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Background: Injury of alveolar epithelial cells and capillary endothelial cells is crucial in the pathogenesis of acute lung injury/acute respiratory distress syndrome (ALI/ARDS). Mesenchymal stem cells (MSCs) are a promising cell source for ALI/ARDS treatment. Overexpression of Fork head box protein M1 (FoxM1) facilitates MSC differentiation into alveolar type II (AT II) cells in vitro. Moreover, FoxM1 has been shown to repair the endothelial barrier. Therefore, this study explored whether overexpression of FoxM1 promotes the therapeutic effect of bone marrow-derived MSCs (BMSCs) on ARDS by differentiation of BMSCs into AT II cells or a paracrine mechanism. Methods: A septic ALI model was established in mice by intraperitoneal administration of lipopolysaccharide. The protective effect of BMSCs-FoxM1 on ALI was explored by detecting pathological variations in the lung, total protein concentration in bronchoalveolar lavage fluid (BALF), wet/dry (W/D) lung weight ratio, oxidative stress levels, cytokine levels, and retention of BMSCs in the lung. In addition, we assessed whether FoxM1 overexpression promoted the therapeutic effect of BMSCs on ALI/ARDS by differentiating into AT II cells using SPC−/− mice. Furthermore, the protective effect of BMSCs-FoxM1 on lipopolysaccharide-induced endothelial cell (EC) injury was explored by detecting EC proliferation, apoptosis, scratch wounds, tube formation, permeability, and oxidative stress, and analyzing whether the Wnt/β-catenin pathway contributes to the regulatory mechanism in vitro using a pathway inhibitor. Results: Compared with BMSCs-Vector, treatment with BMSCs-FoxM1 significantly decreased the W/D lung weight ratio, total BALF protein level, lung injury score, oxidative stress, and cytokine levels. With the detected track of BMSCs-FoxM1, we observed a low residency rate and short duration of residency in the lung. Notably, SPC was not expressed in SPC−/− mice injected with BMSCs-FoxM1. Furthermore, BMSCs-FoxM1 enhanced EC proliferation, migration, and tube formation; inhibited EC apoptosis and inflammation; and maintained vascular integrity through activation of the Wnt/β-catenin pathway, which was partially reversed by XAV-939. Conclusion: Overexpression of FoxM1 enhanced the therapeutic effect of BMSCs on ARDS, possibly through a paracrine mechanism rather than by promoting BMSC differentiation into AT II cells in vivo, and prevented LPS-induced EC barrier disruption partially through activating the Wnt/β-catenin signaling pathway in vitro. [ABSTRACT FROM AUTHOR]

The robustness and imperceptibility of the watermarking algorithm are a pair of contradictory properties. When the robustness increases, the imperceptibility decreases. Most strategies proposed in the literature usually concentrate on improving robustness, but few consider the relationship between robustness and imperceptibility. To deal with this problem, a robust blind image watermarking method is proposed in this work, which is composed of a logistic-sine-COupling map, discrete waVElet transform, and appRoximate maximum eigenvalue of Schur decomposition, namely COVER. In the proposed COVER, an objective function and a two-stage search method are designed to determine the optimal embedding coefficient, thus balancing the imperceptibility and robustness of this work. Specifically, the host image is transformed into four sub-bands by discrete wavelet transform, and the low-low sub-band is further separated into non-overlapping blocks which can be calculated to get the approximate maximum eigenvalues (AMEs) of Schur decomposition. Especially, each AME is not contained in the real Schur decomposition, which effectively reduces the computational complexity. Then, the original AMEs of the host image are updated to complete the embedding process according to the binary feature of encrypted watermark and the proposed embedding method. Finally, the watermarked image is obtained. The experimental results show that all peak signal noise ratio values are above 42 dB, structural similarity index measure values are above 0.98, normalized correlation values are above 0.9, and all bit errors rates are less than 0.14. Compared with six state-of-the-art schemes, the proposed COVER has strong robustness, good imperceptibility, large capacity, and low computational complexity. [ABSTRACT FROM AUTHOR]

Convolutional Neural Network-based Predictors (CNNP) have emerged as a viable solution for enhancing global optimization and prediction capabilities in the field of Reversible Data Hiding (RDH). However, they often encounter limitations in data embedding space due to a scarcity of zero-valued points in predicted images. To address these challenges, in this paper, we propose an Optimized CNNP (OCNNP) technique that increases the zero-valued points in predicted images, significantly enhancing the embedding capacity. Additionally, we introduce a novel Lower Surround Background Complexity (LSBC)-based Prediction Error Expansion (PEE) method, which refines the sorting of prediction errors for data embedding, thereby reducing image distortion and improving overall embedding performance. Experimental results demonstrate that our approach markedly improves the Peak Signal-to-Noise Ratio (PSNR) across various embedding capacities compared to existing methods and shows high robustness against various noise types, establishing its superiority in enhancing both the capacity and quality of RDH in complex image distributions. • RDH method integrates OCNNP with LSBC. • OCNNP technique boosts zero-valued points in image predictions. • LSBC-PEE enhances prediction error ranking for embedding. • Proven RDH enhancements in PSNR and reduced distortion. [ABSTRACT FROM AUTHOR]

To improve the performance of the blind image watermarking technique, a novel blind and robust image watermarking scheme in the spatial-domain by applying a two-level partition is proposed in this work. Specifically, the host image is firstly partitioned into non-overlapping 4 × 4 sub-blocks, and the standard deviations of all sub-blocks are computed in the meanwhile. Then, the sub-blocks with lower standard deviation are selected, and each selected block is partitioned into four non-overlapping 2 × 2 sub-blocks. Thereafter, the direct current coefficients of three 2 × 2 sub-blocks (i.e., top-left, top-right, and bottom-left sub-blocks) are computed in the spatial-domain without carrying out the two-dimensional discrete cosine transform. Lastly, utilizing the correlation principle between adjacent 2 × 2 sub-blocks, two bits of watermark are embedded into a 4 × 4 sub-block via adjusting the direct current coefficients of the three 2 × 2 sub-blocks. Experimental results show that the proposed image watermarking scheme is suitable for gray-scale and color images, and possesses a good performance in terms of invisibility and robustness. In particular, all peak signal noise ratios are greater than 42 dB, all structural similarity index measures are more than 0.96, and the normalized correlations are greater than 0.85 under various attacks. [ABSTRACT FROM AUTHOR]

