocol introduces the PUF chip, and uses the Bloom filter to save and query the challenge-response pairs generated by the PUF chip. It ensures the security of the physical layer of the device and reduces the computing cost and communication cost of the wireless sensor side. The protocol introduces a pre-authentication mechanism to achieve continuous authentication between the gateway and the cloud server. The overall computational cost of the protocol is reduced. Formal security analysis and informal security analysis proved that our proposed protocol has more security features. We implemented various security primitives using the MIRACL cryptographic library and GMP large number library. Our proposed protocol was compared in-depth with related work. Detailed experiments show that our proposed protocol significantly reduces the computational cost and communication cost on the wireless sensor side and the overall computational cost of the protocol.

Deep Reinforcement Learning: A Survey.

Deep reinforcement learning (DRL) integrates the feature representation ability of deep learning with the decision-making ability of reinforcement learning so that it can achieve powerful end-to-end learning control capabilities. In the past decade, DRL has made substantial advances in many tasks that require perceiving high-dimensional input and making optimal or near-optimal decisions. However, there are still many challenging problems in the theory and applications of DRL, especially in learning control tasks with limited samples, sparse rewards, and multiple agents. Researchers have proposed various solutions and new theories to solve these problems and promote the development of DRL. In addition, deep learning has stimulated the further development of many subfields of reinforcement learning, such as hierarchical reinforcement learning (HRL), multiagent reinforcement learning, and imitation learning. This article gives a comprehensive overview of the fundamental theories, key algorithms, and primary research domains of DRL. In addition to value-based and policy-based DRL algorithms, the advances in maximum entropy-based DRL are summarized. The future research topics of DRL are also analyzed and discussed.

A comparative study of functional MRI in predicting response of regional nodes to induction chemotherapy in patients with nasopharyngeal carcinoma.

Purpose: To identify and compare the value of functional MRI (fMRI) in predicting the early response of metastatic cervical lymph nodes (LNs) to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) patients. Methods: This prospective study collected 94 metastatic LNs from 40 consecutive NPC patients treated with IC from January 2021 to May 2021. Conventional diffusion-weighted imaging, diffusion kurtosis imaging, intravoxel incoherent motion, and dynamic contrast-enhanced magnetic resonance imaging were performed before and after IC. The parameter maps apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), mean kurtosis (MK), Dslow, Dfast, perfusion fraction (PF), Ktrans, Ve, and Kep) of the metastatic nodes were calculated by the Functool postprocessing software. All LNs were classified as the responding group (RG) and non-responding group (NRG) according to Response Evaluation Criteria in Solid Tumors 1.1. The fMRI parameters were compared before and after IC and between the RG and the NRG. The significant parameters are fitted by logistic regression analysis to produce new predictive factor (PRE)-predicted probabilities. Logistic regression analysis and receiver operating characteristic (ROC) curves were performed to further identify and compare the efficacy of the parameters. Results: After IC, the mean values of ADC, MD, and Dslow significantly increased, while MK, Dfast, and Ktrans values decreased dramatically, while no significant difference was detected in Ve and Kep. Compared with NRG, PF-pre and Ktrans-pre values in the RG were higher statistically. The areas under the ROC for the pretreatment PF, Ktrans, and PRE were 0.736, 0.722, and 0.810, respectively, with the optimal cutoff value of 222 × 10-4, 934 × 10-3/min, and 0.6624, respectively. Conclusions: The pretreatment fMRI parameters PF and Ktrans showed promising potential in predicting the response of the metastatic LNs to IC in NPC patients. Clinical Trial Registration: This study was approved by the ethics board of the Chinese PLA General Hospital, and registered on 30 January 2021, in the Chinese Clinical Trial Registry; http://www.chictr.org.cn/showproj.aspx?proj=121198, identifier (ChiCTR2100042863).

A Prediction Model of Human Resources Recruitment Demand Based on Convolutional Collaborative BP Neural Network.

This paper presents an in-depth study and analysis of the prediction model of force resource recruitment demand using a convolutional neural network combined with a BP neural network algorithm. BP neural network technology is introduced to be applied to enterprise management talent assessment activities. Using BP neural network has strong parallel processing characteristics, as well as unique adaptive learning and feedback adjustment capabilities while combining the traditional enterprise talent assessment system, to build a business management talent assessment model based on BP neural network technology, to circumvent the possible influence of subjective factors in talent assessment, reduce assessment errors, and improve the accuracy and validity of the assessment. The first layer of convolutional layers may only extract some low-level features such as edges, lines, and corners, and more layers of the network can iteratively extract more complex features from low-level features. The constructed applicant reputation evaluation model based on multiplicative long- and short-term recurrent neural network and the hybrid project recommendation model based on conditional variational self-encoder were experimented on Freelancer's dataset for effectiveness, respectively, and the experimental results showed that the proposed employer hiring decision model, reputation analysis model, and applicant project recommendation model have more reliable performance compared with the existing models. The research results achieve more efficient matching of labor supply and demand in the online labor market and provide technical support for the online labor market platform to realize personalized, intelligent, and accurate services for both employers and applicants.

Application of Diffusion Kurtosis Imaging in Evaluating Acute Xerostomia in Nasopharyngeal Carcinoma Treated With Induction Chemotherapy Plus Concurrent Chemoradiotherapy.

Purpose: The aim of this study was to identify the efficacy of diffusion kurtosis imaging (DKI) in tracking and monitoring the dynamic change of parotid glands (PGs), submandibular glands (SMGs), sublingual glands (SLGs), and acute xerostomia in nasopharyngeal carcinoma (NPC) patients treated with induction chemotherapy (IC) plus concurrent chemoradiotherapy (CCRT). Methods: The prospective study recruited 42 participants treated with IC+CCRT. All patients underwent DKI scanning six times: before IC, before RT, in the middle of the RT course, immediately after RT, and 1 and 3 months post-RT. Mean diffusion coefficient (MD) and mean kurtosis (MK) of PG, SMG, SLG, saliva flow rate measured under resting (uSFR) and stimulated condition (sSFR), and xerostomia questionnaire (XQ) scores were recorded. Results: At each time point, sSFR was significantly higher than uSFR (p < 0.05 for all). MD of the salivary glands and XQ scores increased over time while MK, uSFR, and sSFR decreased. After IC, the significant differences were detected in MD and MK of bilateral SMG and MK of the left SLG (p < 0.05 for all), but not in MD and MK of PG, uSFR, sSFR, and XQ scores. After RT, sSFR at 1m-RT decreased significantly (p = 0.03) while no significant differences were detected in uSFR and XQ scores. Moderate-strong correlations were detected in ΔMD-PG-R%, ΔMK-PG-R%, ΔMD-PG-L%, ΔMK-PG-L%, ΔMD-SMG-R%, ΔMK-SMG-R%, ΔMD-SMG-L%, ΔMK-SMG-L%, and ΔMD-SLG-R%, with correlation coefficients (p < 0.05 for all) ranging from 0.401 to 0.714. ΔuSFR% was correlated with ΔMD-SMG% (p = 0.01, r = -0.39), ΔMD-SLG% (p < 0.001, r = -0.532), and ΔMK-SMG% (p < 0.001, r = -0.493). ΔsSFR% correlated with ΔMD-PG% (p = 0.001, r = -0.509), ΔMD-SMG% (p = 0.015, r = -0.221), and ΔMK-PG% (p < 0.001, r = 0.524). ΔXQ% was only correlated with ΔMK-PG% (p = 0.004, r = 0.433). Conclusion: DKI is a promising tool for tracking and monitoring the acute damage of PG, SMG, and SLG induced by IC+CCRT in NPC patients.