As an adjuvant drug, alprostadil lipid microsphere injection (Lipo-PGE1) is one of the best-selling drugs in China in recent years. However, the off-label use of Lipo-PGE1 is very common. This study aimed to investigate the use of Lipo-PGE1 and evaluate the clinical effects and economic benefits after administrative intervention on inappropriate use of Lipo-PGE1 in neurosurgical patients in a Chinese tertiary hospital. Administrative interventions were implemented from January to December 2018 by reducing the procurement volume of Lipo-PGE1, judging the rationality of medical records, and establishing reward and punishment mechanisms. Administrative interventions significantly decreased prescription rate (49.98% vs 22.49%), utilization (22,311 DDDs vs 8334 DDDs), drug use density (43.52 DDDs/TID vs 15.84 DDDs/TID), total expenditure (3.58 million RMB vs 1.30 million RMB), and average expenditure (2025.04 RMB vs 1466.49 RMB) of Lipo-PGE1. To our delight, these intervention effects were maintained or even better in the 1-year post-intervention period. Moreover, in the intervention and post-intervention phases, the Lipo-PGE1 use for no indications as well as inappropriate drug dose, frequency, menstruum type, combination, and contraindication were markedly reduced. Besides, the mean costs (P < 0.001), and mean duration (P < 0.001) of Lipo-PGE1 were also obviously decreased. The administrative intervention obviously reduced the off-label use of Lipo-PGE1. However, there still remains a number of inappropriate uses of Lipo-PGE1. To further improve the rational use of Lipo-PGE1, combination of administrative intervention and real-time clinical pharmacists intervention should be implemented. [ABSTRACT FROM AUTHOR]

Protecting digital images is crucial, and image encryption schemes based on chaotic systems have been extensively studied where image permutation is a critical process. The updating processes of particle swarm optimization algorithm have the ability to change the rules of particles motion, and it can be employed to permutate image. In this work, an image encryption scheme based on the updating processes of particle swarm optimization algorithm and hyperchaotic system is proposed. Specifically, a key generation mechanism combined with secure hash algorithm 256 hash is first introduced to generate the initial values of hyperchaotic complex Lü system. Then, the plain image is confused by the position and velocity updating processes of particle swarm optimization algorithm. In addition, an adaptive diffusion mechanism is designed and applied to the confused image to obtain the final cipher image. Simulation results and security analysis show that the proposed scheme exhibits good performance of sophisticated dynamic behavior, high sensitivity to key, and effectively resisting the typical cryptanalysis attacks, and provides an alternative to real-time image encryption application. [ABSTRACT FROM AUTHOR]

C2-formyl indoles are important building blocks for the organic synthesis of alkaloids and small molecules with broad biological activities. Generally, its preparation involves both direct formylation of indoles at the activated C2-position with as unstable carbonyl sources and oxidative cleavage of tedious prepared tetrahydro- β -carbolines (TH β Cs) in the harsh conditions. In continuation of our research on the oxidative functionalization of indoles, in this letter, we report that straightforward effectively oxidation of easily available 2-methylindoles bearing C3-heteroatom functional group proceeds regioselectively C2-formyl indoles in good and excellent yields employing an oxidative combination of NCS/DMSO. This methodology presents a reasonably broad substrate scope and excellent functional group tolerance, thus enabling the preparation of highly versatile building blocks susceptible to further functionalization. An additional feature of this approach includes high efficiency, ease of operation, facile purification, and short reaction time, making it a promising strategy for organic synthesis and drug discovery. To create your abstract, type over the instructions in the template box below.Fonts or abstract dimensions should not be changed or altered. [Display omitted] [ABSTRACT FROM AUTHOR]

The security of chaotic cryptographic system can be theoretically evaluated by using conventional statistical tests and numerical simulations, such as the character frequency test, entropy test, avalanche test and SP 800-22 tests. However, when the cryptographic algorithm operates on a cryptosystem, the leakage information such as power dissipation, electromagnetic emission and time-consuming can be used by attackers to analyze the secret keys, namely the Side Channel Analysis (SCA) attack. In this paper, a cryptanalysis method is proposed for evaluating the security of a chaotic block cryptographic system from a hardware perspective by utilizing the Template Attacks (TAs). Firstly, a chaotic block cryptographic system is described briefly and implemented based on an Atmel XMEGA microcontroller. Then the TA using a multivariate Gaussian model is introduced. In order to reduce computational complexity and improve the efficiency of TA, the Hamming weight is used in this work to model power consumption traces. The proposed TA method has the following advantages including (a) using the sum of difference to select points of interest of traces, (b) using a data processing method to minimize the influences on power information modeling from the redundant sampling points, and (c) all the traces are aligned precisely before establishing the templates. Experimental results show that the TA can be used to attack the chaotic cryptographic systems and is more efficient, i.e. ∼ 32% less attack traces than correlation power analysis, when the templates are properly built. [ABSTRACT FROM AUTHOR]

In this paper, a color image encryption method using the memristive hyperchaotic system and deoxyribonucleic acid (DNA) encryption is proposed. First, the pseudo-random sequences are generated by a keystream generation mechanism based on a memristive hyperchaotic system and the plain image. Due to this, the memristive hyperchaotic system has a complex dynamical behavior and is highly sensitive to initial conditions, the proposed keystream generation mechanism is highly random which is also dependent on the plain images. Second, a permutation based on the cycle-shift operation is designed to eliminate the correlations between adjacent pixels in the plain images. Then, the scrambled sequences are processed by DNA encryption to increase the system ability to defense the brute force attacks. Finally, the cipher image is obtained after the diffusion and interaction among red, green and blue components. Experimental analysis and performance comparisons show that the proposed method has high security, good efficiency and strong robustness under different attacks. [ABSTRACT FROM AUTHOR]

As a basic and crucial security requirement for Wireless Sensor Networks (WSNs), authentication is generally used to prevent various communication attacks such as Denial-of-Service (DoS) attack. A novel broadcast authentication framework is proposed in this paper, where an Identity-Based Signature scheme by using the Extended Chaotic Maps (ECM-IBS) is designed to authenticate all broadcast messages and specifically, a chaos-based hash function is used for message authentication in the WSNs. It is implemented using a WSN hardware device of CC2530 and its performance is analyzed under various methods. Performance analysis and experimental results show that the proposed ECM-IBS scheme has a quick signature generation speed, low energy consumption and short verification delay, and can be applied to WSN applications. [ABSTRACT FROM AUTHOR]

• A robust compression-encryption image cryptosystem is proposed. • A modified measurement matrix CADRMM is developed. • A new method of generating diffusion matrix is introduced. • The robustness of the scheme is improved to defend occlusion and noise attacks. • The experimental results demonstrate a good security performance. In terms of Chua's circuit system, compressive sensing (CS) and Haar wavelet, a novel image compression-encryption scheme (CES) is proposed in this paper. Firstly, the plaintext image is decomposed into approximate component and detail components through Haar wavelet. Then the approximate component is diffused by the threshold processing of local binary patterns (LBP) operator-based chaotic sequence which is produced by the combination of Chua's circuit and Logistic map. Next, the Lissajous map is applied to generate the chaos-combined asymptotic deterministic random measurement matrices (CADRMM) which are employed to measure the detail components in different compression ratios. In addition, the combination of mapped approximate and detail components is shuffled by the Logistic map. The experimental results and simulation analysis prove that the proposed cryptosystem is capable of reducing data for transmission and has good security performance under various attacks, especially for the shear and noise attacks. [ABSTRACT FROM AUTHOR]

The securities of chaotic cryptographic systems are widely evaluated by conventional tests such as the character frequency test, entropy test and avalanche test. However, when the chaotic cryptosystem is in operation, side channel information such as power consumption, and electromagnetic radiation is leaked. The side channel information can be used to attack the cryptosystem, e.g. the side channel attack (SCA), which is a threat for the security of chaotic cryptographic systems. This paper proposes a chaotic block cryptographic algorithm that can resist the SCA, with the aim of enhancing the security of chaotic cryptosystems. Masking and hiding mechanisms are used in this work. By using the former, the intermediate data correlated with the plaintexts/keys are masked by random numbers, thus no direct correlation exists between the power consumption and the plaintexts/keys and the first order SCA can be counteracted. By using the latter, additional noise is added to the side channel information by randomizing the operation sequence of the algorithm. Combining these two methods, the higher order SCA can be counteracted. To evaluate the security of the proposed system, the correlation power analysis attacks are carried out based on the target device of an Atmel XMEGA microcontroller. For the proposed system, the correlation coefficient calculated from the correct key is not larger than the incorrect keys. However, for the unprotected cipher system, the correlation coefficient calculated from the correct key is 0.8 and the coefficients calculated from the incorrect keys are less than 0.5, i.e. the system can be attacked. Experimental results demonstrate that the proposed cryptosystems can counteract the correlation power analysis attacks and maintain the security performance for the chaotic cryptographic systems. [ABSTRACT FROM AUTHOR]

Background: Chimeric antigen receptor T (CAR T) cells immunotherapy is rapidly developed in treating cancers, especially relapsed or refractory B-cell malignancies.Methods: To assess the efficacy and safety of CAR T therapy, we analyzed clinical trials from PUBMED and EMBASE.Results: Results showed that the pooled response rate, 6-months and 1-year progression-free survival (PFS) rate were 67%, 65.62% and 44.18%, respectively. We observed that received lymphodepletion (72% vs 44%, P = 0.0405) and high peak serum IL-2 level (85% vs 31%, P = 0.04) were positively associated with patients' response to CAR T cells. Similarly, costimulatory domains (CD28 vs CD137) in second generation CAR T was positively associated with PFS (52.69% vs 33.39%, P = 0.0489). The pooled risks of all grade adverse effects (AEs) and grade ≥ 3 AEs were 71% and 43%. Most common grade ≥ 3 AEs were fatigue (18%), night sweats (14%), hypotension (12%), injection site reaction (12%), leukopenia (10%), anemia (9%).Conclusions: In conclusion, CAR T therapy has promising outcomes with tolerable AEs in relapsed or refractory B-cell malignancies. Further modifications of CAR structure and optimal therapy strategy in continued clinical trials are needed to obtain significant improvements. [ABSTRACT FROM AUTHOR]

Chaos has been used in cryptography for years and many chaotic cryptographic systems have been proposed. Their securities are often evaluated by conducting conventional statistical tests, however few studies have referred to the security issue of the chaotic hardware cryptographic systems. This paper evaluates the security of the chaotic cryptographic system from a hardware perspective by using the side channel analysis attack. First, a chaotic block cryptosystem is designed and implemented based on an Atmel microcontroller. Then the conventional statistical security tests, including SP 800-22 test, characters frequency test, avalanche test, are used to verify its security performance. In the meantime, the correlation power analysis attack is carried out for the security evaluation. Experimental results demonstrate that even though the chaotic cryptographic system can pass the conventional statistical tests, it still has the probability to be attacked from a hardware perspective using the leaked side channel information such as execution time and power consumption. This paper proposes another way to analyze the security of the chaotic cryptosystem, which can aid designing mechanisms to enhance the security of the hardware cryptosystems in the future. [ABSTRACT FROM AUTHOR]

The chaotic map has complex dynamics under ideal conditions however it suffers from the problem of performance degradation in the case of finite computing precision. In order to prevent the dynamics degradation, in this paper the continuous Chen chaotic system is used to perturb both the inputs and parameters of Chebyshev map to minimize the chaotic degradation phenomenon under finite precision. Experimental evaluations and corresponding performance analysis demonstrate that the Chebyshev chaotic map has a good randomness and complex dynamic performance by using the proposed perturbation method, and some attributes of the proposed system are stronger than the original system (e.g. chaos attractor and approximate entropy). Finally, the corresponding pseudorandom number generator (PRNG) is constructed by this method and then its randomness is evaluated via NIST SP800-22 and TestU01 test suites, respectively. Statistical test results show that the proposed PRNG has high reliability of randomness, thus it can be used for cryptography and other potential applications. [ABSTRACT FROM AUTHOR]

• A dual-color watermarking based on inter-block matrix decomposition is proposed. • A novel diagonal relationship is developed to embed the watermark. • The amount of data for color watermark is reduced by combining compressive sensing. • Matrix decomposition is used for the first time between surrounding inter-block. • Results show a large embedding capacity, excellent imperceptibility and robustness. Compared with binary and grayscale watermark, the color watermark has a larger amount of information, which makes color blind watermarking algorithms more challenging in terms of robustness, watermark capacity and computational complexity under the limitation of distortion. To better meet these challenges, a robust dual-COlor image watermarking scheme exploiting COmpressive seNsing and inter-bloCk approximatE mAximum eigenvaLue, namely CONCEAL, is proposed in this work. Specifically, the Compressive Sensing (CS) is first executed on a watermark, which can effectively compress the watermark information to one-half of the original. Secondly, the carrier image is separated into 4 × 4 non-overlapping pixel blocks, and the standard deviation of each pixel block is calculated. Then, the pixel blocks with smaller standard deviations are subdivided into four 2 × 2 non-overlapping pixel sub-blocks (the upper-left, lower-left, upper-right, and lower-right pixel sub-blocks), and the Approximate Maximum Eigenvalue (AME) of four sub-pixel blocks are directly calculated in the spatial-domain at the same time. Finally, every 3-bit watermark data is embedded in a 4 × 4 pixel block using the AME of the upper-left pixel sub-block and the other three pixel sub-blocks. The imperceptibility and robustness of CONCEAL are assessed experimentally. The experimental results show that all Peak Signal-to-Noise Ratios (PSNRs) are above 47 dB, Structural Similarity Index Measures (SSIMs) are above 0.98, and Normalized Correlations (NCs) are above 0.9. Compared with the state-of-the-art schemes, the proposed CONCEAL possesses large capacity, high imperceptibility, comparable robustness. [ABSTRACT FROM AUTHOR]

Identifies a 100 kd glycoprotein in the chick brain that may be a repulsive guidance cue. Cues as a cause of collapse of neuronal growth cones; Effective concentration; Structure of C-terminal and N-terminal ends; Effect of recombinant version of substance; Possible action of substance.

• A novel watermarking scheme based on IWT and SVD is proposed. • An optimized authentication mechanism is developed to address the FPP. • Three-dimension optimal mapping is proposed to obtain optimal scaling factors. • The proposed algorithm can be applied to watermarks with different sizes. • Results demonstrate a good performance in both invisibility and robustness. Image watermarking technique is one of effective solutions to protect copyright, and it is applied to a variety of information security application domains. It needs to meet four requirements of imperceptibility, robustness, capacity and security. A multi-scale and secure image watermarking method is proposed in this work, which is based on the Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD). Four IWT sub-bands are firstly obtained after 1-level IWT on the host image, and the corresponding singular diagonal matrices of four sub-bands can be obtained using SVD. Then, each singular diagonal matrix is divided into four non-overlapping sections in terms of the size of embedding watermark. Particularly, the size of upper left part is same as the size of watermark. The watermark can be directly embedded into four upper left parts afterwards by multiplying different scaling factors to complete the final watermarking operation. Especially, a novel optimized authentication mechanism is designed to resolve the false positive problem, which exists in the SVD-based watermarking algorithms. In addition, three-dimensional optimal mapping algorithm is proposed to search the optimal scaling factors through a novel objective evaluation function, and it can significantly improve the imperceptibility and robustness. The experimental test and comparison analysis illustrate that the proposed watermark scheme demonstrates a high imperceptibility with peak signal to noise ratio values of 45 dB and strong robustness with average normalized correlation values of 0.92. [ABSTRACT FROM AUTHOR]

Intelligent traffic light control is one of the modern approaches to solve traffic congestion, where reinforcement learning is a widely used method. Conventionally, reinforcement learning is used to determine whether to change the current phase (or choose a traffic phase) after each small interval. One major drawback of these approaches is that it makes the current traffic light phase duration uncertain before the current phase terminates. Directly determining the duration of the traffic light phase can effectively avoid this shortcoming. An adaptive traffic light timing system is proposed in this paper which can directly control the phase duration. In the proposed system, the Q-learning algorithm is employed and the action space is defined as all possible phase durations. In addition, the reward function is redefined to guide the agent to balance more traffic metrics, and the state is redefined to reduce the state space. Finally, the proposed system is evaluated by equal, unequal, and complex traffic scenarios. Results show that the proposed system has a better performance compared with other methods in controlling traffic lights, even on complex traffic scenarios. [ABSTRACT FROM AUTHOR]

• A novel image encryption scheme is proposed based on hyper-chaotic system and quantum coding. • A novel enhanced quantum representation (NEQR) model is applied to image processing. • The bit-level adjacent-exchange operation is designed based on NEQR model. • A novel image scrambling method based on quantum location coding is proposed. • Experimental results demonstrate a good security performance. The application of quantum mechanisms in image processing has attracted wide attention. In this paper, an image encryption scheme that is based on hyper-chaos and quantum coding is proposed. First, based on a novel enhanced quantum representation (NEQR), the bit-level adjacent-exchange operation is implemented to pre-process the plain-image, which has lower computational complexity compared to conventional bit-level switching operations. Then, the processed image is diffused by the quantified sequences where the self-adapting parameters are applied to quantify the sequences that are generated by Chen's hyper-chaotic system. In addition, inspired by the quantum coding structure, a novel scrambling method that is based on quantum location coding is proposed. This method is used to further scramble the diffused image to obtain the final cipher-image. The experimental results and performance analysis demonstrate that the proposed image encryption scheme has high security performance. [ABSTRACT FROM AUTHOR]

Recent studies have shown that biologically inspired Spiking Neural Networks (SNNs) has potentials for the mobile robot controls. Based on SNNs, an autonomous learning paradigm for controlling mobile robots is proposed in this work, which can learn specific tasks autonomously. A reward modulated spike-timing-dependent plasticity (R-STDP) learning algorithm is designed to aid implementing the autonomous learning paradigm. It can train the SNN under different environmental states and conditions. The obstacle avoidance in the synthetic and real environments is used as a robotic task example to verify the effectiveness of the proposed paradigm. Results show that the mobile robot can learn autonomously under different environmental conditions and is able to avoid obstacles after learning processes complete. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) have the potential to closely mimic the information processing of biological brains, by using massive neurons that are interconnected in a complex network. Recent researches have considered using electronic hardware circuits to SNN implementations to meet real-time processing requirements. Network-on-Chips (NoCs) have been widely used to develop such SNN circuits as their interconnections can offer stable interconnectivity for neuron communications with high throughput and real-time execution. However, its scalability is limited due to expensive and complex NoC routers which leads to high energy consumption and large area utilization. Therefore, a minimally buffered deflection router (MBDR) is proposed in this work to address the scalability challenge of the hardware SNNs. It employs a deflection router technique to remove most of the inter-buffers and other expensive components of the conventional routers. Moreover, a novel flow controller is developed in MBDR to further reduce power consumption. Compared to existing approaches, experimental results show that based on 90-nm CMOS technology the area and power consumption of the proposed router are reduced by ~ 86% and ~ 88%, respectively. In the meantime, system throughput is maintained at a high level. [ABSTRACT FROM AUTHOR]

Cellulose nanofiber (CNF) is an environmentally friendly material with a high specific surface area, which is an ideal candidate for cathode catalysts in zinc–air batteries (ZABs). However, untreated CNF-based carbon materials suffer from low intrinsic activity and fewer micro-mesopores. Herein, we developed an N-doped porous carbon aerogel from CNF and graphite phase carbon nitride (g-C3N4) via hydrothermal pretreatment and the carbonization process. The CNF can serve as the skeleton of the aerogel that leads to an interconnected porous structure, while g-C3N4 guarantees the in situ N doping and the formation of more nanopores. Therefore, the as-prepared carbon aerogel exhibits excellent oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) performances owing to the successful N doping with sufficient graphite-N and pyridine-N active sites (69.1%) and high specific surface areas (785.4 m2 g−1). The assembled ZABs with a carbon aerogel deliver a specific capacity of 790 mA h g−1 and an energy density of 945.6 W h kg−1 with a long-term stability over 100 h. This work opens a new avenue for the effective conversion of abundant biomass into bifunctional air cathodes for energy-related applications. [ABSTRACT FROM AUTHOR]

The echo state network (ESN) is a powerful recurrent neural network for time series modelling. ESN inherits the simplified structure and relatively straightforward training process of conventional neural networks, and shows strong computational capabilities to solve nonlinear problems. It is able to map low-dimensional input signals to high-dimensional space for information extraction, but it is found that not every dimension of the reservoir output directly contributes to the model generalization. This work aims to improve the generalization capabilities of the ESN model by reducing the redundant reservoir output features. A novel hybrid model, namely binary grey wolf echo state network (BGWO-ESN), is proposed which optimises the ESN output connection by the feature selection scheme. Specially, the feature selection scheme of BGWO is developed to improve the ESN output connection structure. The proposed method is evaluated using synthetic and financial data sets. Experimental results demonstrate that the proposed BGWO-ESN model is more effective than other benchmarks, and obtains the lowest generalization error. [ABSTRACT FROM AUTHOR]

Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor's 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance. [ABSTRACT FROM AUTHOR]

Abstract Recent studies have shown that the electronic hardware devices can be compromised by the faults and fault tolerance is a crucial capability. This paper addresses the challenge of fault detection in the CMOS circuits, using two bio-inspired structures based on the HP lab's memristor and the BSIM3v3.2.2 transistor models. The first fault detection circuit (FDC) includes the memristor-based synapses and a modified leaky integrate-and-fire (LIF)-based neuron. The memristor-based synapse circuits can be further optimized which is the proposed second fault detection method (O-FDC), and it has a lower hardware overhead and power consumption compared to the former. Experimental results demonstrate that the proposed structures can detect the circuit faults under the inputs of direct current (DC), alternating current (AC) voltage sources, and pulse signals. Under the input of DC, the fault detection times for the two proposed structures are about 0.16 ms and 1.2 ms, respectively; when the input source is AC, the corresponding fault detection times are about 0.206 ms and 0.758 ms; and it takes only 6.47us for fault detection under the input of pulse signals. This work provides an alternative solution to enhance the fault-tolerant capability of the hardware systems. [ABSTRACT FROM AUTHOR]

Pathophysiological and atrophic changes in the cerebellum have been well‐documented in schizophrenia. Reduction of gray matter (GM) in the cerebellum was confirmed across cognitive and motor cerebellar modules in schizophrenia. Such abnormalities in the cerebellum could potentially have widespread effects on both sensorimotor and cognitive symptoms. In this study, we investigated how reduction change in the cerebellum affects the static and the dynamic functional connectivity (FC) between the cerebellum and cortical/subcortical networks in schizophrenia. Reduction of GM in the cerebellum was confirmed across the cognitive and motor cerebellar modules in schizophrenic subjects. Results from this study demonstrates that the extent of reduction of GM within cerebellum correlated with increased static FCs between the cerebellum and the cortical/subcortical networks, including frontoparietal network (FPN), and thalamus in patients with schizophrenia. Decreased GM in the cerebellum was also associated with a declined dynamic FC between the cerebellum and the FPN in schizophrenic subjects. The severity of patients' positive symptom was related to these structural‐functional coupling score of cerebellum. These findings identified potential cerebellar driven functional changes associated with positive symptom deficits. A post hoc analysis exploring the effect of changed FC within cerebellum, confirmed that a significant positive relationship, between dynamic FCs of cerebellum–thalamus and intracerebellum existed in patients, but not in controls. The reduction of GM within the cerebellum might be associated with modulation of cerebellum–thalamus, and contributes to the dysfunctional cerebellar‐cortical communication in schizophrenia. Our results provide a new insight into the role of cerebellum in understanding the pathophysiological of schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Sepsis is an important public health issue, and it is urgent to develop valuable indicators to predict the prognosis of sepsis. Our study aims to assess the predictive value of ICU admission (Neutrophil + Monocyte)/lymphocyte ratio (NMLR) on the 30-day mortality of sepsis patients. Methods: A retrospective analysis was conducted in septic patients, and the data were collected from Medical Information Mart for Intensive Care IV (MIMIC-IV). Univariate and multivariate Cox regression analyses were conducted to investigate the relation between ICU admission NMLR and 30-day mortality. Restricted cubic spline (RCS) was performed to determine the optimum cut-off value of ICU admission NMLR. Survival outcomes of the two groups with different ICU admission NMLR levels were estimated using the Kaplan-Meier method and compared by the log-rank test. Results: Finally, 7292 patients were recruited in the study, of which 1601 died within 30 days of discharge. The non-survival group had higher ICU admission NMLR values than patients in the survival group (12.24 [6.44–23.67] vs. 8.71 [4.81–16.26], P < 0.001). Univariate and multivariate Cox regression analysis demonstrated that ICU admission NMLR was an independent prognostic predictor on 30-day mortality (Univariate: P < 0.001; multivariate: P = 0.011). The RCS model demonstrated the upturn and non-linear relationship between ICU admission NMLR and 30-day mortality (Nonlinearity: P = 0.0124). According to the KM curve analysis,30-day survival was worse in the higher ICU admission NMLR group than that in the lower ICU admission NMLR group (Log rank test, P < 0.0001). Conclusion: The elevated ICU admission NMLR level is an independent risk factor for high 30-day mortality in patients with sepsis. [ABSTRACT FROM AUTHOR]

Here we develop a direct and efficient protocol for 3-halooxidation of indoles using DMSO as the oxygen source and NXS (X = Br, Cl) the halo source. This protocol features broad substrate scope and good to excellent yields, thus rendering it as a highly versatile, straightforward, and atom-economical alternative prior to the previously reported methods, which possess their capability to undergo the S N 1 substitution of a highly reactive electrophilic o -azaxylylene intermediate to generate 3, 3-disubstituted oxindoles. [Display omitted] [ABSTRACT FROM AUTHOR]

Components of metabolic syndrome might be predictors of the therapeutic outcome of psychiatric symptom in schizophrenia, whereas clinical results are inconsistent and an intrinsic therapeutic link between weaker psychiatric symptoms and emergent metabolic syndrome remains unclear. This study aims to reveal the relationship and illustrate potential mechanism by exploring the alteration of cerebellar functional connectivity (FC) in schizophrenia patients with comorbidity metabolic syndrome. Thirty-six schizophrenia patients with comorbidity of metabolic syndrome (SCZ-MetS), 45 schizophrenia patients without metabolic syndrome (SCZ-nMetS) and 39 healthy controls (HC) were recruited in this study. We constructed FC map of cerebello-cortical circuit and used moderation effect analysis to reveal complicated relationship among FC, psychiatric symptom and metabolic disturbance. Components of metabolic syndrome were significantly correlated with positive symptom score and negative symptom score. Importantly, the dysconnectivity between cognitive module of cerebellum and left middle frontal gyrus in SCZ-nMetS was recuperative increased in SCZ-MetS, and was significantly correlated with general symptom score. Finally, we observed significant moderation effect of body mass index on this correlation. The present findings further supported the potential relationship between emergence of metabolic syndrome and weaker psychiatric symptom, and provided neuroimaging evidence. The mechanism of intrinsic therapeutic link involved functional change of cerebello-cortical circuit. [ABSTRACT FROM AUTHOR]

Small cell lung cancer (SCLC) is a highly aggressive cancer of the neuroendocrine system, characterized by poor differentiation, rapid growth, and poor overall survival (OS) of patients. Despite the recent advances in the treatment of SCLC recently, the 2-year survival rate of patients with the cancer is only 14–15%, occasioned by the acquired resistance to drugs and serious off-target effects. In humans, the coding region is only 2% of the total genome, and 20% of that is associated with human diseases. Beyond the coding genome are RNAs, promoters, enhancers, and other intricate elements. The non-coding regulatory regions, mainly the non-coding RNAs (ncRNAs), regulate numerous biological activities including cell proliferation, metastasis, and drug resistance. As such, they are potential diagnostic or prognostic biomarkers, and also potential therapeutic targets for SCLC. Therefore, understanding how non-coding elements regulate SCLC development and progression holds significant clinical implications. Herein, we summarized the recent discoveries on the relationship between the non-coding elements including long non-coding RNAs (lncRNA), microRNAs (miRNAs), circular RNA (circRNA), enhancers as well as promotors, and the pathogenesis of SCLC and their potential clinical applications. [ABSTRACT FROM AUTHOR]

Small cell lung cancer (SCLC) is a type of neuroendocrine tumor with high malignancy and poor prognosis. Besides the de novo SCLC, there is transformed SCLC, which has similar characteristics of pathological morphology, molecular characteristics, clinical manifestations and drug sensitivity. However, de novo SCLC and transformed SCLC have different pathogenesis and tumor microenvironment. SCLC transformation is one of the mechanisms of resistance to chemotherapy, immunotherapy, and targeted therapy in NSCLC. Two hypotheses have been used to explain the pathogenesis of SCLC transformation. Although SCLC transformation is not common in clinical practice, it has been repeatedly identified in many small patient series and case reports. It usually occurs in epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma after treatment with tyrosine kinase inhibitors (TKIs). SCLC transformation can also occur in anaplastic lymphoma kinase (ALK)-positive lung cancer after treatment with ALK inhibitors and in wild-type EGFR or ALK NSCLC treated with immunotherapy. Chemotherapy was previously used to treat transformed SCLC, yet it is associated with an unsatisfactory prognosis. We comprehensively review the advancements in transformed SCLC, including clinical and pathological characteristics, and the potential effective treatment after SCLC transformation, aiming to give a better understanding of transformed SCLC and provide support for clinical uses. [ABSTRACT FROM AUTHOR]

Small cell lung cancer (SCLC) is a type of neuroendocrine tumor with high malignancy and poor prognosis. Besides the de novo SCLC, there is transformed SCLC, which has similar characteristics of pathological morphology, molecular characteristics, clinical manifestations and drug sensitivity. However, de novo SCLC and transformed SCLC have different pathogenesis and tumor microenvironment. SCLC transformation is one of the mechanisms of resistance to chemotherapy, immunotherapy, and targeted therapy in NSCLC. Two hypotheses have been used to explain the pathogenesis of SCLC transformation. Although SCLC transformation is not common in clinical practice, it has been repeatedly identified in many small patient series and case reports. It usually occurs in epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma after treatment with tyrosine kinase inhibitors (TKIs). SCLC transformation can also occur in anaplastic lymphoma kinase (ALK)-positive lung cancer after treatment with ALK inhibitors and in wild-type EGFR or ALK NSCLC treated with immunotherapy. Chemotherapy was previously used to treat transformed SCLC, yet it is associated with an unsatisfactory prognosis. We comprehensively review the advancements in transformed SCLC, including clinical and pathological characteristics, and the potential effective treatment after SCLC transformation, aiming to give a better understanding of transformed SCLC and provide support for clinical uses. [ABSTRACT FROM AUTHOR]

Objective: When facing major emergency public accidents, men and women may react differently. Our research aimed to assess the influence of gender difference on social support, information preference, biological rhythm, psychological distress, and the possible interaction among these factors during the COVID-19 pandemic. Methods: In this cross-sectional study, 3,237 respondents aged 12 years and older finished the online survey. Levels of social support, information preference, biological rhythm, and psychological distress were assessed using validated scales. A path analysis was conducted to explore possible associations among these variables. Results: The path analysis indicated that women with high levels of social support had a lower possibility of biological rhythm disorders and lower levels of somatization symptoms of psychological distress during the COVID-19 pandemic. The influence of social support on somatization symptoms was exerted via biological rhythm. Women tended to believe both negative and positive information, while men preferred more extreme information. Conclusion: Our results highlighted gender difference in study variables during the COVID-19 pandemic and the importance of social support in alleviating psychological distress and biological rhythm disorders. Moreover, we confirmed that information preference differed significantly by somatization symptoms of psychological distress, suggesting extra efforts to provide more individualized epidemic information. Longitudinal research is required to further explore casual inferences. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks (SNNs) exhibit the strong capability to address spatiotemporal dynamic problems. Recent research has explored the hardware SNN systems to solve the spatiotemporal problems in real-time. The Network-on-Chip (NoC) is an effective scheme for building large-scale hardware SNNs. However, for the existing NoC-based hardware SNNs, large area overhead and hardware power are consumed by their interconnections, because of complex topologies and router structures. Therefore, in this work a novel Unidirectional and Hierarchical on-Chip Interconnected Architecture (UHCIA) is proposed to address this problem. The proposed UHCIA mainly combines the novel hybrid topology of unidirectional multiple loops and rings, and uses a deflection router technique. Experimental results show that compared to other works, the UHCIA achieves ∼ 23.6X of area reduction and ∼ 6.4X of power reduction, with high system throughput and biological real-time computations. • The UHCIA significantly simplifies SNN interconnections, enabling a more efficient design. • The architecture achieves a 23.6X area and 6.4X power reduction. • Despite its compact size, the UHCIA maintains high throughput and real-time capability. [ABSTRACT FROM AUTHOR]

• The delayed onset action of antidepressants poses a significant challenge. • TDNs@FLX complex exert early and sustained therapeutic effects. • Early therapeutic efficacy of TDNs@FLX is linked to neurogenesis and neuroplasticity. The delayed-onset effects of antidepressants may increase the risk of suicide in patients before they take full effect. Accumulating evidence indicates that the restriction of the blood–brain barrier (BBB), the inhibition of hippocampal neurogenesis, and the decrease in neuroplasticity play vital roles in this delayed response. This study utilizes tetrahedral DNA nanostructures (TDNs) as a drug delivery system, inherently facilitating the proliferation of neural precursor cells and effectively penetrating the BBB. The nanoscale complex, TDNs@FLX, is constructed by combining TDNs with the classic antidepressant fluoxetine (FLX). TDNs@FLX demonstrates the ability to alleviate depression symptoms in a mouse model during the early stages of treatment. In contrast, pure TDNs alone fail to provide long-lasting therapeutic benefits, and free FLX results in delayed therapeutic outcomes. The early efficacy of the TDNs@FLX complex in treating depression is closely related to the promotion of neural precursor cell proliferation, dendritic complexity, and spine density. Thus, this study addresses the limitations of classic antidepressants and presents exciting prospects for the application of DNA-based nanomedicine in the treatment of depressive disorders. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has negatively impacted the income of many bank users. Many users without emergency funds had difficulty coping with this unexpected event and had to use credit or apply to the government for bailout funds. Therefore, it is necessary to develop spending plans and deposit plans based on transaction data of users to assist them in saving sufficient emergency funds to cope with unexpected events. In this paper, an emergency fund model is proposed, and two optimization algorithms are applied to solve the optimal solution of the model. Secondly, an early warning mechanism is proposed, i.e. an unexpected prevention index and a consumption index are proposed to measure the ability of users to cope with unexpected events and the reasonableness of their expenditure respectively, which provides early warning to users. Finally, the model is experimented with real bank users and the performance of the model is analysed. The experiments show that compared to the no-planning scenario, the model helps users to save more emergency funds to cope with unexpected events, furthermore, the proposed model is real-time and sensitive. [ABSTRACT FROM AUTHOR]

Currently, effective therapies for advanced gastric cancer with systemic metastasis are lacking. Pharmacological research has been slowly progressing over the past decades. Here, we report the case of a 56-year-old female with human epidermal growth factor receptor 2 (HER2) expression (IHC 2+/FISH-) in gastric cancer with systemic metastasis. The first-line therapeutic regime consisted of systemic administration of camrelizumab, local arterial infusion of oxaliplatin and arterial embolization, oral apatinib, and PS scheme (oral tegafur-gimeracil-oteracil (S-1) and paclitaxel (PTX), which was administered both intraperitoneally and systemically). After the treatment, a 3-month progression-free survival (PFS) was observed. Due to the occurrence of CTCAE grade 4 adverse reactions, the patient could not tolerate chemotherapy. In the second line of treatment, we replaced the PS scheme with disitamab vedotin and continued the use of carrilizumab and apatinib. After four cycles, efficacy evaluation showed that it was stable disease (SD), only CTCAE 1/2 grade adverse reactions occurred, and endoscopy examination showed local tumor control with a reduction in the ulcer lesion. At the time of submission of the current manuscript, a 6-month PFS was achieved and the treatment was continued. Due to the safety and efficacy of disitamab vedotin observed in our case, we propose that disitamab vedotin could be a promising drug for the treatment of advanced gastric cancer patients with HER2 expression. [ABSTRACT FROM AUTHOR]

Three monoclonal antibodies (MAbs) against channel catfish virus (CCV) were generated from mice immunized with purified CCV. Western blot analysis revealed that the MAb 3G12 reacted with three CCV proteins of 94 kDa, 130 kDa, and 170 kDa; the MAb 4C4 reacted with two CCV proteins of 130 kDa and 170 kDa; and the MAb 4D4 reacted with two CCV proteins of 94 kDa and 98 kDa. Indirect immunofluorescence assay showed intense fluorescence in the CCV-infected channel catfish ovary (CCO) cells in areas corresponding to the location of granular structures. In addition, the three MAbs could completely neutralize CCV at a dilution of 1:500. This study demonstrated that these MAbs could recognize CCV specifically and will be useful in the development of diagnostic methods for the detection of fish CCV infection. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks (SNNs) are the new generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. In this work, a multi-task autonomous learning paradigm is proposed for the mobile robot application, which employs a SNN to construct the controlling system of the mobile robot. The Reward-modulated Spiking-time-dependent Plasticity learning rule is developed for the SNN-based controller, which aims to achieve the capability of autonomous learning under multiple tasks. Reward signals are generated based on the instantaneous frequencies of pre- and post-synaptic spikes, which adapts to the sensory stimuli and environmental feedback. Meanwhile, inspired by lateral inhibition connections, a task switch mechanism is designed to enable the controller to switch the operations between multiple tasks. Two tasks of obstacle avoidance and target tracking are used for performance evaluation and results demonstrate that the mobile robot with the proposed paradigm is able to autonomously learn, switch and complete the tasks. [ABSTRACT FROM AUTHOR]

This study used histopathological image features to predict molecular features, and combined with multi-dimensional omics data to predict overall survival (OS) in high-grade serous ovarian cancer (HGSOC). Patients from The Cancer Genome Atlas (TCGA) were distributed into training set (n = 115) and test set (n = 114). In addition, we collected tissue microarrays of 92 patients as an external validation set. Quantitative features were extracted from histopathological images using CellProfiler, and utilized to establish prediction models by machine learning methods in training set. The prediction performance was assessed in test set and validation set. The prediction models were able to identify BRCA1 mutation (AUC = 0.952), BRCA2 mutation (AUC = 0.912), microsatellite instability-high (AUC = 0.919), microsatellite stable (AUC = 0.924), and molecular subtypes: proliferative (AUC = 0.961), differentiated (AUC = 0.952), immunoreactive (AUC = 0.941), mesenchymal (AUC = 0.918) in test set. The prognostic model based on histopathological image features could predict OS in test set (5-year AUC = 0.825) and validation set (5-year AUC = 0.703). We next explored the integrative prognostic models of image features, genomics, transcriptomics and proteomics. In test set, the models combining two omics had higher prediction accuracy, such as image features and genomics (5-year AUC = 0.834). The multi-omics model including all features showed the best prediction performance (5-year AUC = 0.911). According to risk score of multi-omics model, the high-risk and low-risk groups had significant survival differences (HR = 18.23, p < 0.001). These results indicated the potential ability of histopathological image features to predict above molecular features and survival risk of HGSOC patients. The integration of image features and multi-omics data may improve prognosis prediction in HGSOC patients. • Computer-aided image analysis system can provide quantitative assessment of histopathological images. • Histopathological image features help to predict mutations, microsatellite instability, molecular subtypes and prognosis. • Integrative model of histopathological images and omics data may improve prognosis prediction and risk stratification. [ABSTRACT FROM AUTHOR]

• A novel method is proposed to identify Alzheimer's disease. • An optimized convolutional neural network is developed. • Neural network parameters are reduced for computing efficiency. • Experimental results show good performance on accuracy. To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However, a few limitations of these algorithms, such as the requirement for large number of training images and the necessity for powerful computers, still hinder the extensive usage of AD diagnosis based on machine learning. In addition, large number of training parameters and heavy computation make the DL systems difficult in integrating with mobile embedded devices, for example the mobile phones. For AD detection using DL, most of the current research solely focused on improving the classification performance, while few studies have been done to obtain a more compact model with less complexity and relatively high recognition accuracy. In order to solve this problem and improve the efficiency of the DL algorithm, a deep separable convolutional neural network model is proposed for AD classification in this paper. The depthwise separable convolution (DSC) is used in this work to replace the conventional convolution. Compared to the traditional neural networks, the parameters and computing cost of the proposed neural network are found greatly reduced. The parameters and computational costs of the proposed neural network are found to be significantly reduced compared with conventional neural networks. With its low power consumption, the proposed model is particularly suitable for embedding mobile devices. Experimental findings show that the DSC algorithm, based on the OASIS magnetic resonance imaging dataset, is very successful for AD detection. Moreover, transfer learning is employed in this work to improve model performance. Two trained models with complex networks, namely AlexNet and GoogLeNet, are used for transfer learning, with average classification rates of 91.40%, 93.02% and a less power consumption. [ABSTRACT FROM AUTHOR]

Background: Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC). Methods: We first used histopathological image features and machine‐learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA‐HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis. Results: Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5‐year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5‐year AUC = 0.860) and test set (5‐year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high‐risk and low‐risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation. Conclusions: Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC. [ABSTRACT FROM AUTHOR]

A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations. The sliding threshold mechanism of the Bienenstock-Cooper-Munro rule (BCM) is used to redefine the memristance (i.e. synaptic weight) adjustment process of the memristive synapse model. Based on the proposed memristor-based synapse and Leaky Integrate-and-Fire neurons, a spiking neural network (SNN) hardware fragment is constructed, where spike trains with different frequencies are used to evaluate the stability performance of the proposed SNN hardware. Results show that compared to other approaches, the network is stable under different stimuli due to the characteristics of the memristor-based synapse model, and prove that the proposed synapse model is able to mimic biological synaptic behaviour and the problem of synaptic weight infinite modulations is addressed. [ABSTRACT FROM AUTHOR]

Highlights • We developed a novel structural neural network (sNN) model for forecasting tourism demand. • The sNN captures the trend and seasonal patterns of tourism demand accurately. • Empirical results show a significantly superior performance of sNN to benchmark models. Abstract Tourist arrival and tourist demand forecasting are a crucial issue in tourism economy and the community economic development as well. Tourist demand forecasting has attracted much attention from tourism academics as well as industries. In recent year, it attracts increasing attention in the computational literature as advances in machine learning method allow us to construct models that significantly improve the precision of tourism prediction. In this paper, we draw upon both strands of the literature and propose a novel paired neural network model. The tourist arrival data is decomposed by two low-pass filters into long-term trend and short-term seasonal components, which are then modelled by a pair of autoregressive neural network models as a parallel structure. The proposed model is evaluated by the tourist arrival data to United States from twelve source markets. The empirical studies show that our proposed paired neural network model outperforming the selected benchmark model across all error measures and over different horizons. [ABSTRACT FROM AUTHOR]